From aefcbe047ec60cebea764a072dad6037ee329e12 Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Wed, 3 Jan 2024 09:21:21 +0100 Subject: [PATCH 01/36] started ZI inclusion --- DESCRIPTION | 7 +- NAMESPACE | 9 ++ R/PLNfit-class.R | 6 - R/RcppExports.R | 36 +++++ R/ZIPLN.R | 96 ++++++++++++ R/ZIPLNfit-class.R | 335 ++++++++++++++++++++++++++++++++++++++++ R/ZIPLNfits-S3methods.R | 86 +++++++++++ R/optim-zipln.R | 217 ++++++++++++++++++++++++++ R/scRNA.R | 14 ++ R/utils.R | 47 ++++++ data-raw/scRNA.R | 25 +++ data/scRNA.rda | Bin 0 -> 1056895 bytes man/ZIPLN.Rd | 56 +++++++ man/ZIPLNfit.Rd | 244 +++++++++++++++++++++++++++++ man/coef.ZIPLNfit.Rd | 32 ++++ man/fitted.ZIPLNfit.Rd | 19 +++ man/scRNA.Rd | 26 ++++ man/sigma.ZIPLNfit.Rd | 19 +++ src/RcppExports.cpp | 148 ++++++++++++++++++ src/optim_zi-pln.cpp | 264 +++++++++++++++++++++++++++++++ src/utils.h | 8 + 21 files changed, 1687 insertions(+), 7 deletions(-) create mode 100644 R/ZIPLN.R create mode 100644 R/ZIPLNfit-class.R create mode 100644 R/ZIPLNfits-S3methods.R create mode 100644 R/optim-zipln.R create mode 100644 R/scRNA.R create mode 100644 data-raw/scRNA.R create mode 100644 data/scRNA.rda create mode 100644 man/ZIPLN.Rd create mode 100644 man/ZIPLNfit.Rd create mode 100644 man/coef.ZIPLNfit.Rd create mode 100644 man/fitted.ZIPLNfit.Rd create mode 100644 man/scRNA.Rd create mode 100644 man/sigma.ZIPLNfit.Rd create mode 100644 src/optim_zi-pln.cpp diff --git a/DESCRIPTION b/DESCRIPTION index 2eef96b7..09e3b79a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: PLNmodels Title: Poisson Lognormal Models -Version: 1.1.0 +Version: 1.1.0-9000 Authors@R: c( person("Julien", "Chiquet", role = c("aut", "cre"), email = "julien.chiquet@inrae.fr", comment = c(ORCID = "0000-0002-3629-3429")), @@ -86,11 +86,16 @@ Collate: 'PLNnetworkfit-S3methods.R' 'PLNnetworkfit-class.R' 'RcppExports.R' + 'ZIPLNfit-class.R' + 'ZIPLN.R' + 'ZIPLNfits-S3methods.R' 'barents.R' 'import_utils.R' 'mollusk.R' 'oaks.R' + 'optim-zipln.R' 'plot_utils.R' + 'scRNA.R' 'trichoptera.R' 'utils-pipe.R' 'utils.R' diff --git a/NAMESPACE b/NAMESPACE index b21b7021..90c918dc 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -3,8 +3,10 @@ S3method(coef,PLNLDAfit) S3method(coef,PLNfit) S3method(coef,PLNmixturefit) +S3method(coef,ZIPLNfit) S3method(fitted,PLNfit) S3method(fitted,PLNmixturefit) +S3method(fitted,ZIPLNfit) S3method(getBestModel,PLNPCAfamily) S3method(getBestModel,PLNmixturefamily) S3method(getBestModel,PLNnetworkfamily) @@ -25,6 +27,7 @@ S3method(predict,PLNmixturefit) S3method(predict_cond,PLNfit) S3method(sigma,PLNfit) S3method(sigma,PLNmixturefit) +S3method(sigma,ZIPLNfit) S3method(standard_error,PLNPCAfit) S3method(standard_error,PLNfit) S3method(standard_error,PLNfit_fixedcov) @@ -42,6 +45,7 @@ export(PLNmixture) export(PLNmixture_param) export(PLNnetwork) export(PLNnetwork_param) +export(ZIPLN) export(coefficient_path) export(compute_PLN_starting_point) export(compute_offset) @@ -71,6 +75,7 @@ importFrom(corrplot,corrplot) importFrom(future.apply,future_lapply) importFrom(future.apply,future_sapply) importFrom(glassoFast,glassoFast) +importFrom(glmnet,glmnet) importFrom(grid,nullGrob) importFrom(grid,textGrob) importFrom(gridExtra,arrangeGrob) @@ -83,6 +88,7 @@ importFrom(igraph,graph_from_adjacency_matrix) importFrom(igraph,layout_in_circle) importFrom(igraph,plot.igraph) importFrom(magrittr,"%>%") +importFrom(pscl,zeroinfl) importFrom(purrr,map) importFrom(purrr,map2) importFrom(purrr,map2_dbl) @@ -93,8 +99,11 @@ importFrom(purrr,reduce) importFrom(rlang,.data) importFrom(stats,.getXlevels) importFrom(stats,.lm.fit) +importFrom(stats,as.formula) importFrom(stats,binomial) +importFrom(stats,coef) importFrom(stats,coefficients) +importFrom(stats,fitted) importFrom(stats,glm.control) importFrom(stats,glm.fit) importFrom(stats,lm.fit) diff --git a/R/PLNfit-class.R b/R/PLNfit-class.R index 98c5fa54..e8ddd2ce 100644 --- a/R/PLNfit-class.R +++ b/R/PLNfit-class.R @@ -184,7 +184,6 @@ PLNfit <- R6Class( out }, - ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ## PRIVATE METHODS FOR VARIANCE OF THE ESTIMATORS ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @@ -345,7 +344,6 @@ PLNfit <- R6Class( ## END OF PRIVATE METHODS ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - ), ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ## PUBLIC MEMBERS @@ -373,10 +371,6 @@ PLNfit <- R6Class( private$S <- control$inception$var_par$S } else { if (control$trace > 1) cat("\n Use LM after log transformation to define the inceptive model") - # fits <- lm.fit(weights * covariates, weights * log((1 + responses)/exp(offsets))) - # private$B <- matrix(fits$coefficients, d, p) - # private$M <- matrix(fits$residuals, n, p) - # private$S <- matrix(.1, n, p) start_point <- compute_PLN_starting_point(Y = responses, X = covariates, O = offsets, w = weights) private$B <- start_point$B private$M <- start_point$M diff --git a/R/RcppExports.R b/R/RcppExports.R index 3536c3f7..b320c8c3 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -45,6 +45,42 @@ nlopt_optimize_vestep_spherical <- function(data, params, B, Omega, config) { .Call('_PLNmodels_nlopt_optimize_vestep_spherical', PACKAGE = 'PLNmodels', data, params, B, Omega, config) } +zipln_vloglik <- function(Y, X, O, Pi, Omega, B, R, M, S) { + .Call('_PLNmodels_zipln_vloglik', PACKAGE = 'PLNmodels', Y, X, O, Pi, Omega, B, R, M, S) +} + +optim_zipln_Omega_full <- function(M, X, B, S) { + .Call('_PLNmodels_optim_zipln_Omega_full', PACKAGE = 'PLNmodels', M, X, B, S) +} + +optim_zipln_Omega_spherical <- function(M, X, B, S) { + .Call('_PLNmodels_optim_zipln_Omega_spherical', PACKAGE = 'PLNmodels', M, X, B, S) +} + +optim_zipln_Omega_diagonal <- function(M, X, B, S) { + .Call('_PLNmodels_optim_zipln_Omega_diagonal', PACKAGE = 'PLNmodels', M, X, B, S) +} + +optim_zipln_B_dense <- function(M, X) { + .Call('_PLNmodels_optim_zipln_B_dense', PACKAGE = 'PLNmodels', M, X) +} + +optim_zipln_zipar_covar <- function(init_B0, X, R, configuration) { + .Call('_PLNmodels_optim_zipln_zipar_covar', PACKAGE = 'PLNmodels', init_B0, X, R, configuration) +} + +optim_zipln_R <- function(Y, X, O, M, S, Pi) { + .Call('_PLNmodels_optim_zipln_R', PACKAGE = 'PLNmodels', Y, X, O, M, S, Pi) +} + +optim_zipln_M <- function(init_M, Y, X, O, R, S, B, Omega, configuration) { + .Call('_PLNmodels_optim_zipln_M', PACKAGE = 'PLNmodels', init_M, Y, X, O, R, S, B, Omega, configuration) +} + +optim_zipln_S <- function(init_S, O, M, R, B, diag_Omega, configuration) { + .Call('_PLNmodels_optim_zipln_S', PACKAGE = 'PLNmodels', init_S, O, M, R, B, diag_Omega, configuration) +} + cpp_test_packing <- function() { .Call('_PLNmodels_cpp_test_packing', PACKAGE = 'PLNmodels') } diff --git a/R/ZIPLN.R b/R/ZIPLN.R new file mode 100644 index 00000000..12198c02 --- /dev/null +++ b/R/ZIPLN.R @@ -0,0 +1,96 @@ +#' Zero Inflated Poisson lognormal model +#' +#' Fit the multivariate Zero Inflated Poisson lognormal model with a variational algorithm. Use the (g)lm syntax for model specification (covariates, offsets, subset). +#' +#' @param formula an object of class "formula": a symbolic description of the model to be fitted. See details +#' @param data an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which PLN is called. +#' @param subset an optional vector specifying a subset of observations to be used in the fitting process. +#' @param lambda a positive number controlling the level of sparsity in the regression parameters of the PLN component. Default to 0 (no sparsity). +#' @param rho a positive number controlling the level of sparsity in the inverse covariance matrix. Default to 0 (no sparsity). +#' @param control a list for controlling the optimization. See details. +#' +#' @return an R6 object with class [`ZIPLNfit`] +#' +#' @details The parameter `control` is a list controlling the optimization with the following entries: +#' * covariance a character setting the model for the covariance matrix: either "full", "diagonal", "spherical" or "sparse". Default is "full", automatically set to sparse if rho > 0. +#' * "trace" integer for verbosity. +#' * "maxiter_out" control outer optimization (VEM) maximum number of iterations in the variational E-M algorithm. Default is 100. +#' * "ftol_out" control outer optimization (VEM) stop when an full V-EM iteration changes the ELBO by less than ftol_out. Default is 1e-4. +#' * "ftol_rel" control inner optimization (VE step) stop when an optimization step changes in the objective function by less than ftol multiplied by the absolute value of the parameter. Default is 1e-6 when n < p, 1e-8 otherwise. +#' * "ftol_abs" control inner optimization (VE step) stop when an optimization step changes the objective function by less than ftol multiplied by the absolute value of the parameter. Default is 0 +#' * "xtol_rel" control inner optimization (VE step) stop when an optimization step changes every parameters by less than xtol multiplied by the absolute value of the parameter. Default is 1e-4 +#' * "xtol_abs" control inner optimization (VE step) stop when an optimization step changes every parameters by less than xtol multiplied by the absolute value of the parameter. Default is 0 +#' * "maxeval" control inner optimization (VE step) stop when the number of iteration exceeds maxeval. Default is 1000 +#' * "maxtime" control inner optimization (VE step) stop when the optimization time (in seconds) exceeds maxtime. Default is -1 (no restriction) +#' * "algorithm" control inner optimization (VE step): the optimization method used by NLOPT among LD type, i.e. "CCSAQ", "MMA", "LBFGS", "VAR1", "VAR2". See NLOPT documentation for further details. Default is "CCSAQ". +#' +#' @rdname ZIPLN +#' @include ZIPLNfit-class.R +#' @examples +#' data(scRNA) +#' # data subsample: only 100 random cell and the 50 most varying transcript +#' scRNA <- scRNA[sample.int(nrow(scRNA), 100), ] +#' scRNA$counts <- scRNA$counts[, 1:50] +#' myPLN_full <- ZIPLN(counts ~ 1 + cell_line + offset(log(total_counts)) | 1, data = scRNA) +#' myPLN_sparse <- ZIPLN(counts ~ 1 + offset(log(total_counts)), rho = .5, data = scRNA) +#' myPLN_full$criteria # better BIC with sparse version +#' myPLN_sparse$criteria +#' @seealso The class [`ZIPLNfit`] +#' @importFrom stats model.frame model.matrix model.response model.offset terms as.formula +#' @export +ZIPLN <- function(formula, data, subset, rho = 0, lambda = 0, control = list()) { + + ## extract the data matrices and weights + args <- extract_model_zi(match.call(expand.dots = FALSE), parent.frame()) + + ## define default control parameters for optim and eventually overwrite them by user-defined parameters + ctrl <- ZIPLN_param(control, nrow(args$Y), ncol(args$Y)) + ctrl$lambda <- lambda + ctrl$rho <- rho + ctrl$ziparam <- args$ziparam + if (ctrl$rho > 0) ctrl$covariance <- "sparse" + + ## handling intercept term for penalized regression + if (attr(terms(as.formula(args$formula)), "intercept") >0 & !ctrl$penalize_intercept) + ctrl$ind_intercept <- match("(Intercept)", colnames(args$X)) + + ## initialization + if (ctrl$trace > 0) cat("\n Initialization...") + myPLN <- ZIPLNfit$new(args$Y, args$X, args$O, args$formula, args$xlevels, ctrl) + + ## optimization + if (ctrl$trace > 0) cat("\n Adjusting a ZI-PLN model with", + ctrl$covariance,"covariance model and", + ctrl$ziparam, "specific parameter(s) in Zero inflation component.") + myPLN$optimize(args$Y, args$X, args$O, ctrl) + + if (ctrl$trace > 0) cat("\n DONE!\n") + myPLN +} + +## ----------------------------------------------------------------- +## Series of setter to default parameters for user's main functions + +available_algorithms <- c("MMA", "CCSAQ", "LBFGS", "VAR1", "VAR2", "TNEWTON", "TNEWTON_PRECOND", "TNEWTON_PRECOND_RESTART") + +ZIPLN_param <- function(control, n, p) { + xtol_abs <- ifelse(is.null(control$xtol_abs) , 0 , control$xtol_abs) + ctrl <- list( + "ftol_out" = 1e-8, + "maxit_out" = 100 , + "algorithm" = "CCSAQ", + "maxeval" = 1000 , + "maxtime" = -1 , + "ftol_rel" = 1e-8 , + "ftol_abs" = 0, + "xtol_rel" = 1e-8, + "xtol_abs" = xtol_abs, + "trace" = 1, + "covariance" = "full", + "penalize_intercept" = FALSE + ) + ctrl[names(control)] <- control + stopifnot(ctrl$algorithm %in% available_algorithms) + ctrl +} + diff --git a/R/ZIPLNfit-class.R b/R/ZIPLNfit-class.R new file mode 100644 index 00000000..d84de0ed --- /dev/null +++ b/R/ZIPLNfit-class.R @@ -0,0 +1,335 @@ +#' An R6 Class to represent a ZIPLNfit +#' +#' @description The function [ZIPLN()] fit a model which is an instance of a object with class [`ZIPLNfit`]. +#' +#' This class comes with a set of R6 methods, some of them being useful for the user and exported as S3 methods. +#' See the documentation for [coef()], [sigma()], [predict()]. +#' +#' Fields are accessed via active binding and cannot be changed by the user. +#' +#' @param responses the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class +#' @param covariates design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class +#' @param offsets offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class +#' @param formula model formula used for fitting, extracted from the formula in the upper-level call +#' @param control a list for controlling the optimization. See details. +#' @param xlevels named listed of factor levels included in the models, extracted from the formula in the upper-level call and used for predictions. +#' +#' @inherit ZIPLN details +#' +#' @importFrom R6 R6Class +#' +#' @examples +#' \dontrun{ +#' data(scRNA) +#' # data subsample: only 100 random cell and the 50 most varying transcript +#' subset <- sample.int(nrow(scRNA), 100) +#' myPLN <- ZIPLN(counts[, 1:50] ~ 1 + offset(log(total_counts)), subset = subset, data = scRNA) +#' } +ZIPLNfit <- R6Class( + classname = "ZIPLNfit", + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + ## PUBLIC MEMBERS + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + public = list( + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + ## Creation functions ---------------- + #' @description + #' Update a [`ZIPLNfit`] object + #' @param B matrix of regression parameters in the Poisson lognormal component + #' @param B0 matrix of regression parameters in the zero inflated component + #' @param Pi Zero inflated probability parameter (either scalar, row-vector, col-vector of matrix) + #' @param Omega precision matrix of the latent variables + #' @param Sigma covariance matrix of the latent variables + #' @param M matrix of mean vectors for the variational approximation + #' @param S matrix of variance parameters for the variational approximation + #' @param R matrix of probabilities for the variational approximation + #' @param Ji vector of variational lower bounds of the log-likelihoods (one value per sample) + #' @param Z matrix of latent vectors (includes covariates and offset effects) + #' @param A matrix of fitted values + #' @param monitoring a list with optimization monitoring quantities + #' @return Update the current [`ZIPLNfit`] object + update = function(B=NA, B0=NA, Pi=NA, Omega=NA, Sigma=NA, M=NA, S=NA, R=NA, Ji=NA, Z=NA, A=NA, monitoring=NA) { + if (!anyNA(B)) private$B <- B + if (!anyNA(B0)) private$B0 <- B0 + if (!anyNA(Pi)) private$Pi <- Pi + if (!anyNA(Omega)) private$Omega <- Omega + if (!anyNA(Sigma)) private$Sigma <- Sigma + if (!anyNA(M)) private$M <- M + if (!anyNA(S)) private$S <- S + if (!anyNA(R)) private$R <- R + if (!anyNA(Z)) private$Z <- Z + if (!anyNA(A)) private$A <- A + if (!anyNA(Ji)) private$Ji <- Ji + if (!anyNA(monitoring)) private$monitoring <- monitoring + }, + + #' @description Initialize a [`ZIPLNfit`] model + #' @importFrom stats glm.fit residuals poisson fitted coef + #' @importFrom pscl zeroinfl + initialize = function(responses, covariates, offsets, formula, xlevels, control) { + ## problem dimensions + n <- nrow(responses); p <- ncol(responses); d <- ncol(covariates) + + ## save the formula call as specified by the user + private$formula <- formula + private$xlevels <- xlevels + private$X <- covariates + ## initialize the covariance model + private$covariance <- control$covariance + private$ziparam <- control$ziparam + + R <- matrix(0, n, p) + M <- matrix(0, n, p) + B <- matrix(0, d, p) + B0 <- matrix(0, d, p) + Pi <- matrix(0, n, p) + for (j in 1:p) { + y = responses[, j] + if (min(y == 0)) { + zip_out <- switch(control$ziparam, + "single" = pscl::zeroinfl(y ~ 0 + covariates | 1 , offset = offsets[, j]), + "row" = pscl::zeroinfl(y ~ 0 + covariates | 1:n, offset = offsets[, j]), + "col" = pscl::zeroinfl(y ~ 0 + covariates | 1 , offset = offsets[, j]), + "covar" = pscl::zeroinfl(y ~ 0 + covariates , offset = offsets[, j])) # offset only for the count model + B0[,j] <- coef(zip_out, "zero") + B[,j] <- coef(zip_out, "count") + R[, j] <- predict(zip_out, type = "zero") + M[,j] <- residuals(zip_out, type = "pearson") + covariates %*% coef(zip_out, "count") + } else { + p_out <- glm(y ~ 0 + covariates, family = 'poisson', offset = offsets[, j]) + B0[,j] <- rep(-10, d) + B[,j] <- coef(p_out) + R[, j] <- 0 + M[,j] <- residuals(p_out) + covariates %*% coef(p_out) + } + } + + ## Initialization of the ZI component + private$R <- R + private$Pi <- switch(control$ziparam, + "single" = matrix( mean(R), n, p) , + "row" = matrix(rowMeans(R), n, p) , + "col" = matrix(colMeans(R), n, p, byrow = TRUE), + "covar" = R) + private$B0 <- B0 + delta <- 1 * (responses == 0) + private$zeros <- delta + + ## Initialization of the PLN component + private$B <- B + private$M <- M + private$S <- matrix(.1, n, p) + }, + + #' @description Call to the Cpp optimizer and update of the relevant fields + #' @param control a list for controlling the optimization. See details. + optimize = function(responses, covariates, offsets, control) { + + args <- list(Y = responses, X = covariates, O = offsets, configuration = control) + args$init_parameters <- + list(Omega = NA, B0 = private$B0, B = private$B, Pi = private$Pi, + M = private$M, S = private$S, R = private$R) + optim_out <- do.call(optimize_zi, args) + + self$update( + B = optim_out$parameters$B, + B0 = optim_out$parameters$B0, + Pi = optim_out$parameters$Pi, + Omega = optim_out$parameters$Omega, + M = optim_out$parameters$M, + S = optim_out$parameters$S, + R = optim_out$parameters$R, + Z = offsets + optim_out$parameters$M, + A = exp(offsets + optim_out$parameters$M + .5 * optim_out$parameters$S^2), + Ji = optim_out$vloglik, + monitoring = list( + iterations = optim_out$nb_iter, + message = optim_out$stop_reason, + objective = optim_out$criterion) + ) + + if (is.null(colnames(responses))) colnames(responses) <- paste0("Y", 1:self$p) + colnames(private$B0) <- colnames(private$B) <- colnames(responses) + rownames(private$B0) <- rownames(private$B) <- colnames(covariates) + rownames(private$Omega) <- colnames(private$Omega) <- colnames(private$Pi) <- colnames(responses) + rownames(private$M) <- rownames(private$S) <- rownames(private$R) <- rownames(private$Pi) <- rownames(responses) + + }, + + # #' @description Result of one call to the VE step of the optimization procedure: optimal variational parameters (M, S) and corresponding log likelihood values for fixed model parameters (Sigma, B). Intended to position new data in the latent space. + # #' @return A list with three components: + # #' * the matrix `M` of variational means, + # #' * the matrix `S2` of variational variances + # #' * the vector `log.lik` of (variational) log-likelihood of each new observation + # VEstep = function(covariates, offsets, responses, control = list()) { + # + # # problem dimension + # n <- nrow(responses); p <- ncol(responses); d <- ncol(covariates) + # + # ## define default control parameters for optim and overwrite by user defined parameters + # control$covariance <- self$vcov_model + # control <- ZIPLN_param(control, n, p) + # + # VEstep_optimizer <- + # switch(control$covariance, + # "spherical" = cpp_optimize_vestep_spherical, + # "diagonal" = cpp_optimize_vestep_diagonal, + # "full" = cpp_optimize_vestep_full, + # "genetic" = cpp_optimize_vestep_full + # ) + # + # ## Initialize the variational parameters with the appropriate new dimension of the data + # optim_out <- VEstep_optimizer( + # list(M = matrix(0, n, p), S = matrix(sqrt(0.1), n, p)), + # responses, + # covariates, + # offsets, + # weights, + # B = self$model_par$B, + # ## Robust inversion using Matrix::solve instead of solve.default + # Omega = as(Matrix::solve(Matrix::Matrix(self$model_par$Sigma)), 'matrix'), + # control + # ) + # + # Ji <- optim_out$loglik + # attr(Ji, "weights") <- weights + # + # ## output + # list(M = optim_out$M, + # S2 = (optim_out$S)**2, + # log.lik = setNames(Ji, rownames(responses))) + # }, + + # #' @description Predict position, scores or observations of new data. + # #' @param newdata A data frame in which to look for variables with which to predict. If omitted, the fitted values are used. + # #' @param type Scale used for the prediction. Either `link` (default, predicted positions in the latent space) or `response` (predicted counts). + # #' @param envir Environment in which the prediction is evaluated + # #' @return A matrix with predictions scores or counts. + # predict = function(newdata, type = c("link", "response"), envir = parent.frame()) { + # type = match.arg(type) + # + # ## Extract the model matrices from the new data set with initial formula + # X <- model.matrix(formula(private$formula)[-2], newdata, xlev = private$xlevels) + # O <- model.offset(model.frame(formula(private$formula)[-2], newdata)) + # + # ## mean latent positions in the parameter space + # EZ <- X %*% private$B + # if (!is.null(O)) EZ <- EZ + O + # EZ <- sweep(EZ, 2, .5 * diag(self$model_par$Sigma), "+") + # colnames(EZ) <- colnames(private$Sigma) + # + # results <- switch(type, link = EZ, response = exp(EZ)) + # + # attr(results, "type") <- type + # results + # }, + + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + ## Print functions ----------------------- + #' @description User friendly print method + #' @param model First line of the print output + show = function(model = paste("A multivariate Zero Inflated Poisson Lognormal fit with", private$covariance, "covariance model.\n")) { + cat(model) + cat("================================================================================\n") + print(as.data.frame(round(self$criteria, digits = 3), row.names = "")) + cat("================================================================================\n") + cat("* Useful fields\n") + cat(" $model_par, $var_par, $optim_par, $nb_param\n") + cat(" $loglik, $BIC, $ICL, $loglik_vec, $criteria\n") + cat("* Useful S3 methods\n") + cat(" print(), coef(), sigma(), fitted()\n") + }, + + #' @description User friendly print method + print = function() { self$show() } + + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + ## Other functions ---------------- + ), + + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + ## PRIVATE MEMBERS ---- + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + private = list( + formula = NA, # the formula call for the model as specified by the user + xlevels = NA, # factor levels present in the original data, useful for predict() methods. + X = NA, # design matrix + B = NA, # the model parameters for the covariable effect (PLN part) + B0 = NA, # the model parameters for the covariate effects ('0'/Bernoulli part) + Pi = NA, # the probability parameters for the '0'/Bernoulli part + Omega = NA, # the precision matrix + S = NA, # the variational parameters for the variances + M = NA, # the variational parameters for the means + Z = NA, # the matrix of latent variable + P = NA, # the matrix of latent variable without covariates effect + zeros = NA, # an indicator of the zeros + A = NA, # the matrix of expected counts + R = NA, # probabilities for being observed + Ji = NA, # element-wise approximated loglikelihood + covariance = NA, # a string describing the covariance model + ziparam = NA, # a string describing the ZI parametrisation + monitoring = NA # a list with optimization monitoring quantities + ), + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + ## ACTIVE BINDINGS ---- + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + active = list( + #' @field n number of samples + n = function() {nrow(private$M)}, + #' @field p number of species + p = function() {ncol(private$M)}, + #' @field d number of covariates + d = function() {nrow(private$B)}, + #' @field latent a matrix: values of the latent vector (Z in the model) + latent = function() {private$Z}, + #' @field latent_pos a matrix: values of the latent position vector (Z) without covariates effects or offset + latent_pos = function() {private$M - private$X %*% private$B}, + #' @field model_par a list with the matrices of parameters found in the model (B, Sigma, plus some others depending on the variant) + model_par = function() {list(B = private$B, B0 = private$B0, Pi = private$Pi, Omega = private$Omega)}, + #' @field var_par a list with two matrices, M and S2, which are the estimated parameters in the variational approximation + var_par = function() {list(M = private$M, S2 = private$S^2, R = private$R)}, + #' @field fitted a matrix: fitted values of the observations (A in the model) + fitted = function() {private$R * private$zeros + (1 - private$R) * private$A}, + #' @field nb_param number of parameters in the current PLN model + nb_param = function() { + res <- self$p * self$d + + switch(private$ziparam, + "single" = 1, + "row" = self$n, + "col" = self$p, + "covar" = self$p * self$d) + + switch(private$covariance, + "full" = self$p * (self$p + 1)/2, + "diagonal" = self$p, + "spherical" = 1, + "sparse" = (sum(private$Omega != 0) - self$p)/2L) + as.integer(res) + }, + #' @field vcov_model character: the model used for the covariance (either "spherical", "diagonal" or "full") + vcov_model = function() {private$covariance}, + #' @field optim_par a list with parameters useful for monitoring the optimization + optim_par = function() {private$monitoring}, + #' @field loglik (weighted) variational lower bound of the loglikelihood + loglik = function() {sum(private$Ji) }, + #' @field loglik_vec element-wise variational lower bound of the loglikelihood + loglik_vec = function() {private$Ji}, + #' @field BIC variational lower bound of the BIC + BIC = function() {self$loglik - .5 * log(self$n) * self$nb_param}, + #' @field entropy Entropy of the variational distribution + entropy = function() {self$entropy_ZI + self$entropy_PLN}, + #' @field entropy_ZI Entropy of the variational distribution + entropy_ZI = function() {-sum(.xlogx(1-private$R)) - sum(.xlogx(private$R))}, + #' @field entropy_PLN Entropy of the Gaussian variational distribution in the PLN component + entropy_PLN = function() {.5 * (self$n * self$p * log(2*pi*exp(1)) + sum(log(private$S^2)))}, + #' @field ICL variational lower bound of the ICL + ICL = function() {self$BIC - self$entropy}, + #' @field criteria a vector with loglik, BIC, ICL and number of parameters + criteria = function() {data.frame(nb_param = self$nb_param, loglik = self$loglik, BIC = self$BIC, ICL = self$ICL)} + ) + + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + ## END OF THE CLASS ---- + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +) + diff --git a/R/ZIPLNfits-S3methods.R b/R/ZIPLNfits-S3methods.R new file mode 100644 index 00000000..18b80155 --- /dev/null +++ b/R/ZIPLNfits-S3methods.R @@ -0,0 +1,86 @@ +## ========================================================================================= +## +## PUBLIC S3 METHODS FOR ZIPLNfit +## +## ========================================================================================= + +## Auxiliary functions to check the given class of an objet +isZIPLNfit <- function(Robject) {inherits(Robject, "ZIPLNfit")} + +# #' Predict counts of a new sample +# #' +# #' @name predict.ZIPLNfit +# #' +# #' @param object an R6 object with class [`ZIPLNfit`] +# #' @param newdata A data frame in which to look for variables and offsets with which to predict +# #' @param type The type of prediction required. The default is on the scale of the linear predictors (i.e. log average count) +# #' @param ... additional parameters for S3 compatibility. Not used +# #' @return A matrix of predicted log-counts (if `type = "link"`) or predicted counts (if `type = "response"`). +# #' @importFrom stats predict +# #' @export +# predict.ZIPLNfit <- function(object, newdata, type = c("link", "response"), ...) { +# stopifnot(isZIPLNfit(object)) +# object$predict(newdata, type, parent.frame()) +# } + +#' Extract model coefficients +#' +#' @description Extracts model coefficients from objects returned by [ZIPLN()] and its variants +#' +#' @name coef.ZIPLNfit +#' +#' @param object an R6 object with class [`ZIPLNfit`] +#' @param type type of parameter that should be extracted. Either "mainPLN" (default) for \deqn{\Theta}, +#' "mainZI" (default) for \deqn{\Theta0} or "precision" for \deqn{\Omega} +#' @param ... additional parameters for S3 compatibility. Not used +#' @return A matrix of coefficients extracted from the ZIPLNfit model. +#' +#' @importFrom stats coef +#' @seealso [sigma.ZIPLNfit()] +#' +#' @examples +#' data(scRNA) +#' # data subsample: only 100 random cell and the 50 most varying transcript +#' subset <- sample.int(nrow(scRNA), 100) +#' myPLN <- ZIPLN(counts[, 1:50] ~ 1 + offset(log(total_counts)), subset = subset, data = scRNA) +#' +#' @export +coef.ZIPLNfit <- function(object, type = c("mainPLN", "mainZI", "precision"), ...) { + stopifnot(isZIPLNfit(object)) + switch(match.arg(type), + mainPLN = object$model_par$Theta, + mainZI = object$model_par$Theta0, + precision = object$model_par$Omega) +} + +#' Extracts model fitted values from objects returned by [ZIPLN()] and its variants +#' +#' @name fitted.ZIPLNfit +#' +#' @inheritParams coef.ZIPLNfit +#' @return A matrix of Fitted values extracted from the object object. +#' +#' @importFrom stats fitted +#' @export +fitted.ZIPLNfit <- function(object, ...) { + stopifnot(isZIPLNfit(object)) + object$fitted +} + +#' Extract variance-covariance of residuals 'Sigma' +#' +#' @name sigma.ZIPLNfit +#' @description Extract the variance-covariance matrix of the residuals, usually noted \deqn{\Sigma} in ZIPLN models. +#' +#' @inheritParams coef.ZIPLNfit +#' +#' @return A semi definite positive matrix of size p, assuming there are p species in the model. +#' +#' @export +#' +#' @importFrom stats sigma +sigma.ZIPLNfit <- function(object, ...) { + stopifnot(isZIPLNfit(object)) + solve(object$model_par$Omega) +} + diff --git a/R/optim-zipln.R b/R/optim-zipln.R new file mode 100644 index 00000000..4a93f2ed --- /dev/null +++ b/R/optim-zipln.R @@ -0,0 +1,217 @@ +# ZI optimization: +# +# init_parameters: named list(B, B0, Omega, M, S2, R) +# Y, X, O: observed data, constant +# configuration: named list(algorithm, xtol_rel, xtol_abs, ftol_rel, ftol_abs, maxeval, maxtime) +# +# Returns named list(parameters: named list, nb_iter: int, stop_reason: str) +# +# Configuration : +# Forwarded to nloptr iterative steps (c,e,f). +# Except for algorithm, the keys are optional termination criteria. Not all of them are needed. +# A missing key or a key set to a negative number means disabled. +# xtol_abs: double for every parameter, or named list with values for each parameter. +# +# xtol_abs, xtol_rel, maxeval are also used for overall loop. +# ftol_abs and ftol_rel could maybe be used if defined on J(params), but I'm not sure. +# +# Dimensions are checked only on C++ side. +optimize_zi <- function(init_parameters, Y, X, O, configuration) { + + n <- nrow(Y); p <- ncol(Y); d <- ncol(X) + + # Link to the approximate function to optimize Omega ,depending on the target structure + optim_zipln_Omega <- switch( + configuration$covariance, + "spherical" = optim_zipln_Omega_spherical, + "diagonal" = optim_zipln_Omega_diagonal, + "full" = optim_zipln_Omega_full, + "sparse" = function(M, X, B, S) optim_zipln_Omega_sparse(M, X, B, S, rho = configuration$rho) + ) + + optim_zipln_zipar <- switch( + configuration$ziparam, + "single" = function(init_B0, X, R, config) list(Pi = matrix( mean(R), n, p) , B0 = matrix(NA, d, p)), + "row" = function(init_B0, X, R, config) list(Pi = matrix(rowMeans(R), n, p) , B0 = matrix(NA, d, p)), + "col" = function(init_B0, X, R, config) list(Pi = matrix(colMeans(R), n, p, byrow = TRUE), B0 = matrix(NA, d, p)), + "covar" = optim_zipln_zipar_covar + ) + + maxit_out <- if("maxit_out" %in% names(configuration)) { configuration$maxit_out } else { 50 } + + # Main loop + nb_iter <- 0 + parameters <- init_parameters + criterion <- vector("numeric", maxit_out) + vloglik <- -Inf; objective <- Inf + repeat { + # Check maxeval + if(maxit_out >= 0 && nb_iter >= maxit_out) { + return(list( + parameters = parameters, + nb_iter = nb_iter, + stop_reason = "maximum number of iterations reached", + criterion = criterion[1:nb_iter], + vloglik = vloglik + )) + } + + # M Step + new_Omega <- optim_zipln_Omega( + M = parameters$M, X = X, B = parameters$B, S = parameters$S + ) + new_B <- optim_zipln_B( + M = parameters$M, X = X, Omega = new_Omega, configuration + ) + + optim_new_zipar <- optim_zipln_zipar( + init_B0 = parameters$B0, X = X, R = parameters$R, config = configuration + ) + new_B0 <- optim_new_zipar$B0 + new_Pi <- optim_new_zipar$Pi + + # VE Step + new_R <- optim_zipln_R( + Y = Y, X = X, O = O, M = parameters$M, S = parameters$S, Pi = new_Pi + ) + optim_new_M <- optim_zipln_M( + init_M = parameters$M, + Y = Y, X = X, O = O, R = new_R, S = parameters$S, B = new_B, Omega = new_Omega, + configuration = configuration + ) + new_M <- optim_new_M$M + optim_new_S <- optim_zipln_S( + init_S = parameters$S, + O = O, M = new_M, R = new_R, B = new_B, diag_Omega = diag(new_Omega), + configuration = configuration + ) + new_S <- optim_new_S$S + # print(optim_new_S$status) + + # Check convergence + new_parameters <- list( + Omega = new_Omega, B = new_B, B0 = new_B0, Pi = new_Pi, + R = new_R, M = new_M, S = new_S + ) + nb_iter <- nb_iter + 1 + + vloglik <- zipln_vloglik( + Y, X, O, new_Pi, new_Omega, new_B, new_R, new_M, new_S + ) + + criterion[nb_iter] <- new_objective <- -sum(vloglik) + + objective_converged <- + (objective - new_objective) < configuration$ftol_out | + (objective - new_objective)/abs(new_objective) < configuration$ftol_out + + parameters_converged <- parameter_list_converged( + parameters, new_parameters, + xtol_abs = configuration$xtol_abs, xtol_rel = configuration$xtol_rel + ) + + if (parameters_converged | objective_converged) { + return(list( + parameters = new_parameters, + nb_iter = nb_iter, + stop_reason = "converged", + criterion = criterion[1:nb_iter], + vloglik = vloglik + )) + } + + parameters <- new_parameters + objective <- new_objective + } +} + +#' @importFrom glassoFast glassoFast +optim_zipln_Omega_sparse <- function(M, X, B, S, rho) { + n <- nrow(M); p <- ncol(M) + glassoFast::glassoFast( crossprod(M - X %*% B)/n + diag(colMeans(S * S), p, p), rho = rho )$wi +} + +#' @importFrom glmnet glmnet +optim_zipln_B <- function(M, X, Omega, config) { + + if(config$lambda > 0) { + if (!is.null(config$ind_intercept)) { + m_bar <- colMeans(M) + x_bar <- colMeans(X[, -config$ind_intercept]) + X <- scale(X[, -config$ind_intercept], x_bar, FALSE) + M <- scale(M, m_bar, FALSE) + } + p <- ncol(M); d <- ncol(X) + if (d > 0) { + Omega12 <- chol(Omega) + y <- as.vector(M %*% t(Omega12)) + x <- kronecker(Omega12, X) + glmnet_out <- glmnet(x, y, lambda = config$lambda, intercept = FALSE, standardize = FALSE) + B <- matrix(as.numeric(glmnet_out$beta), nrow = d, ncol = p) + } else { + B <- matrix(0, nrow = d, ncol = p) + } + + if (!is.null(config$ind_intercept)) { + mu0 <- m_bar - as.vector(crossprod(B, x_bar)) + B <- rbind(mu0, B) + } + + } else { + B <- optim_zipln_B_dense(M, X) + } + B +} + +# Test convergence for a named list of parameters +# oldp, newp: named list of parameters +# xtol_rel: double ; negative or NULL = disabled +# xtol_abs: double ; negative or NULL = disabled +# Returns boolean +parameter_list_converged <- function(oldp, newp, xtol_abs = NULL, xtol_rel = NULL) { + # Strategy is to compare each pair of list elements with matching names. + # Named lists are just vectors (T,str) using order of insertion. + # mapply() is handy to do the pair tests, but it works on the underlying vector order (ignoring names). + # So reorder lists by their names to use mapply. + stopifnot(is.list(oldp), is.list(newp)) + oldp <- oldp[order(names(oldp))] + newp <- newp[order(names(newp))] + stopifnot(all(names(oldp) == names(newp))) + + # Check convergence with xtol_rel if enabled + if(is.double(xtol_rel) && xtol_rel > 0) { + if(all(mapply(function(o, n) { all(abs(n - o) <= xtol_rel * abs(o)) }, oldp, newp))) { + return(TRUE) + } + } + + # Check convergence with xtol_abs (homogeneous) if enabled + if(is.double(xtol_abs) && xtol_abs > 0) { + if(all(mapply(function(o, n) { all(abs(n - o) <= xtol_abs) }, oldp, newp))) { + return(TRUE) + } + } + + # Check convergence with xtol_abs as list(xtol_abs for each param_name) + if(is.list(xtol_abs)) { + xtol_abs <- xtol_abs[order(names(xtol_abs))] + stopifnot(all(names(oldp) == names(xtol_abs))) + # Due to the possible presence of NULLs, mapply may return a list. unlist allows all() to operate anyway. + if(all(unlist(mapply( + function(o, n, tol) { + if((is.double(tol) && tol > 0) || is.matrix(tol)) { + all(abs(n - o) <= tol) + } else { + NULL # Ignore comparison in outer all() + } + }, + oldp, newp, xtol_abs + )))) { + return(TRUE) + } + } + + # If no criteria has triggered, indicate no convergence + FALSE +} + diff --git a/R/scRNA.R b/R/scRNA.R new file mode 100644 index 00000000..6a0377f8 --- /dev/null +++ b/R/scRNA.R @@ -0,0 +1,14 @@ +#' Single cell RNA-seq data +#' +#' A dataset containing the counts of the 500 most varying transcripts in the mixtures of +#' 5 cell lines in human liver (obtained with standard 10x scRNAseq Chromium protocol). +#' +#' @format A data frame named 'scRNA' with 3918 rows (the cells) and 3 variables: +#' \describe{ +#' \item{counts}{a 500 trancript by 3918 count matrix} +#' \item{cell_line}{factor, the cell line of the current row (among 5)} +#' \item{total_counts}{Total number of reads for that cell} +#' ... +#' } +#' @source \url{https://github.com/LuyiTian/sc_mixology/} +"scRNA" diff --git a/R/utils.R b/R/utils.R index 7b5c293d..838f6b66 100644 --- a/R/utils.R +++ b/R/utils.R @@ -172,6 +172,53 @@ extract_model <- function(call, envir) { list(Y = Y, X = X, O = O, miss = is.na(Y), w = w, formula = call$formula) } +#' @importFrom stats .getXlevels +extract_model_zi <- function(call, envir, xlev = NULL) { + + ## create the call for the model frame + call_frame <- call[c(1L, match(c("formula", "data", "subset"), names(call), 0L))] + call_frame[[1]] <- quote(stats::model.frame) + + formula <- as.formula(call$formula) + ff <- formula + + ## Check if a ZI specific formula has been + if (length(ff[[3]]) > 1 && identical(ff[[3]][[1]], as.name("|"))) { + ff_zi <- ~. ; ff_zi[[3]] <- ff[[3]][[3]] ; ff_zi[[2]] <- NULL + ff_pln <- ~. ; ff_pln[[3]] <- ff[[3]][[2]] ; ff_pln[[2]] <- NULL + tt_zi <- terms(ff_zi) ; attr(tt_zi , "offset") <- NULL + tt_pln <- terms(ff_pln) ; attr(tt_pln, "offset") <- NULL + formula[[3]][1] <- call("+") + ziparam <- "covar" + if (tt_zi[[2]] == "row") {ziparam <- "row"; formula[[3]][[3]] <- NULL} + if (tt_zi[[2]] == "col") {ziparam <- "col"; formula[[3]][[3]] <- NULL} + call_frame$formula <- formula + } else { + ff_pln <- ff + tt_pln <- terms(ff_pln) ; attr(tt_pln, "offset") <- NULL + ziparam <- "single" + } + + ## eval the call in the parent environment + frame <- eval(call_frame, envir) + ## create the set of matrices to fit the PLN model + Y <- frame[[1L]] ## model.response oversimplifies into a numeric when a single variable is involved + if (ncol(Y) == 1 & is.null(colnames(Y))) colnames(Y) <- "Y" + + ## Extract the design matrices for ZI and PLN components + X <- model.matrix(tt_pln, frame, xlev = xlev$pln) + X0 <- switch(ziparam, "covar" = model.matrix(tt_zi, frame, xlev = xlev$zi), NULL) + + # Offsets are only considered for the PLN component + O <- model.offset(frame) + if (is.null(O)) O <- matrix(0, nrow(Y), ncol(Y)) + if (is.vector(O)) O <- O %o% rep(1, ncol(Y)) + + ## Save encoutered levels for predict methods + xlevels <- .getXlevels(terms(frame), frame) + list(Y = Y, X = X, X0 = X0, O = O, formula = call$formula, ziparam = ziparam, xlevels = xlevels) +} + edge_to_node <- function(x, n = max(x)) { x <- x - 1 ## easier for arithmetic to number edges starting from 0 n.node <- round((1 + sqrt(1 + 8*n)) / 2) ## n.node * (n.node -1) / 2 = n (if integer) diff --git a/data-raw/scRNA.R b/data-raw/scRNA.R new file mode 100644 index 00000000..df7bb57f --- /dev/null +++ b/data-raw/scRNA.R @@ -0,0 +1,25 @@ +## code to prepare `scRNA` dataset goes here + +library(scran) # bioManager::install("scran") +library(scater) # bioManager::install("scater") +library(ggplot2) # install.packages("ggplot2") + +## get data from "https://github.com/LuyiTian/sc_mixology/raw/master/data/sincell_with_class_5cl.RData") +load("~/Downloads/sincell_with_class_5cl.RData") + + +sce_qc <- computeSumFactors(sce_sc_10x_5cl_qc) +sce_log_qc <- logNormCounts(sce_sc_10x_5cl_qc) + +sce_log_qc$cell_line <- as.factor(sce_log_qc$cell_line) +myPCA <- scater::calculatePCA(sce_log_qc) +top_features <- rownames(attr(myPCA, "rotation")) +counts <- data.matrix(t(counts(sce_log_qc)[top_features , ])) +scRNA <- data.frame( + counts = NA, ## placeholder for Abundance, to avoid using I() and inheriting "AsIs" class + cell_line = as.factor(sce_log_qc$cell_line), + total_counts = sce_log_qc$total_count_per_cell +) +scRNA$counts <- counts + +usethis::use_data(scRNA, overwrite = TRUE) diff --git a/data/scRNA.rda b/data/scRNA.rda new file mode 100644 index 0000000000000000000000000000000000000000..80f10d598544f8d609e63983ad3f33c8b0f6e430 GIT binary patch literal 1056895 zcmagFV{j$T6E=F16Whj#ZQHhO+qSW>HnummHnwf-#x^#7fB*O1ueWZ07<5g~%rj3< 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+ZIPLN(formula, data, subset, rho = 0, lambda = 0, control = list()) +} +\arguments{ +\item{formula}{an object of class "formula": a symbolic description of the model to be fitted. See details} + +\item{data}{an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which PLN is called.} + +\item{subset}{an optional vector specifying a subset of observations to be used in the fitting process.} + +\item{rho}{a positive number controlling the level of sparsity in the inverse covariance matrix. Default to 0 (no sparsity).} + +\item{lambda}{a positive number controlling the level of sparsity in the regression parameters of the PLN component. Default to 0 (no sparsity).} + +\item{control}{a list for controlling the optimization. See details.} +} +\value{ +an R6 object with class \code{\link{ZIPLNfit}} +} +\description{ +Fit the multivariate Zero Inflated Poisson lognormal model with a variational algorithm. Use the (g)lm syntax for model specification (covariates, offsets, subset). +} +\details{ +The parameter \code{control} is a list controlling the optimization with the following entries: +\itemize{ +\item covariance a character setting the model for the covariance matrix: either "full", "diagonal", "spherical" or "sparse". Default is "full", automatically set to sparse if rho > 0. +\item "trace" integer for verbosity. +\item "maxiter_out" control outer optimization (VEM) maximum number of iterations in the variational E-M algorithm. Default is 100. +\item "ftol_out" control outer optimization (VEM) stop when an full V-EM iteration changes the ELBO by less than ftol_out. Default is 1e-4. +\item "ftol_rel" control inner optimization (VE step) stop when an optimization step changes in the objective function by less than ftol multiplied by the absolute value of the parameter. Default is 1e-6 when n < p, 1e-8 otherwise. +\item "ftol_abs" control inner optimization (VE step) stop when an optimization step changes the objective function by less than ftol multiplied by the absolute value of the parameter. Default is 0 +\item "xtol_rel" control inner optimization (VE step) stop when an optimization step changes every parameters by less than xtol multiplied by the absolute value of the parameter. Default is 1e-4 +\item "xtol_abs" control inner optimization (VE step) stop when an optimization step changes every parameters by less than xtol multiplied by the absolute value of the parameter. Default is 0 +\item "maxeval" control inner optimization (VE step) stop when the number of iteration exceeds maxeval. Default is 1000 +\item "maxtime" control inner optimization (VE step) stop when the optimization time (in seconds) exceeds maxtime. Default is -1 (no restriction) +\item "algorithm" control inner optimization (VE step): the optimization method used by NLOPT among LD type, i.e. "CCSAQ", "MMA", "LBFGS", "VAR1", "VAR2". See NLOPT documentation for further details. Default is "CCSAQ". +} +} +\examples{ +data(scRNA) +# data subsample: only 100 random cell and the 50 most varying transcript +scRNA <- scRNA[sample.int(nrow(scRNA), 100), ] +scRNA$counts <- scRNA$counts[, 1:50] +myPLN_full <- ZIPLN(counts ~ 1 + cell_line + offset(log(total_counts)) | 1, data = scRNA) +myPLN_sparse <- ZIPLN(counts ~ 1 + offset(log(total_counts)), rho = .5, data = scRNA) +myPLN_full$criteria # better BIC with sparse version +myPLN_sparse$criteria +} +\seealso{ +The class \code{\link{ZIPLNfit}} +} diff --git a/man/ZIPLNfit.Rd b/man/ZIPLNfit.Rd new file mode 100644 index 00000000..c5c9f576 --- /dev/null +++ b/man/ZIPLNfit.Rd @@ -0,0 +1,244 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/ZIPLNfit-class.R +\name{ZIPLNfit} +\alias{ZIPLNfit} +\title{An R6 Class to represent a ZIPLNfit} +\description{ +The function \code{\link[=ZIPLN]{ZIPLN()}} fit a model which is an instance of a object with class \code{\link{ZIPLNfit}}. + +This class comes with a set of R6 methods, some of them being useful for the user and exported as S3 methods. +See the documentation for \code{\link[=coef]{coef()}}, \code{\link[=sigma]{sigma()}}, \code{\link[=predict]{predict()}}. + +Fields are accessed via active binding and cannot be changed by the user. +} +\details{ +The parameter \code{control} is a list controlling the optimization with the following entries: +\itemize{ +\item covariance a character setting the model for the covariance matrix: either "full", "diagonal", "spherical" or "sparse". Default is "full", automatically set to sparse if rho > 0. +\item "trace" integer for verbosity. +\item "maxiter_out" control outer optimization (VEM) maximum number of iterations in the variational E-M algorithm. Default is 100. +\item "ftol_out" control outer optimization (VEM) stop when an full V-EM iteration changes the ELBO by less than ftol_out. Default is 1e-4. +\item "ftol_rel" control inner optimization (VE step) stop when an optimization step changes in the objective function by less than ftol multiplied by the absolute value of the parameter. Default is 1e-6 when n < p, 1e-8 otherwise. +\item "ftol_abs" control inner optimization (VE step) stop when an optimization step changes the objective function by less than ftol multiplied by the absolute value of the parameter. Default is 0 +\item "xtol_rel" control inner optimization (VE step) stop when an optimization step changes every parameters by less than xtol multiplied by the absolute value of the parameter. Default is 1e-4 +\item "xtol_abs" control inner optimization (VE step) stop when an optimization step changes every parameters by less than xtol multiplied by the absolute value of the parameter. Default is 0 +\item "maxeval" control inner optimization (VE step) stop when the number of iteration exceeds maxeval. Default is 1000 +\item "maxtime" control inner optimization (VE step) stop when the optimization time (in seconds) exceeds maxtime. Default is -1 (no restriction) +\item "algorithm" control inner optimization (VE step): the optimization method used by NLOPT among LD type, i.e. "CCSAQ", "MMA", "LBFGS", "VAR1", "VAR2". See NLOPT documentation for further details. Default is "CCSAQ". +} +} +\examples{ +\dontrun{ +data(scRNA) +# data subsample: only 100 random cell and the 50 most varying transcript +subset <- sample.int(nrow(scRNA), 100) +myPLN <- ZIPLN(counts[, 1:50] ~ 1 + offset(log(total_counts)), subset = subset, data = scRNA) +} +} +\section{Active bindings}{ +\if{html}{\out{

}} +\describe{ +\item{\code{n}}{number of samples} + +\item{\code{p}}{number of species} + +\item{\code{d}}{number of covariates} + +\item{\code{latent}}{a matrix: values of the latent vector (Z in the model)} + +\item{\code{latent_pos}}{a matrix: values of the latent position vector (Z) without covariates effects or offset} + +\item{\code{model_par}}{a list with the matrices of parameters found in the model (B, Sigma, plus some others depending on the variant)} + +\item{\code{var_par}}{a list with two matrices, M and S2, which are the estimated parameters in the variational approximation} + +\item{\code{fitted}}{a matrix: fitted values of the observations (A in the model)} + +\item{\code{nb_param}}{number of parameters in the current PLN model} + +\item{\code{vcov_model}}{character: the model used for the covariance (either "spherical", "diagonal" or "full")} + +\item{\code{optim_par}}{a list with parameters useful for monitoring the optimization} + +\item{\code{loglik}}{(weighted) variational lower bound of the loglikelihood} + +\item{\code{loglik_vec}}{element-wise variational lower bound of the loglikelihood} + +\item{\code{BIC}}{variational lower bound of the BIC} + +\item{\code{entropy}}{Entropy of the variational distribution} + +\item{\code{entropy_ZI}}{Entropy of the variational distribution} + +\item{\code{entropy_PLN}}{Entropy of the Gaussian variational distribution in the PLN component} + +\item{\code{ICL}}{variational lower bound of the ICL} + +\item{\code{criteria}}{a vector with loglik, BIC, ICL and number of parameters} +} +\if{html}{\out{
}} +} +\section{Methods}{ +\subsection{Public methods}{ +\itemize{ +\item \href{#method-ZIPLNfit-update}{\code{ZIPLNfit$update()}} +\item \href{#method-ZIPLNfit-new}{\code{ZIPLNfit$new()}} +\item \href{#method-ZIPLNfit-optimize}{\code{ZIPLNfit$optimize()}} +\item \href{#method-ZIPLNfit-show}{\code{ZIPLNfit$show()}} +\item \href{#method-ZIPLNfit-print}{\code{ZIPLNfit$print()}} +\item \href{#method-ZIPLNfit-clone}{\code{ZIPLNfit$clone()}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-ZIPLNfit-update}{}}} +\subsection{Method \code{update()}}{ +Update a \code{\link{ZIPLNfit}} object +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ZIPLNfit$update( + B = NA, + B0 = NA, + Pi = NA, + Omega = NA, + Sigma = NA, + M = NA, + S = NA, + R = NA, + Ji = NA, + Z = NA, + A = NA, + monitoring = NA +)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{B}}{matrix of regression parameters in the Poisson lognormal component} + +\item{\code{B0}}{matrix of regression parameters in the zero inflated component} + +\item{\code{Pi}}{Zero inflated probability parameter (either scalar, row-vector, col-vector of matrix)} + +\item{\code{Omega}}{precision matrix of the latent variables} + +\item{\code{Sigma}}{covariance matrix of the latent variables} + +\item{\code{M}}{matrix of mean vectors for the variational approximation} + +\item{\code{S}}{matrix of variance parameters for the variational approximation} + +\item{\code{R}}{matrix of probabilities for the variational approximation} + +\item{\code{Ji}}{vector of variational lower bounds of the log-likelihoods (one value per sample)} + +\item{\code{Z}}{matrix of latent vectors (includes covariates and offset effects)} + +\item{\code{A}}{matrix of fitted values} + +\item{\code{monitoring}}{a list with optimization monitoring quantities} +} +\if{html}{\out{
}} +} +\subsection{Returns}{ +Update the current \code{\link{ZIPLNfit}} object +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-ZIPLNfit-new}{}}} +\subsection{Method \code{new()}}{ +Initialize a \code{\link{ZIPLNfit}} model +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ZIPLNfit$new(responses, covariates, offsets, formula, xlevels, control)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{responses}}{the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{covariates}}{design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{offsets}}{offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{formula}}{model formula used for fitting, extracted from the formula in the upper-level call} + +\item{\code{xlevels}}{named listed of factor levels included in the models, extracted from the formula in the upper-level call and used for predictions.} + +\item{\code{control}}{a list for controlling the optimization. See details.} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-ZIPLNfit-optimize}{}}} +\subsection{Method \code{optimize()}}{ +Call to the Cpp optimizer and update of the relevant fields +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ZIPLNfit$optimize(responses, covariates, offsets, control)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{responses}}{the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{covariates}}{design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{offsets}}{offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{control}}{a list for controlling the optimization. See details.} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-ZIPLNfit-show}{}}} +\subsection{Method \code{show()}}{ +User friendly print method +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ZIPLNfit$show( + model = paste("A multivariate Zero Inflated Poisson Lognormal fit with", + private$covariance, "covariance model.\\n") +)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{model}}{First line of the print output} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-ZIPLNfit-print}{}}} +\subsection{Method \code{print()}}{ +User friendly print method +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ZIPLNfit$print()}\if{html}{\out{
}} +} + +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-ZIPLNfit-clone}{}}} +\subsection{Method \code{clone()}}{ +The objects of this class are cloneable with this method. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ZIPLNfit$clone(deep = FALSE)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{deep}}{Whether to make a deep clone.} +} +\if{html}{\out{
}} +} +} +} diff --git a/man/coef.ZIPLNfit.Rd b/man/coef.ZIPLNfit.Rd new file mode 100644 index 00000000..96d445e8 --- /dev/null +++ b/man/coef.ZIPLNfit.Rd @@ -0,0 +1,32 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/ZIPLNfits-S3methods.R +\name{coef.ZIPLNfit} +\alias{coef.ZIPLNfit} +\title{Extract model coefficients} +\usage{ +\method{coef}{ZIPLNfit}(object, type = c("mainPLN", "mainZI", "precision"), ...) +} +\arguments{ +\item{object}{an R6 object with class \code{\link{ZIPLNfit}}} + +\item{type}{type of parameter that should be extracted. Either "mainPLN" (default) for \deqn{\Theta}, +"mainZI" (default) for \deqn{\Theta0} or "precision" for \deqn{\Omega}} + +\item{...}{additional parameters for S3 compatibility. Not used} +} +\value{ +A matrix of coefficients extracted from the ZIPLNfit model. +} +\description{ +Extracts model coefficients from objects returned by \code{\link[=ZIPLN]{ZIPLN()}} and its variants +} +\examples{ +data(scRNA) +# data subsample: only 100 random cell and the 50 most varying transcript +subset <- sample.int(nrow(scRNA), 100) +myPLN <- ZIPLN(counts[, 1:50] ~ 1 + offset(log(total_counts)), subset = subset, data = scRNA) + +} +\seealso{ +\code{\link[=sigma.ZIPLNfit]{sigma.ZIPLNfit()}} +} diff --git a/man/fitted.ZIPLNfit.Rd b/man/fitted.ZIPLNfit.Rd new file mode 100644 index 00000000..4ba1c11d --- /dev/null +++ b/man/fitted.ZIPLNfit.Rd @@ -0,0 +1,19 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/ZIPLNfits-S3methods.R +\name{fitted.ZIPLNfit} +\alias{fitted.ZIPLNfit} +\title{Extracts model fitted values from objects returned by \code{\link[=ZIPLN]{ZIPLN()}} and its variants} +\usage{ +\method{fitted}{ZIPLNfit}(object, ...) +} +\arguments{ +\item{object}{an R6 object with class \code{\link{ZIPLNfit}}} + +\item{...}{additional parameters for S3 compatibility. Not used} +} +\value{ +A matrix of Fitted values extracted from the object object. +} +\description{ +Extracts model fitted values from objects returned by \code{\link[=ZIPLN]{ZIPLN()}} and its variants +} diff --git a/man/scRNA.Rd b/man/scRNA.Rd new file mode 100644 index 00000000..5332438f --- /dev/null +++ b/man/scRNA.Rd @@ -0,0 +1,26 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/scRNA.R +\docType{data} +\name{scRNA} +\alias{scRNA} +\title{Single cell RNA-seq data} +\format{ +A data frame named 'scRNA' with 3918 rows (the cells) and 3 variables: +\describe{ +\item{counts}{a 500 trancript by 3918 count matrix} +\item{cell_line}{factor, the cell line of the current row (among 5)} +\item{total_counts}{Total number of reads for that cell} +... +} +} +\source{ +\url{https://github.com/LuyiTian/sc_mixology/} +} +\usage{ +scRNA +} +\description{ +A dataset containing the counts of the 500 most varying transcripts in the mixtures of +5 cell lines in human liver (obtained with standard 10x scRNAseq Chromium protocol). +} +\keyword{datasets} diff --git a/man/sigma.ZIPLNfit.Rd b/man/sigma.ZIPLNfit.Rd new file mode 100644 index 00000000..4e79c5b7 --- /dev/null +++ b/man/sigma.ZIPLNfit.Rd @@ -0,0 +1,19 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/ZIPLNfits-S3methods.R +\name{sigma.ZIPLNfit} +\alias{sigma.ZIPLNfit} +\title{Extract variance-covariance of residuals 'Sigma'} +\usage{ +\method{sigma}{ZIPLNfit}(object, ...) +} +\arguments{ +\item{object}{an R6 object with class \code{\link{ZIPLNfit}}} + +\item{...}{additional parameters for S3 compatibility. Not used} +} +\value{ +A semi definite positive matrix of size p, assuming there are p species in the model. +} +\description{ +Extract the variance-covariance matrix of the residuals, usually noted \deqn{\Sigma} in ZIPLN models. +} diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp index 22f66bc0..49414744 100644 --- a/src/RcppExports.cpp +++ b/src/RcppExports.cpp @@ -163,6 +163,145 @@ BEGIN_RCPP return rcpp_result_gen; END_RCPP } +// zipln_vloglik +arma::vec zipln_vloglik(const arma::mat& Y, const arma::mat& X, const arma::mat& O, const arma::mat& Pi, const arma::mat& Omega, const arma::mat& B, const arma::mat& R, const arma::mat& M, const arma::mat& S); +RcppExport SEXP _PLNmodels_zipln_vloglik(SEXP YSEXP, SEXP XSEXP, SEXP OSEXP, SEXP PiSEXP, SEXP OmegaSEXP, SEXP BSEXP, SEXP RSEXP, SEXP MSEXP, SEXP SSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type Y(YSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type X(XSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type O(OSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type Pi(PiSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type Omega(OmegaSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type B(BSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type R(RSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type M(MSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type S(SSEXP); + rcpp_result_gen = Rcpp::wrap(zipln_vloglik(Y, X, O, Pi, Omega, B, R, M, S)); + return rcpp_result_gen; +END_RCPP +} +// optim_zipln_Omega_full +arma::mat optim_zipln_Omega_full(const arma::mat& M, const arma::mat& X, const arma::mat& B, const arma::mat& S); +RcppExport SEXP _PLNmodels_optim_zipln_Omega_full(SEXP MSEXP, SEXP XSEXP, SEXP BSEXP, SEXP SSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type M(MSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type X(XSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type B(BSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type S(SSEXP); + rcpp_result_gen = Rcpp::wrap(optim_zipln_Omega_full(M, X, B, S)); + return rcpp_result_gen; +END_RCPP +} +// optim_zipln_Omega_spherical +arma::mat optim_zipln_Omega_spherical(const arma::mat& M, const arma::mat& X, const arma::mat& B, const arma::mat& S); +RcppExport SEXP _PLNmodels_optim_zipln_Omega_spherical(SEXP MSEXP, SEXP XSEXP, SEXP BSEXP, SEXP SSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type M(MSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type X(XSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type B(BSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type S(SSEXP); + rcpp_result_gen = Rcpp::wrap(optim_zipln_Omega_spherical(M, X, B, S)); + return rcpp_result_gen; +END_RCPP +} +// optim_zipln_Omega_diagonal +arma::mat optim_zipln_Omega_diagonal(const arma::mat& M, const arma::mat& X, const arma::mat& B, const arma::mat& S); +RcppExport SEXP _PLNmodels_optim_zipln_Omega_diagonal(SEXP MSEXP, SEXP XSEXP, SEXP BSEXP, SEXP SSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type M(MSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type X(XSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type B(BSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type S(SSEXP); + rcpp_result_gen = Rcpp::wrap(optim_zipln_Omega_diagonal(M, X, B, S)); + return rcpp_result_gen; +END_RCPP +} +// optim_zipln_B_dense +arma::mat optim_zipln_B_dense(const arma::mat& M, const arma::mat& X); +RcppExport SEXP _PLNmodels_optim_zipln_B_dense(SEXP MSEXP, SEXP XSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type M(MSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type X(XSEXP); + rcpp_result_gen = Rcpp::wrap(optim_zipln_B_dense(M, X)); + return rcpp_result_gen; +END_RCPP +} +// optim_zipln_zipar_covar +Rcpp::List optim_zipln_zipar_covar(const arma::mat& init_B0, const arma::mat& X, const arma::mat& R, const Rcpp::List& configuration); +RcppExport SEXP _PLNmodels_optim_zipln_zipar_covar(SEXP init_B0SEXP, SEXP XSEXP, SEXP RSEXP, SEXP configurationSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type init_B0(init_B0SEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type X(XSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type R(RSEXP); + Rcpp::traits::input_parameter< const Rcpp::List& >::type configuration(configurationSEXP); + rcpp_result_gen = Rcpp::wrap(optim_zipln_zipar_covar(init_B0, X, R, configuration)); + return rcpp_result_gen; +END_RCPP +} +// optim_zipln_R +arma::mat optim_zipln_R(const arma::mat& Y, const arma::mat& X, const arma::mat& O, const arma::mat& M, const arma::mat& S, const arma::mat& Pi); +RcppExport SEXP _PLNmodels_optim_zipln_R(SEXP YSEXP, SEXP XSEXP, SEXP OSEXP, SEXP MSEXP, SEXP SSEXP, SEXP PiSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type Y(YSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type X(XSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type O(OSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type M(MSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type S(SSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type Pi(PiSEXP); + rcpp_result_gen = Rcpp::wrap(optim_zipln_R(Y, X, O, M, S, Pi)); + return rcpp_result_gen; +END_RCPP +} +// optim_zipln_M +Rcpp::List optim_zipln_M(const arma::mat& init_M, const arma::mat& Y, const arma::mat& X, const arma::mat& O, const arma::mat& R, const arma::mat& S, const arma::mat& B, const arma::mat& Omega, const Rcpp::List& configuration); +RcppExport SEXP _PLNmodels_optim_zipln_M(SEXP init_MSEXP, SEXP YSEXP, SEXP XSEXP, SEXP OSEXP, SEXP RSEXP, SEXP SSEXP, SEXP BSEXP, SEXP OmegaSEXP, SEXP configurationSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type init_M(init_MSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type Y(YSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type X(XSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type O(OSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type R(RSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type S(SSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type B(BSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type Omega(OmegaSEXP); + Rcpp::traits::input_parameter< const Rcpp::List& >::type configuration(configurationSEXP); + rcpp_result_gen = Rcpp::wrap(optim_zipln_M(init_M, Y, X, O, R, S, B, Omega, configuration)); + return rcpp_result_gen; +END_RCPP +} +// optim_zipln_S +Rcpp::List optim_zipln_S(const arma::mat& init_S, const arma::mat& O, const arma::mat& M, const arma::mat& R, const arma::mat& B, const arma::vec& diag_Omega, const Rcpp::List& configuration); +RcppExport SEXP _PLNmodels_optim_zipln_S(SEXP init_SSEXP, SEXP OSEXP, SEXP MSEXP, SEXP RSEXP, SEXP BSEXP, SEXP diag_OmegaSEXP, SEXP configurationSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type init_S(init_SSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type O(OSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type M(MSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type R(RSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type B(BSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type diag_Omega(diag_OmegaSEXP); + Rcpp::traits::input_parameter< const Rcpp::List& >::type configuration(configurationSEXP); + rcpp_result_gen = Rcpp::wrap(optim_zipln_S(init_S, O, M, R, B, diag_Omega, configuration)); + return rcpp_result_gen; +END_RCPP +} // cpp_test_packing bool cpp_test_packing(); RcppExport SEXP _PLNmodels_cpp_test_packing() { @@ -186,6 +325,15 @@ static const R_CallMethodDef CallEntries[] = { {"_PLNmodels_nlopt_optimize_vestep_rank", (DL_FUNC) &_PLNmodels_nlopt_optimize_vestep_rank, 5}, {"_PLNmodels_nlopt_optimize_spherical", (DL_FUNC) &_PLNmodels_nlopt_optimize_spherical, 3}, {"_PLNmodels_nlopt_optimize_vestep_spherical", (DL_FUNC) &_PLNmodels_nlopt_optimize_vestep_spherical, 5}, + {"_PLNmodels_zipln_vloglik", (DL_FUNC) &_PLNmodels_zipln_vloglik, 9}, + {"_PLNmodels_optim_zipln_Omega_full", (DL_FUNC) &_PLNmodels_optim_zipln_Omega_full, 4}, + {"_PLNmodels_optim_zipln_Omega_spherical", (DL_FUNC) &_PLNmodels_optim_zipln_Omega_spherical, 4}, + {"_PLNmodels_optim_zipln_Omega_diagonal", (DL_FUNC) &_PLNmodels_optim_zipln_Omega_diagonal, 4}, + {"_PLNmodels_optim_zipln_B_dense", (DL_FUNC) &_PLNmodels_optim_zipln_B_dense, 2}, + {"_PLNmodels_optim_zipln_zipar_covar", (DL_FUNC) &_PLNmodels_optim_zipln_zipar_covar, 4}, + {"_PLNmodels_optim_zipln_R", (DL_FUNC) &_PLNmodels_optim_zipln_R, 6}, + {"_PLNmodels_optim_zipln_M", (DL_FUNC) &_PLNmodels_optim_zipln_M, 9}, + {"_PLNmodels_optim_zipln_S", (DL_FUNC) &_PLNmodels_optim_zipln_S, 7}, {"_PLNmodels_cpp_test_packing", (DL_FUNC) &_PLNmodels_cpp_test_packing, 0}, {NULL, NULL, 0} }; diff --git a/src/optim_zi-pln.cpp b/src/optim_zi-pln.cpp new file mode 100644 index 00000000..0f6c3cc7 --- /dev/null +++ b/src/optim_zi-pln.cpp @@ -0,0 +1,264 @@ +#include + +// [[Rcpp::depends(RcppArmadillo)]] +// [[Rcpp::depends(nloptr)]] + +#include "nlopt_wrapper.h" +#include "packing.h" +#include "utils.h" + +// [[Rcpp::export]] +arma::vec zipln_vloglik( + const arma::mat & Y, // responses (n,p) + const arma::mat & X, // covariates (n,d) + const arma::mat & O, // offsets (n,p) + const arma::mat & Pi, // (d,p) + const arma::mat & Omega, // (p,p) + const arma::mat & B, // (d,p) + const arma::mat & R, // (n,p) + const arma::mat & M, // (n,p) + const arma::mat & S // (n,p) +) { + const arma::uword n = Y.n_rows; + const arma::uword p = Y.n_cols; + + const arma::mat S2 = S % S ; + const arma::mat A = exp(O + M + .5 * S2) ; + const arma::mat M_mu = M - X * B ; + const arma::mat mu0 = logit(Pi) ; + return ( + 0.5 * real(log_det(Omega)) + 0.5 * double(p) + + sum( + (1 - R) % ( Y % (O + M) - A - logfact_mat(Y) ) + + R % mu0 - log( 1 + exp(mu0) ) + + 0.5 * log(S2) - 0.5 * ((M_mu * Omega) % M_mu + S2 * diagmat(Omega)) + - R % trunc_log(R) - (1 - R) % trunc_log(1-R), 1) + ) ; +} + +// [[Rcpp::export]] +arma::mat optim_zipln_Omega_full( + const arma::mat & M, // (n,p) + const arma::mat & X, // (n,d) + const arma::mat & B, // (d,p) + const arma::mat & S // (n,p) +) { + const arma::uword n = M.n_rows; + arma::mat M_mu = M - X * B; + return (double(n) * inv_sympd(M_mu.t() * M_mu + diagmat(sum(S % S, 0)))); +} + +// [[Rcpp::export]] +arma::mat optim_zipln_Omega_spherical( + const arma::mat & M, // (n,p) + const arma::mat & X, // (n,d) + const arma::mat & B, // (d,p) + const arma::mat & S // (n,p) +) { + const arma::uword n = M.n_rows; + const arma::uword p = M.n_cols; + double sigma2 = accu( pow(M - X * B, 2) + S % S ) / double(n * p) ; + return arma::diagmat(arma::ones(p)/sigma2) ; +} + +// [[Rcpp::export]] +arma::mat optim_zipln_Omega_diagonal( + const arma::mat & M, // (n,p) + const arma::mat & X, // (n,d) + const arma::mat & B, // (d,p) + const arma::mat & S // (n,p) +) { + const arma::uword n = M.n_rows; + const arma::uword p = M.n_cols; + return arma::diagmat(double(n) / sum( pow(M - X * B, 2) + S % S, 0)) ; +} + +// [[Rcpp::export]] +arma::mat optim_zipln_B_dense( + const arma::mat & M, // (n,p) + const arma::mat & X // (n,d) +) { + // X^T X is sympd, provide this indications to solve() + return solve(X.t() * X, X.t() * M, arma::solve_opts::likely_sympd); +} + +// [[Rcpp::export]] +Rcpp::List optim_zipln_zipar_covar( + const arma::mat & init_B0, // (d,p) + const arma::mat & X, // covariates (n,d) + const arma::mat & R, // (n,p) + const Rcpp::List & configuration // List of config values ; xtol_abs is B0 only (double or mat) +) { + const auto metadata = tuple_metadata(init_B0); + enum { B0_ID }; // Names for metadata indexes + + auto parameters = std::vector(metadata.packed_size); + metadata.map(parameters.data()) = init_B0; + + auto optimizer = new_nlopt_optimizer(configuration, parameters.size()); + if(configuration.containsElementNamed("xtol_abs")) { + SEXP value = configuration["xtol_abs"]; + if(Rcpp::is(value)) { + set_uniform_xtol_abs(optimizer.get(), Rcpp::as(value)); + } else { + auto packed = std::vector(metadata.packed_size); + metadata.map(packed.data()) = Rcpp::as(value); + set_per_value_xtol_abs(optimizer.get(), packed); + } + } + + const arma::mat Xt_R = X.t() * R; + + // Optimize + auto objective_and_grad = [&metadata, &X, &R, &Xt_R](const double * params, double * grad) -> double { + const arma::mat B0 = metadata.map(params); + + arma::mat e_mu0 = exp(X * B0); + double objective = -trace(Xt_R.t() * B0) + accu(log(1. + e_mu0)); + metadata.map(grad) = -Xt_R + X.t() * (e_mu0 % pow(1. + e_mu0, -1)) ; + return objective; + }; + OptimizerResult result = minimize_objective_on_parameters(optimizer.get(), objective_and_grad, parameters); + + arma::mat B0 = metadata.copy(parameters.data()); + return Rcpp::List::create( + Rcpp::Named("status") = static_cast(result.status), + Rcpp::Named("iterations") = result.nb_iterations, + Rcpp::Named("B0") = B0, + Rcpp::Named("Pi") = logistic(X * B0)); +} + +// [[Rcpp::export]] +arma::mat optim_zipln_R( + const arma::mat & Y, // responses (n,p) + const arma::mat & X, // covariates (n,d) + const arma::mat & O, // offsets (n,p) + const arma::mat & M, // (n,p) + const arma::mat & S, // (n,p) + const arma::mat & Pi // (d,p) +) { + arma::mat A = exp(O + M + 0.5 * S % S); + arma::mat R = pow(1. + exp(- (A + logit(Pi))), -1); + // Zero R_{i,j} if Y_{i,j} > 0 + // multiplication with f(sign(Y)) could work to zero stuff as there should not be any +inf + // using a loop as it is more explicit and should have ok performance in C++ + arma::uword n = Y.n_rows; + arma::uword p = Y.n_cols; + for(arma::uword i = 0; i < n; i += 1) { + for(arma::uword j = 0; j < p; j += 1) { + // Add fuzzy comparison ? + if(Y(i, j) > 0.) { + R(i, j) = 0.; + } + } + } + return R; +} + +// [[Rcpp::export]] +Rcpp::List optim_zipln_M( + const arma::mat & init_M, // (n,p) + const arma::mat & Y, // responses (n,p) + const arma::mat & X, // covariates (n,d) + const arma::mat & O, // offsets (n, p) + const arma::mat & R, // (n,p) + const arma::mat & S, // (n,p) + const arma::mat & B, // (d,p) + const arma::mat & Omega, // (p,p) + const Rcpp::List & configuration // List of config values ; xtol_abs is M only (double or mat) +) { + const auto metadata = tuple_metadata(init_M); + enum { M_ID }; // Names for metadata indexes + + auto parameters = std::vector(metadata.packed_size); + metadata.map(parameters.data()) = init_M; + + auto optimizer = new_nlopt_optimizer(configuration, parameters.size()); + if(configuration.containsElementNamed("xtol_abs")) { + SEXP value = configuration["xtol_abs"]; + if(Rcpp::is(value)) { + set_uniform_xtol_abs(optimizer.get(), Rcpp::as(value)); + } else { + auto packed = std::vector(metadata.packed_size); + metadata.map(packed.data()) = Rcpp::as(value); + set_per_value_xtol_abs(optimizer.get(), packed); + } + } + + const arma::mat X_B = X * B; // (n,p) + const arma::mat O_S2 = O + 0.5 * S % S; // (n,p) + + // Optimize + auto objective_and_grad = + [&metadata, &Y, &X, &O_S2, &R, &X_B, &Omega](const double * params, double * grad) -> double { + const arma::mat M = metadata.map(params); + + arma::mat A = exp(O_S2 + M); // (n,p) + arma::mat M_mu_Omega = (M - X_B) * Omega; // (n,p) + + double objective = - trace((1. - R).t() * (Y % M - A)) + 0.5 * trace(M_mu_Omega * (M - X_B).t()); + metadata.map(grad) = M_mu_Omega + (1. - R) % (A - Y); + return objective; + }; + OptimizerResult result = minimize_objective_on_parameters(optimizer.get(), objective_and_grad, parameters); + + arma::mat M = metadata.copy(parameters.data()); + return Rcpp::List::create( + Rcpp::Named("status") = static_cast(result.status), + Rcpp::Named("iterations") = result.nb_iterations, + Rcpp::Named("M") = M); +} + +// [[Rcpp::export]] +Rcpp::List optim_zipln_S( + const arma::mat & init_S, // (n,p) + const arma::mat & O, // offsets (n, p) + const arma::mat & M, // (n,p) + const arma::mat & R, // (n,p) + const arma::mat & B, // (d,p) + const arma::vec & diag_Omega,// (p,1) + const Rcpp::List & configuration // List of config values ; xtol_abs is S2 only (double or mat) +) { + const auto metadata = tuple_metadata(init_S); + enum { S_ID }; // Names for metadata indexes + + auto parameters = std::vector(metadata.packed_size); + metadata.map(parameters.data()) = init_S; + + auto optimizer = new_nlopt_optimizer(configuration, parameters.size()); + if(configuration.containsElementNamed("xtol_abs")) { + SEXP value = configuration["xtol_abs"]; + if(Rcpp::is(value)) { + set_uniform_xtol_abs(optimizer.get(), Rcpp::as(value)); + } else { + auto packed = std::vector(metadata.packed_size); + metadata.map(packed.data()) = Rcpp::as(value); + set_per_value_xtol_abs(optimizer.get(), packed); + } + } + + const arma::mat O_M = O + M; + + // Optimize + auto objective_and_grad = [&metadata, &O_M, &R, &diag_Omega](const double * params, double * grad) -> double { + const arma::mat S = metadata.map(params); + + arma::uword n = S.n_rows; + arma::mat A = exp(O_M + 0.5 * S % S); // (n,p) + + // trace(1^T log(S)) == accu(log(S)). + // S_bar = diag(sum(S, 0)). trace(Omega * S_bar) = dot(diagvec(Omega), sum(S2, 0)) + double objective = trace((1. - R).t() * A) + 0.5 * dot(diag_Omega, sum(S % S, 0)) - 0.5 * accu(log(S % S)); + // S2^\emptyset interpreted as pow(S2, -1.) as that makes the most sense (gradient component for log(S2)) + // 1_n Diag(Omega)^T is n rows of diag(omega) values + metadata.map(grad) = S.each_row() % diag_Omega.t() + (1. - R) % S % A - pow(S, -1.) ; + return objective; + }; + OptimizerResult result = minimize_objective_on_parameters(optimizer.get(), objective_and_grad, parameters); + + arma::mat S = metadata.copy(parameters.data()); + return Rcpp::List::create( + Rcpp::Named("status") = static_cast(result.status), + Rcpp::Named("iterations") = result.nb_iterations, + Rcpp::Named("S") = S); +} diff --git a/src/utils.h b/src/utils.h index 39c90489..68d38a4d 100644 --- a/src/utils.h +++ b/src/utils.h @@ -16,3 +16,11 @@ inline arma::vec ki(arma::mat y) { arma::uword p = y.n_cols; return -logfact(std::move(y)) + 0.5 * double(p) ; } + +inline arma::mat logistic(arma::mat M) { + return arma::trunc_exp(M) % pow(1. + arma::trunc_exp(M), -1) ; +} + +inline arma::mat logit(arma::mat M) { + return arma::trunc_log(M) - arma::trunc_log(1 - M) ; +} From f92c78e35acda860e62305dec4c6204ac51e15d3 Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Wed, 3 Jan 2024 09:47:50 +0100 Subject: [PATCH 02/36] adding depencies to DESC. to pass check --- DESCRIPTION | 2 ++ 1 file changed, 2 insertions(+) diff --git a/DESCRIPTION b/DESCRIPTION index 09e3b79a..68214c4d 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -37,6 +37,8 @@ Imports: future.apply, R6, glassoFast, + pscl, + glmnet, Matrix, Rcpp, nloptr, From 5c422e55ce852df662521f4bf272a497aa0accbb Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Mon, 8 Jan 2024 13:30:41 +0100 Subject: [PATCH 03/36] preparing CRAN release --- NEWS.md | 2 +- cran-comments.md | 19 +++++++++++-------- inst/WORDLIST | 14 ++++++++++++++ inst/case_studies/oaks_tree.R | 8 ++++---- 4 files changed, 30 insertions(+), 13 deletions(-) diff --git a/NEWS.md b/NEWS.md index ed2dbb5a..e4f38366 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,4 +1,4 @@ -# PLNmodels 1.1.0 (2023-08-24) +# PLNmodels 1.1.0 (2024-01-08) * Update documentation of PLN*_param() functions to include torch optimization parameters * Add (somehow) explicit error message when torch convergence fails diff --git a/cran-comments.md b/cran-comments.md index c8cee98d..ae4ddf7a 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,7 +1,7 @@ Submitting PLNmodels version 1.1.0 to CRAN -Fix various bugs, update package documentation for controling optimization, better integration of the torch backend, better jackknife estimation for PLN. +Fix various bugs, update package documentation for controlling optimization, better integration of the torch backend, better jackknife estimation for PLN. ## Tested environments @@ -9,8 +9,8 @@ Fix various bugs, update package documentation for controling optimization, bett * tested remotely with win-builder - Windows Server 2022, R-devel, 64 bit - - Windows Server 2022, R-release, 64 bit - - Windows Server 2022, R-old, 64 bit + - Windows Server 2022, R-release (4.3.2), 64 bit + - Windows Server 2022, R-old (4.2.3), 64 bit * tested remotely with github-action - Linux Ubuntu 22.04, R-release @@ -23,12 +23,15 @@ all status OK except for * the usual NOTE about libs size (RcppArmadillo) +── R CMD check results ─────────────────────────── PLNmodels 1.1.0 ──── +Duration: 4m 32.7s + ❯ checking installed package size ... NOTE - installed size is 19.4Mb + installed size is 11.2Mb sub-directories of 1Mb or more: - doc 2.2Mb - libs 14.9Mb - -0 errors ✓ | 0 warnings ✓ | 1 note x + doc 2.0Mb + libs 7.8Mb + +0 errors ✔ | 0 warnings ✔ | 1 note ✖ R CMD check succeeded diff --git a/inst/WORDLIST b/inst/WORDLIST index 2e0dd5e2..6fe04b50 100644 --- a/inst/WORDLIST +++ b/inst/WORDLIST @@ -1,3 +1,4 @@ +ADAGRAD Anders Annales Archiv @@ -5,6 +6,7 @@ Aschan Auda BLAS BMC +Barebone Bournaud CAH CCSA @@ -47,6 +49,7 @@ ORCID OTU OTUs Okrah +Oshlack PLN PLNLDA PLNLDAfit @@ -71,6 +74,9 @@ Paulson PeerJ Polatera RLE +RMSPROP +RMSProp +RPROP Richardot Robject Roxygen @@ -78,6 +84,7 @@ Schwaller Slud StARS Supervized +TMM TSS Trichoptera Typological @@ -101,6 +108,7 @@ biom biomformat boldsymbol caddisflies +calcFactorTMM caughts citEntry cloneable @@ -119,6 +127,7 @@ distTObase distTOground distTOtrunk doi +edgeR emph espèces et @@ -154,6 +163,7 @@ les lm loadings loglik +lr lumineux lyon mathbb @@ -176,8 +186,11 @@ nlopt nloptr noLD normalisation +num +numepoch ofposterior optim +optimisation optimizers ot overset @@ -193,6 +206,7 @@ pln pmInfection poscounts pps +pseudocount pseudocounts qquad rcl diff --git a/inst/case_studies/oaks_tree.R b/inst/case_studies/oaks_tree.R index f105b962..12e45c8b 100644 --- a/inst/case_studies/oaks_tree.R +++ b/inst/case_studies/oaks_tree.R @@ -2,7 +2,7 @@ library(PLNmodels) library(factoextra) ## setting up future for parallelism -nb_cores <- 20 +nb_cores <- 10 options(future.fork.enable = TRUE) future::plan("multicore", workers = nb_cores) @@ -70,11 +70,11 @@ factoextra::fviz_pca_biplot( labs(col = "distance (cm)") + scale_color_viridis_c() ## Network inference with sparce covariance estimation -system.time(myPLNnets <- PLNnetwork(Abundance ~ 0 + tree + offset(log(Offset)), data = oaks, control = PLNnetwork_param(min_ratio = 0.1, penalize_diagonal = FALSE))) +system.time(myPLNnets <- PLNnetwork(Abundance ~ 0 + tree + offset(log(Offset)), data = oaks, control = PLNnetwork_param(min_ratio = 0.05))) plot(myPLNnets) plot(getBestModel(myPLNnets, "EBIC")) -stability_selection(myPLNnets) -plot(getBestModel(myPLNnets, "StARS", stability = .975)) +# stability_selection(myPLNnets) +# plot(getBestModel(myPLNnets, "StARS", stability = .975)) ## Mixture model to recover tree structure system.time(my_mixtures <- PLNmixture(Abundance ~ 1 + offset(log(Offset)), data = oaks, clusters = 1:5)) From 06ecda06628a137e2a894983b9cdbbba229642f1 Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Thu, 11 Jan 2024 13:35:53 +0100 Subject: [PATCH 04/36] more inclusive zi-pln fit --- NAMESPACE | 1 + R/PLNfit-class.R | 2 +- R/ZIPLN.R | 125 ++++++++++++++----------- R/ZIPLNfit-class.R | 225 +++++++++++++++++++++++++-------------------- R/utils.R | 14 ++- man/ZIPLN.Rd | 23 +---- man/ZIPLN_param.Rd | 48 ++++++++++ man/ZIPLNfit.Rd | 16 ---- 8 files changed, 262 insertions(+), 192 deletions(-) create mode 100644 man/ZIPLN_param.Rd diff --git a/NAMESPACE b/NAMESPACE index 90c918dc..03992aaa 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -46,6 +46,7 @@ export(PLNmixture_param) export(PLNnetwork) export(PLNnetwork_param) export(ZIPLN) +export(ZIPLN_param) export(coefficient_path) export(compute_PLN_starting_point) export(compute_offset) diff --git a/R/PLNfit-class.R b/R/PLNfit-class.R index 3426701d..9da0598e 100644 --- a/R/PLNfit-class.R +++ b/R/PLNfit-class.R @@ -907,7 +907,7 @@ PLNfit_fixedcov <- R6Class( initialize = function(responses, covariates, offsets, weights, formula, control) { super$initialize(responses, covariates, offsets, weights, formula, control) private$optimizer$main <- ifelse(control$backend == "nlopt", nlopt_optimize_fixed, private$torch_optimize) - ## ve step is the same as in the fullly parameterized covariance + ## ve step is the same as in the fully parameterized covariance private$Omega <- control$Omega }, #' @description Call to the NLopt or TORCH optimizer and update of the relevant fields diff --git a/R/ZIPLN.R b/R/ZIPLN.R index 12198c02..9f9ec218 100644 --- a/R/ZIPLN.R +++ b/R/ZIPLN.R @@ -2,28 +2,14 @@ #' #' Fit the multivariate Zero Inflated Poisson lognormal model with a variational algorithm. Use the (g)lm syntax for model specification (covariates, offsets, subset). #' -#' @param formula an object of class "formula": a symbolic description of the model to be fitted. See details -#' @param data an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which PLN is called. -#' @param subset an optional vector specifying a subset of observations to be used in the fitting process. +#' @inheritParams PLN +#' @param control a list-like structure for controlling the optimization, with default generated by [ZIPLN_param()]. See the associated documentation +#' for details. #' @param lambda a positive number controlling the level of sparsity in the regression parameters of the PLN component. Default to 0 (no sparsity). #' @param rho a positive number controlling the level of sparsity in the inverse covariance matrix. Default to 0 (no sparsity). -#' @param control a list for controlling the optimization. See details. #' #' @return an R6 object with class [`ZIPLNfit`] #' -#' @details The parameter `control` is a list controlling the optimization with the following entries: -#' * covariance a character setting the model for the covariance matrix: either "full", "diagonal", "spherical" or "sparse". Default is "full", automatically set to sparse if rho > 0. -#' * "trace" integer for verbosity. -#' * "maxiter_out" control outer optimization (VEM) maximum number of iterations in the variational E-M algorithm. Default is 100. -#' * "ftol_out" control outer optimization (VEM) stop when an full V-EM iteration changes the ELBO by less than ftol_out. Default is 1e-4. -#' * "ftol_rel" control inner optimization (VE step) stop when an optimization step changes in the objective function by less than ftol multiplied by the absolute value of the parameter. Default is 1e-6 when n < p, 1e-8 otherwise. -#' * "ftol_abs" control inner optimization (VE step) stop when an optimization step changes the objective function by less than ftol multiplied by the absolute value of the parameter. Default is 0 -#' * "xtol_rel" control inner optimization (VE step) stop when an optimization step changes every parameters by less than xtol multiplied by the absolute value of the parameter. Default is 1e-4 -#' * "xtol_abs" control inner optimization (VE step) stop when an optimization step changes every parameters by less than xtol multiplied by the absolute value of the parameter. Default is 0 -#' * "maxeval" control inner optimization (VE step) stop when the number of iteration exceeds maxeval. Default is 1000 -#' * "maxtime" control inner optimization (VE step) stop when the optimization time (in seconds) exceeds maxtime. Default is -1 (no restriction) -#' * "algorithm" control inner optimization (VE step): the optimization method used by NLOPT among LD type, i.e. "CCSAQ", "MMA", "LBFGS", "VAR1", "VAR2". See NLOPT documentation for further details. Default is "CCSAQ". -#' #' @rdname ZIPLN #' @include ZIPLNfit-class.R #' @examples @@ -38,33 +24,37 @@ #' @seealso The class [`ZIPLNfit`] #' @importFrom stats model.frame model.matrix model.response model.offset terms as.formula #' @export -ZIPLN <- function(formula, data, subset, rho = 0, lambda = 0, control = list()) { +ZIPLN <- function(formula, data, subset, rho = 0, lambda = 0, control = ZIPLN_param()) { ## extract the data matrices and weights args <- extract_model_zi(match.call(expand.dots = FALSE), parent.frame()) ## define default control parameters for optim and eventually overwrite them by user-defined parameters - ctrl <- ZIPLN_param(control, nrow(args$Y), ncol(args$Y)) - ctrl$lambda <- lambda - ctrl$rho <- rho - ctrl$ziparam <- args$ziparam - if (ctrl$rho > 0) ctrl$covariance <- "sparse" + control$lambda <- lambda + control$rho <- rho + control$ziparam <- args$ziparam + control$penalize_intercept <- FALSE + if (control$rho > 0) control$covariance <- "sparse" ## handling intercept term for penalized regression - if (attr(terms(as.formula(args$formula)), "intercept") >0 & !ctrl$penalize_intercept) - ctrl$ind_intercept <- match("(Intercept)", colnames(args$X)) + if (attr(terms(as.formula(args$formula)), "intercept") > 0 & !control$penalize_intercept) + control$ind_intercept <- match("(Intercept)", colnames(args$X)) ## initialization - if (ctrl$trace > 0) cat("\n Initialization...") - myPLN <- ZIPLNfit$new(args$Y, args$X, args$O, args$formula, args$xlevels, ctrl) + if (control$trace > 0) cat("\n Initialization...") + myPLN <- switch(control$covariance, + "diagonal" = ZIPLNfit_diagonal$new(args$Y, args$X, args$X0, args$O, args$w, args$formula, control), + "spherical" = ZIPLNfit_spherical$new(args$Y, args$X, args$X0, args$O, args$w, args$formula, control), + "fixed" = ZIPLNfit_fixedcov$new(args$Y, args$X, args$X0, args$O, args$w, args$formula, control), + ZIPLNfit$new(args$Y, args$X, args$X0, args$O, args$w, args$formula, control)) # default: full covariance ## optimization - if (ctrl$trace > 0) cat("\n Adjusting a ZI-PLN model with", - ctrl$covariance,"covariance model and", - ctrl$ziparam, "specific parameter(s) in Zero inflation component.") - myPLN$optimize(args$Y, args$X, args$O, ctrl) + if (control$trace > 0) cat("\n Adjusting a ZI-PLN model with", + control$covariance,"covariance model and", + control$ziparam, "specific parameter(s) in Zero inflation component.") + myPLN$optimize(args$Y, args$X, args$O, args$w, control$config_optim) - if (ctrl$trace > 0) cat("\n DONE!\n") + if (control$trace > 0) cat("\n DONE!\n") myPLN } @@ -73,24 +63,55 @@ ZIPLN <- function(formula, data, subset, rho = 0, lambda = 0, control = list()) available_algorithms <- c("MMA", "CCSAQ", "LBFGS", "VAR1", "VAR2", "TNEWTON", "TNEWTON_PRECOND", "TNEWTON_PRECOND_RESTART") -ZIPLN_param <- function(control, n, p) { - xtol_abs <- ifelse(is.null(control$xtol_abs) , 0 , control$xtol_abs) - ctrl <- list( - "ftol_out" = 1e-8, - "maxit_out" = 100 , - "algorithm" = "CCSAQ", - "maxeval" = 1000 , - "maxtime" = -1 , - "ftol_rel" = 1e-8 , - "ftol_abs" = 0, - "xtol_rel" = 1e-8, - "xtol_abs" = xtol_abs, - "trace" = 1, - "covariance" = "full", - "penalize_intercept" = FALSE - ) - ctrl[names(control)] <- control - stopifnot(ctrl$algorithm %in% available_algorithms) - ctrl -} +#' Control of a PLN fit +#' +#' Helper to define list of parameters to control the PLN fit. All arguments have defaults. +#' +#' @inheritParams PLN_param +#' @return list of parameters configuring the fit. +#' +#' @inherit PLN_param details +#' @details See [PLN_param()] for a full description of the generic optimization parameters. ZIPLN_param() also has two additional parameters controlling the optimization due +#' the inner-outer loop structure of the optimizer: +#' * "ftol_out" outer solver stops when an optimization step changes the objective function by less than xtol multiplied by the absolute value of the parameter. Default is 1e-8 +#' * "maxit_out" outer solver stops when the number of iteration exceeds maxit_out. Default is 100 +#' +#' @export +ZIPLN_param <- function( + backend = c("nlopt"), + trace = 1, + covariance = c("full", "diagonal", "spherical", "fixed"), + Omega = NULL, + config_post = list(), + config_optim = list(), + inception = NULL # pretrained ZIPLNfit used as initialization +) { + + covariance <- match.arg(covariance) + if (covariance == "fixed") stopifnot(inherits(Omega, "matrix") | inherits(Omega, "Matrix")) + if (!is.null(inception)) stopifnot(isZIPLNfit(inception)) + + ## post-treatment config + config_pst <- config_post_default_PLN + config_pst[names(config_post)] <- config_post + config_pst$trace <- trace + ## optimization config + stopifnot(backend %in% c("nlopt")) + stopifnot(config_optim$algorithm %in% available_algorithms_nlopt) + config_opt <- config_default_nlopt + config_opt$trace <- trace + config_opt$ftol_out <- 1e-6 + config_opt$maxit_out <- 100 + config_opt[names(config_optim)] <- config_optim + + structure(list( + backend = backend , + trace = trace , + covariance = covariance, + Omega = Omega , + config_post = config_pst, + config_optim = config_opt, + inception = inception), class = "PLNmodels_param") + +} diff --git a/R/ZIPLNfit-class.R b/R/ZIPLNfit-class.R index d84de0ed..3b69ced9 100644 --- a/R/ZIPLNfit-class.R +++ b/R/ZIPLNfit-class.R @@ -66,18 +66,18 @@ ZIPLNfit <- R6Class( #' @description Initialize a [`ZIPLNfit`] model #' @importFrom stats glm.fit residuals poisson fitted coef #' @importFrom pscl zeroinfl - initialize = function(responses, covariates, offsets, formula, xlevels, control) { + initialize = function(responses, covariates, covariates0, offsets, weights, formula, control) { ## problem dimensions n <- nrow(responses); p <- ncol(responses); d <- ncol(covariates) ## save the formula call as specified by the user private$formula <- formula - private$xlevels <- xlevels private$X <- covariates + private$X0 <- covariates0 ## initialize the covariance model private$covariance <- control$covariance private$ziparam <- control$ziparam - + R <- matrix(0, n, p) M <- matrix(0, n, p) B <- matrix(0, d, p) @@ -87,9 +87,9 @@ ZIPLNfit <- R6Class( y = responses[, j] if (min(y == 0)) { zip_out <- switch(control$ziparam, - "single" = pscl::zeroinfl(y ~ 0 + covariates | 1 , offset = offsets[, j]), - "row" = pscl::zeroinfl(y ~ 0 + covariates | 1:n, offset = offsets[, j]), - "col" = pscl::zeroinfl(y ~ 0 + covariates | 1 , offset = offsets[, j]), + "single" = pscl::zeroinfl(y ~ 0 + covariates | 1 , offset = offsets[, j]), + "row" = pscl::zeroinfl(y ~ 0 + covariates | 1:n, offset = offsets[, j]), + "col" = pscl::zeroinfl(y ~ 0 + covariates | 1 , offset = offsets[, j]), "covar" = pscl::zeroinfl(y ~ 0 + covariates , offset = offsets[, j])) # offset only for the count model B0[,j] <- coef(zip_out, "zero") B[,j] <- coef(zip_out, "count") @@ -103,51 +103,141 @@ ZIPLNfit <- R6Class( M[,j] <- residuals(p_out) + covariates %*% coef(p_out) } } - + ## Initialization of the ZI component private$R <- R - private$Pi <- switch(control$ziparam, + private$Pi <- switch(control$ziparam, "single" = matrix( mean(R), n, p) , "row" = matrix(rowMeans(R), n, p) , "col" = matrix(colMeans(R), n, p, byrow = TRUE), "covar" = R) private$B0 <- B0 - delta <- 1 * (responses == 0) - private$zeros <- delta - + private$zeros <- 1 * (responses == 0) + ## Initialization of the PLN component private$B <- B private$M <- M private$S <- matrix(.1, n, p) + + ## Link to functions performing the optimization + private$optimizer$main <- optimize_zi + private$optimizer$zi <- switch( + control$ziparam, + "single" = function(init_B0, X, R, config) list(Pi = matrix( mean(R), n, p) , B0 = matrix(NA, d, p)), + "row" = function(init_B0, X, R, config) list(Pi = matrix(rowMeans(R), n, p) , B0 = matrix(NA, d, p)), + "col" = function(init_B0, X, R, config) list(Pi = matrix(colMeans(R), n, p, byrow = TRUE), B0 = matrix(NA, d, p)), + "covar" = optim_zipln_zipar_covar + ) + private$optimizer$Omega <- optim_zipln_Omega_full + private$optimizer$B <- function(M, X, Omega, control) optim_zipln_B_dense(M, X) + # private$optimizer$vestep <- xxxx + }, #' @description Call to the Cpp optimizer and update of the relevant fields #' @param control a list for controlling the optimization. See details. - optimize = function(responses, covariates, offsets, control) { + optimize = function(responses, covariates, offsets, weights, control) { - args <- list(Y = responses, X = covariates, O = offsets, configuration = control) - args$init_parameters <- + data <- list(Y = responses, X = covariates, O = offsets) + parameters <- list(Omega = NA, B0 = private$B0, B = private$B, Pi = private$Pi, M = private$M, S = private$S, R = private$R) - optim_out <- do.call(optimize_zi, args) - + + # Main loop + nb_iter <- 0 + criterion <- vector("numeric", control$maxit_out) + vloglik <- -Inf; objective <- Inf + repeat { + + # Check maxeval + if(control$maxit_out >= 0 && nb_iter >= control$maxit_out) { + stop_reason = "maximum number of iterations reached" + criterion = criterion[1:nb_iter] + break + } + + # M Step + new_Omega <- private$optimizer$Omega( + M = parameters$M, X = data$X, B = parameters$B, S = parameters$S + ) + new_B <- private$optimizer$B( + M = parameters$M, X = data$X, Omega = new_Omega, control + ) + + optim_new_zipar <- private$optimizer$zi( + init_B0 = parameters$B0, X = data$X, R = parameters$R, config = control + ) + new_B0 <- optim_new_zipar$B0 + new_Pi <- optim_new_zipar$Pi + + # VE Step + new_R <- optim_zipln_R( + Y = data$Y, X = data$X, O = data$O, M = parameters$M, S = parameters$S, Pi = new_Pi + ) + optim_new_M <- optim_zipln_M( + init_M = parameters$M, + Y = data$Y, X = data$X, O = data$O, R = new_R, S = parameters$S, B = new_B, Omega = new_Omega, + configuration = control + ) + new_M <- optim_new_M$M + optim_new_S <- optim_zipln_S( + init_S = parameters$S, + O = data$O, M = new_M, R = new_R, B = new_B, diag_Omega = diag(new_Omega), + configuration = control + ) + new_S <- optim_new_S$S + + # Check convergence + new_parameters <- list( + Omega = new_Omega, B = new_B, B0 = new_B0, Pi = new_Pi, + R = new_R, M = new_M, S = new_S + ) + nb_iter <- nb_iter + 1 + + vloglik <- zipln_vloglik( + data$Y, data$X, data$O, new_Pi, new_Omega, new_B, new_R, new_M, new_S + ) + + criterion[nb_iter] <- new_objective <- -sum(vloglik) + + objective_converged <- + (objective - new_objective) <= control$ftol_out | + (objective - new_objective)/abs(new_objective) <= control$ftol_out + + parameters_converged <- parameter_list_converged( + parameters, new_parameters, + xtol_abs = control$xtol_abs, xtol_rel = control$xtol_rel + ) + + if (parameters_converged | objective_converged) { + parameters <- new_parameters + stop_reason <- "converged" + criterion <- criterion[1:nb_iter] + break + } + + parameters <- new_parameters + objective <- new_objective + } + self$update( - B = optim_out$parameters$B, - B0 = optim_out$parameters$B0, - Pi = optim_out$parameters$Pi, - Omega = optim_out$parameters$Omega, - M = optim_out$parameters$M, - S = optim_out$parameters$S, - R = optim_out$parameters$R, - Z = offsets + optim_out$parameters$M, - A = exp(offsets + optim_out$parameters$M + .5 * optim_out$parameters$S^2), - Ji = optim_out$vloglik, + B = parameters$B, + B0 = parameters$B0, + Pi = parameters$Pi, + Omega = parameters$Omega, + M = parameters$M, + S = parameters$S, + R = parameters$R, + Z = offsets + parameters$M, + A = exp(offsets + parameters$M + .5 * parameters$S^2), + Ji = vloglik, monitoring = list( - iterations = optim_out$nb_iter, - message = optim_out$stop_reason, - objective = optim_out$criterion) + iterations = nb_iter, + message = stop_reason, + objective = criterion) ) + ### TODO: Should be in post-treatment if (is.null(colnames(responses))) colnames(responses) <- paste0("Y", 1:self$p) colnames(private$B0) <- colnames(private$B) <- colnames(responses) rownames(private$B0) <- rownames(private$B) <- colnames(covariates) @@ -156,74 +246,6 @@ ZIPLNfit <- R6Class( }, - # #' @description Result of one call to the VE step of the optimization procedure: optimal variational parameters (M, S) and corresponding log likelihood values for fixed model parameters (Sigma, B). Intended to position new data in the latent space. - # #' @return A list with three components: - # #' * the matrix `M` of variational means, - # #' * the matrix `S2` of variational variances - # #' * the vector `log.lik` of (variational) log-likelihood of each new observation - # VEstep = function(covariates, offsets, responses, control = list()) { - # - # # problem dimension - # n <- nrow(responses); p <- ncol(responses); d <- ncol(covariates) - # - # ## define default control parameters for optim and overwrite by user defined parameters - # control$covariance <- self$vcov_model - # control <- ZIPLN_param(control, n, p) - # - # VEstep_optimizer <- - # switch(control$covariance, - # "spherical" = cpp_optimize_vestep_spherical, - # "diagonal" = cpp_optimize_vestep_diagonal, - # "full" = cpp_optimize_vestep_full, - # "genetic" = cpp_optimize_vestep_full - # ) - # - # ## Initialize the variational parameters with the appropriate new dimension of the data - # optim_out <- VEstep_optimizer( - # list(M = matrix(0, n, p), S = matrix(sqrt(0.1), n, p)), - # responses, - # covariates, - # offsets, - # weights, - # B = self$model_par$B, - # ## Robust inversion using Matrix::solve instead of solve.default - # Omega = as(Matrix::solve(Matrix::Matrix(self$model_par$Sigma)), 'matrix'), - # control - # ) - # - # Ji <- optim_out$loglik - # attr(Ji, "weights") <- weights - # - # ## output - # list(M = optim_out$M, - # S2 = (optim_out$S)**2, - # log.lik = setNames(Ji, rownames(responses))) - # }, - - # #' @description Predict position, scores or observations of new data. - # #' @param newdata A data frame in which to look for variables with which to predict. If omitted, the fitted values are used. - # #' @param type Scale used for the prediction. Either `link` (default, predicted positions in the latent space) or `response` (predicted counts). - # #' @param envir Environment in which the prediction is evaluated - # #' @return A matrix with predictions scores or counts. - # predict = function(newdata, type = c("link", "response"), envir = parent.frame()) { - # type = match.arg(type) - # - # ## Extract the model matrices from the new data set with initial formula - # X <- model.matrix(formula(private$formula)[-2], newdata, xlev = private$xlevels) - # O <- model.offset(model.frame(formula(private$formula)[-2], newdata)) - # - # ## mean latent positions in the parameter space - # EZ <- X %*% private$B - # if (!is.null(O)) EZ <- EZ + O - # EZ <- sweep(EZ, 2, .5 * diag(self$model_par$Sigma), "+") - # colnames(EZ) <- colnames(private$Sigma) - # - # results <- switch(type, link = EZ, response = exp(EZ)) - # - # attr(results, "type") <- type - # results - # }, - ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ## Print functions ----------------------- #' @description User friendly print method @@ -252,8 +274,8 @@ ZIPLNfit <- R6Class( ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% private = list( formula = NA, # the formula call for the model as specified by the user - xlevels = NA, # factor levels present in the original data, useful for predict() methods. - X = NA, # design matrix + X = NA, # design matrix for the PLN component + X0 = NA, # design matrix for the ZI component B = NA, # the model parameters for the covariable effect (PLN part) B0 = NA, # the model parameters for the covariate effects ('0'/Bernoulli part) Pi = NA, # the probability parameters for the '0'/Bernoulli part @@ -268,7 +290,8 @@ ZIPLNfit <- R6Class( Ji = NA, # element-wise approximated loglikelihood covariance = NA, # a string describing the covariance model ziparam = NA, # a string describing the ZI parametrisation - monitoring = NA # a list with optimization monitoring quantities + optimizer = list(), # list of links to the functions doing the optimization + monitoring = list() # list with optimization monitoring quantities ), ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ## ACTIVE BINDINGS ---- @@ -297,7 +320,7 @@ ZIPLNfit <- R6Class( "single" = 1, "row" = self$n, "col" = self$p, - "covar" = self$p * self$d) + + "covar" = self$p * self$d) + switch(private$covariance, "full" = self$p * (self$p + 1)/2, "diagonal" = self$p, diff --git a/R/utils.R b/R/utils.R index 838f6b66..0e0f5fd4 100644 --- a/R/utils.R +++ b/R/utils.R @@ -214,9 +214,17 @@ extract_model_zi <- function(call, envir, xlev = NULL) { if (is.null(O)) O <- matrix(0, nrow(Y), ncol(Y)) if (is.vector(O)) O <- O %o% rep(1, ncol(Y)) - ## Save encoutered levels for predict methods - xlevels <- .getXlevels(terms(frame), frame) - list(Y = Y, X = X, X0 = X0, O = O, formula = call$formula, ziparam = ziparam, xlevels = xlevels) + # Model weights + w <- model.weights(frame) + if (is.null(w)) { + w <- rep(1.0, nrow(Y)) + } else { + stopifnot(all(w > 0) && length(w) == nrow(Y)) + } + + ## Save encountered levels for predict methods as attribute of the formula + attr(call$formula, "xlevels") <- .getXlevels(terms(frame), frame) + list(Y = Y, X = X, X0 = X0, O = O, w = w, formula = call$formula, ziparam = ziparam) } edge_to_node <- function(x, n = max(x)) { diff --git a/man/ZIPLN.Rd b/man/ZIPLN.Rd index 5386886b..9f77949e 100644 --- a/man/ZIPLN.Rd +++ b/man/ZIPLN.Rd @@ -4,10 +4,10 @@ \alias{ZIPLN} \title{Zero Inflated Poisson lognormal model} \usage{ -ZIPLN(formula, data, subset, rho = 0, lambda = 0, control = list()) +ZIPLN(formula, data, subset, rho = 0, lambda = 0, control = ZIPLN_param()) } \arguments{ -\item{formula}{an object of class "formula": a symbolic description of the model to be fitted. See details} +\item{formula}{an object of class "formula": a symbolic description of the model to be fitted.} \item{data}{an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which PLN is called.} @@ -17,7 +17,8 @@ ZIPLN(formula, data, subset, rho = 0, lambda = 0, control = list()) \item{lambda}{a positive number controlling the level of sparsity in the regression parameters of the PLN component. Default to 0 (no sparsity).} -\item{control}{a list for controlling the optimization. See details.} +\item{control}{a list-like structure for controlling the optimization, with default generated by \code{\link[=ZIPLN_param]{ZIPLN_param()}}. See the associated documentation +for details.} } \value{ an R6 object with class \code{\link{ZIPLNfit}} @@ -25,22 +26,6 @@ an R6 object with class \code{\link{ZIPLNfit}} \description{ Fit the multivariate Zero Inflated Poisson lognormal model with a variational algorithm. Use the (g)lm syntax for model specification (covariates, offsets, subset). } -\details{ -The parameter \code{control} is a list controlling the optimization with the following entries: -\itemize{ -\item covariance a character setting the model for the covariance matrix: either "full", "diagonal", "spherical" or "sparse". Default is "full", automatically set to sparse if rho > 0. -\item "trace" integer for verbosity. -\item "maxiter_out" control outer optimization (VEM) maximum number of iterations in the variational E-M algorithm. Default is 100. -\item "ftol_out" control outer optimization (VEM) stop when an full V-EM iteration changes the ELBO by less than ftol_out. Default is 1e-4. -\item "ftol_rel" control inner optimization (VE step) stop when an optimization step changes in the objective function by less than ftol multiplied by the absolute value of the parameter. Default is 1e-6 when n < p, 1e-8 otherwise. -\item "ftol_abs" control inner optimization (VE step) stop when an optimization step changes the objective function by less than ftol multiplied by the absolute value of the parameter. Default is 0 -\item "xtol_rel" control inner optimization (VE step) stop when an optimization step changes every parameters by less than xtol multiplied by the absolute value of the parameter. Default is 1e-4 -\item "xtol_abs" control inner optimization (VE step) stop when an optimization step changes every parameters by less than xtol multiplied by the absolute value of the parameter. Default is 0 -\item "maxeval" control inner optimization (VE step) stop when the number of iteration exceeds maxeval. Default is 1000 -\item "maxtime" control inner optimization (VE step) stop when the optimization time (in seconds) exceeds maxtime. Default is -1 (no restriction) -\item "algorithm" control inner optimization (VE step): the optimization method used by NLOPT among LD type, i.e. "CCSAQ", "MMA", "LBFGS", "VAR1", "VAR2". See NLOPT documentation for further details. Default is "CCSAQ". -} -} \examples{ data(scRNA) # data subsample: only 100 random cell and the 50 most varying transcript diff --git a/man/ZIPLN_param.Rd b/man/ZIPLN_param.Rd new file mode 100644 index 00000000..97d4631d --- /dev/null +++ b/man/ZIPLN_param.Rd @@ -0,0 +1,48 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/ZIPLN.R +\name{ZIPLN_param} +\alias{ZIPLN_param} +\title{Control of a PLN fit} +\usage{ +ZIPLN_param( + backend = c("nlopt"), + trace = 1, + covariance = c("full", "diagonal", "spherical", "fixed"), + Omega = NULL, + config_post = list(), + config_optim = list(), + penalize_intercept = FALSE, + inception = NULL +) +} +\arguments{ +\item{backend}{optimization back used, either "nlopt" or "torch". Default is "nlopt"} + +\item{trace}{a integer for verbosity.} + +\item{covariance}{character setting the model for the covariance matrix. Either "full", "diagonal", "spherical" or "fixed". Default is "full".} + +\item{Omega}{precision matrix of the latent variables. Inverse of Sigma. Must be specified if \code{covariance} is "fixed"} + +\item{config_post}{a list for controlling the post-treatments (optional bootstrap, jackknife, R2, etc.). See details} + +\item{config_optim}{a list for controlling the optimizer (either "nlopt" or "torch" backend). See details} + +\item{inception}{Set up the parameters initialization: by default, the model is initialized with a multivariate linear model applied on +log-transformed data, and with the same formula as the one provided by the user. However, the user can provide a PLNfit (typically obtained from a previous fit), +which sometimes speeds up the inference.} +} +\value{ +list of parameters configuring the fit. +} +\description{ +Helper to define list of parameters to control the PLN fit. All arguments have defaults. +} +\details{ +See \code{\link[=PLN_param]{PLN_param()}} for a full description of the generic optimization parameters. ZIPLN_param() also has two additional parameters controlling the optimization due +the inner-outer loop structure of the optimizer: +\itemize{ +\item "ftol_out" outer solver stops when an optimization step changes the objective function by less than xtol multiplied by the absolute value of the parameter. Default is 1e-8 +\item "maxit_out" outer solver stops when the number of iteration exceeds maxit_out. Default is 100 +} +} diff --git a/man/ZIPLNfit.Rd b/man/ZIPLNfit.Rd index c5c9f576..497b1b87 100644 --- a/man/ZIPLNfit.Rd +++ b/man/ZIPLNfit.Rd @@ -11,22 +11,6 @@ See the documentation for \code{\link[=coef]{coef()}}, \code{\link[=sigma]{sigma Fields are accessed via active binding and cannot be changed by the user. } -\details{ -The parameter \code{control} is a list controlling the optimization with the following entries: -\itemize{ -\item covariance a character setting the model for the covariance matrix: either "full", "diagonal", "spherical" or "sparse". Default is "full", automatically set to sparse if rho > 0. -\item "trace" integer for verbosity. -\item "maxiter_out" control outer optimization (VEM) maximum number of iterations in the variational E-M algorithm. Default is 100. -\item "ftol_out" control outer optimization (VEM) stop when an full V-EM iteration changes the ELBO by less than ftol_out. Default is 1e-4. -\item "ftol_rel" control inner optimization (VE step) stop when an optimization step changes in the objective function by less than ftol multiplied by the absolute value of the parameter. Default is 1e-6 when n < p, 1e-8 otherwise. -\item "ftol_abs" control inner optimization (VE step) stop when an optimization step changes the objective function by less than ftol multiplied by the absolute value of the parameter. Default is 0 -\item "xtol_rel" control inner optimization (VE step) stop when an optimization step changes every parameters by less than xtol multiplied by the absolute value of the parameter. Default is 1e-4 -\item "xtol_abs" control inner optimization (VE step) stop when an optimization step changes every parameters by less than xtol multiplied by the absolute value of the parameter. Default is 0 -\item "maxeval" control inner optimization (VE step) stop when the number of iteration exceeds maxeval. Default is 1000 -\item "maxtime" control inner optimization (VE step) stop when the optimization time (in seconds) exceeds maxtime. Default is -1 (no restriction) -\item "algorithm" control inner optimization (VE step): the optimization method used by NLOPT among LD type, i.e. "CCSAQ", "MMA", "LBFGS", "VAR1", "VAR2". See NLOPT documentation for further details. Default is "CCSAQ". -} -} \examples{ \dontrun{ data(scRNA) From 84df01d47c7f746dca71ffa7eccdd26c8d637d10 Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Thu, 11 Jan 2024 18:22:02 +0100 Subject: [PATCH 05/36] advances in ZIPLN integration --- R/PLNfit-class.R | 4 +- R/ZIPLN.R | 10 +- R/ZIPLNfit-class.R | 233 ++++++++++++++++++++++++++++----- R/utils.R | 40 +++++- inst/case_studies/scRNA.R | 27 ++++ inst/simus_ZIPLN/essai_ZIPLN.R | 84 ++++++++++++ 6 files changed, 353 insertions(+), 45 deletions(-) create mode 100644 inst/case_studies/scRNA.R create mode 100644 inst/simus_ZIPLN/essai_ZIPLN.R diff --git a/R/PLNfit-class.R b/R/PLNfit-class.R index 9da0598e..cca92193 100644 --- a/R/PLNfit-class.R +++ b/R/PLNfit-class.R @@ -31,6 +31,7 @@ #' @rdname PLNfit #' @include PLNfit-class.R #' @importFrom R6 R6Class +#' @import torch #' #' @examples #' \dontrun{ @@ -96,7 +97,6 @@ PLNfit <- R6Class( Ji }, - #' @import torch torch_optimize = function(data, params, config) { #config$device = "mps" @@ -774,6 +774,7 @@ PLNfit_diagonal <- R6Class( attr(Ji, "weights") <- as.numeric(data$w) Ji } + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ## END OF TORCH METHODS ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @@ -854,6 +855,7 @@ PLNfit_spherical <- R6Class( attr(Ji, "weights") <- as.numeric(data$w) Ji } + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ## END OF TORCH METHODS FOR OPTIMIZATION ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% diff --git a/R/ZIPLN.R b/R/ZIPLN.R index 9f9ec218..41e100a0 100644 --- a/R/ZIPLN.R +++ b/R/ZIPLN.R @@ -43,16 +43,16 @@ ZIPLN <- function(formula, data, subset, rho = 0, lambda = 0, control = ZIPLN_pa ## initialization if (control$trace > 0) cat("\n Initialization...") myPLN <- switch(control$covariance, - "diagonal" = ZIPLNfit_diagonal$new(args$Y, args$X, args$X0, args$O, args$w, args$formula, control), - "spherical" = ZIPLNfit_spherical$new(args$Y, args$X, args$X0, args$O, args$w, args$formula, control), - "fixed" = ZIPLNfit_fixedcov$new(args$Y, args$X, args$X0, args$O, args$w, args$formula, control), - ZIPLNfit$new(args$Y, args$X, args$X0, args$O, args$w, args$formula, control)) # default: full covariance + "diagonal" = ZIPLNfit_diagonal$new(args$Y , list(PLN = args$X, ZI = args$X0), args$O, args$w, args$formula, control), + "spherical" = ZIPLNfit_spherical$new(args$Y, list(PLN = args$X, ZI = args$X0), args$O, args$w, args$formula, control), + "fixed" = ZIPLNfit_fixedcov$new(args$Y , list(PLN = args$X, ZI = args$X0), args$O, args$w, args$formula, control), + ZIPLNfit$new(args$Y, list(PLN = args$X, ZI = args$X0), args$O, args$w, args$formula, control)) # default: full covariance ## optimization if (control$trace > 0) cat("\n Adjusting a ZI-PLN model with", control$covariance,"covariance model and", control$ziparam, "specific parameter(s) in Zero inflation component.") - myPLN$optimize(args$Y, args$X, args$O, args$w, control$config_optim) + myPLN$optimize(args$Y, list(PLN = args$X, ZI = args$X0), args$O, args$w, control$config_optim) if (control$trace > 0) cat("\n DONE!\n") myPLN diff --git a/R/ZIPLNfit-class.R b/R/ZIPLNfit-class.R index 3b69ced9..00075a4c 100644 --- a/R/ZIPLNfit-class.R +++ b/R/ZIPLNfit-class.R @@ -12,7 +12,6 @@ #' @param offsets offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class #' @param formula model formula used for fitting, extracted from the formula in the upper-level call #' @param control a list for controlling the optimization. See details. -#' @param xlevels named listed of factor levels included in the models, extracted from the formula in the upper-level call and used for predictions. #' #' @inherit ZIPLN details #' @@ -66,41 +65,39 @@ ZIPLNfit <- R6Class( #' @description Initialize a [`ZIPLNfit`] model #' @importFrom stats glm.fit residuals poisson fitted coef #' @importFrom pscl zeroinfl - initialize = function(responses, covariates, covariates0, offsets, weights, formula, control) { + initialize = function(responses, covariates, offsets, weights, formula, control) { ## problem dimensions - n <- nrow(responses); p <- ncol(responses); d <- ncol(covariates) + n <- nrow(responses); p <- ncol(responses); d <- ncol(covariates$PLN); d0 <- ncol(covariates$ZI) ## save the formula call as specified by the user private$formula <- formula - private$X <- covariates - private$X0 <- covariates0 + private$X <- covariates$PLN ## initialize the covariance model private$covariance <- control$covariance private$ziparam <- control$ziparam R <- matrix(0, n, p) M <- matrix(0, n, p) - B <- matrix(0, d, p) - B0 <- matrix(0, d, p) - Pi <- matrix(0, n, p) + B <- matrix(0, d , p) + B0 <- matrix(0, d0, p) for (j in 1:p) { y = responses[, j] - if (min(y == 0)) { + if (min(y) == 0) { zip_out <- switch(control$ziparam, - "single" = pscl::zeroinfl(y ~ 0 + covariates | 1 , offset = offsets[, j]), - "row" = pscl::zeroinfl(y ~ 0 + covariates | 1:n, offset = offsets[, j]), - "col" = pscl::zeroinfl(y ~ 0 + covariates | 1 , offset = offsets[, j]), - "covar" = pscl::zeroinfl(y ~ 0 + covariates , offset = offsets[, j])) # offset only for the count model + "single" = pscl::zeroinfl(y ~ 0 + covariates$PLN | 1, offset = offsets[, j]), + "row" = pscl::zeroinfl(y ~ 0 + covariates$PLN | 1:n, offset = offsets[, j]), + "col" = pscl::zeroinfl(y ~ 0 + covariates$PLN | 1, offset = offsets[, j]), + "covar" = pscl::zeroinfl(y ~ 0 + covariates$PLN | covariates$ZI, offset = offsets[, j])) # offset only for the count model B0[,j] <- coef(zip_out, "zero") B[,j] <- coef(zip_out, "count") R[, j] <- predict(zip_out, type = "zero") - M[,j] <- residuals(zip_out, type = "pearson") + covariates %*% coef(zip_out, "count") + M[,j] <- residuals(zip_out) + covariates$PLN %*% coef(zip_out, "count") } else { - p_out <- glm(y ~ 0 + covariates, family = 'poisson', offset = offsets[, j]) + p_out <- glm(y ~ 0 + covariates$PLN, family = 'poisson', offset = offsets[, j]) B0[,j] <- rep(-10, d) B[,j] <- coef(p_out) R[, j] <- 0 - M[,j] <- residuals(p_out) + covariates %*% coef(p_out) + M[,j] <- residuals(p_out) + covariates$PLN %*% coef(p_out) } } @@ -121,15 +118,15 @@ ZIPLNfit <- R6Class( ## Link to functions performing the optimization private$optimizer$main <- optimize_zi - private$optimizer$zi <- switch( + private$optimizer$B <- function(M, X, Omega, control) optim_zipln_B_dense(M, X) + private$optimizer$zi <- switch( control$ziparam, - "single" = function(init_B0, X, R, config) list(Pi = matrix( mean(R), n, p) , B0 = matrix(NA, d, p)), - "row" = function(init_B0, X, R, config) list(Pi = matrix(rowMeans(R), n, p) , B0 = matrix(NA, d, p)), - "col" = function(init_B0, X, R, config) list(Pi = matrix(colMeans(R), n, p, byrow = TRUE), B0 = matrix(NA, d, p)), + "single" = function(init_B0, X, R, config) list(Pi = matrix( mean(R), n, p) , B0 = matrix(NA, d0, p)), + "row" = function(init_B0, X, R, config) list(Pi = matrix(rowMeans(R), n, p) , B0 = matrix(NA, d0, p)), + "col" = function(init_B0, X, R, config) list(Pi = matrix(colMeans(R), n, p, byrow = TRUE), B0 = matrix(NA, d0, p)), "covar" = optim_zipln_zipar_covar ) private$optimizer$Omega <- optim_zipln_Omega_full - private$optimizer$B <- function(M, X, Omega, control) optim_zipln_B_dense(M, X) # private$optimizer$vestep <- xxxx }, @@ -138,7 +135,7 @@ ZIPLNfit <- R6Class( #' @param control a list for controlling the optimization. See details. optimize = function(responses, covariates, offsets, weights, control) { - data <- list(Y = responses, X = covariates, O = offsets) + data <- list(Y = responses, X = covariates$PLN, X0 = covariates$ZI, O = offsets) parameters <- list(Omega = NA, B0 = private$B0, B = private$B, Pi = private$Pi, M = private$M, S = private$S, R = private$R) @@ -150,9 +147,9 @@ ZIPLNfit <- R6Class( repeat { # Check maxeval - if(control$maxit_out >= 0 && nb_iter >= control$maxit_out) { - stop_reason = "maximum number of iterations reached" - criterion = criterion[1:nb_iter] + if (control$maxit_out >= 0 && nb_iter >= control$maxit_out) { + stop_reason <- "maximum number of iterations reached" + criterion <- criterion[1:nb_iter] break } @@ -301,6 +298,8 @@ ZIPLNfit <- R6Class( n = function() {nrow(private$M)}, #' @field p number of species p = function() {ncol(private$M)}, + #' @field q number of dimensions of the latent space + q = function() {ncol(private$M)}, #' @field d number of covariates d = function() {nrow(private$B)}, #' @field latent a matrix: values of the latent vector (Z in the model) @@ -310,26 +309,24 @@ ZIPLNfit <- R6Class( #' @field model_par a list with the matrices of parameters found in the model (B, Sigma, plus some others depending on the variant) model_par = function() {list(B = private$B, B0 = private$B0, Pi = private$Pi, Omega = private$Omega)}, #' @field var_par a list with two matrices, M and S2, which are the estimated parameters in the variational approximation - var_par = function() {list(M = private$M, S2 = private$S^2, R = private$R)}, + var_par = function() {list(M = private$M, S2 = private$S^2, S = private$S, R = private$R)}, #' @field fitted a matrix: fitted values of the observations (A in the model) fitted = function() {private$R * private$zeros + (1 - private$R) * private$A}, #' @field nb_param number of parameters in the current PLN model nb_param = function() { - res <- self$p * self$d + - switch(private$ziparam, + as.integer( + self$p * self$d + self$p * (self$p + 1)/2 + + switch(private$ziparam, "single" = 1, "row" = self$n, "col" = self$p, - "covar" = self$p * self$d) + - switch(private$covariance, - "full" = self$p * (self$p + 1)/2, - "diagonal" = self$p, - "spherical" = 1, - "sparse" = (sum(private$Omega != 0) - self$p)/2L) - as.integer(res) + "covar" = self$p * self$d) + ) }, #' @field vcov_model character: the model used for the covariance (either "spherical", "diagonal" or "full") vcov_model = function() {private$covariance}, + #' @field zi_model character: the model used for the zero inflation (either "single", "row", "col" or "covar") + zi_model = function() {private$ziparam}, #' @field optim_par a list with parameters useful for monitoring the optimization optim_par = function() {private$monitoring}, #' @field loglik (weighted) variational lower bound of the loglikelihood @@ -351,8 +348,172 @@ ZIPLNfit <- R6Class( ) ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - ## END OF THE CLASS ---- + ## END OF THE CLASS ZIPLNfit ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ) +## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +## CLASS ZIPLNfit_diagonal ############################ +## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +#' An R6 Class to represent a ZIPLNfit in a standard, general framework, with diagonal residual covariance +#' +#' @param responses the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class +#' @param covariates design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class +#' @param offsets offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class +#' @param weights an optional vector of observation weights to be used in the fitting process. +#' @param formula model formula used for fitting, extracted from the formula in the upper-level call +#' @param control a list for controlling the optimization. See details. +#' +#' @rdname ZIPLNfit_diagonal +#' @importFrom R6 R6Class +#' +#' @examples +#' \dontrun{ +#' data(trichoptera) +#' trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) +#' myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(covariance = "diagonal")) +#' class(myPLN) +#' print(myPLN) +#' } +ZIPLNfit_diagonal <- R6Class( + classname = "ZIPLNfit_diagonal", + inherit = ZIPLNfit, + public = list( + #' @description Initialize a [`PLNfit`] model + initialize = function(responses, covariates, offsets, weights, formula, control) { + super$initialize(responses, covariates, offsets, weights, formula, control) + private$optimizer$Omega <- optim_zipln_Omega_diagonal + } + ), + active = list( + #' @field nb_param number of parameters in the current PLN model + nb_param = function() { + res <- self$p * self$d + self$p + + switch(private$ziparam, + "single" = 1, + "row" = self$n, + "col" = self$p, + "covar" = self$p * self$d) + as.integer(res) + }, + #' @field vcov_model character: the model used for the residual covariance + vcov_model = function() {"diagonal"} + ) + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + ## END OF THE CLASS ZIPLNfit_diagonal + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +) + +## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +## CLASS ZIPLNfit_spherical ########################## +## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +#' An R6 Class to represent a ZIPLNfit in a standard, general framework, with spherical residual covariance +#' +#' @param responses the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class +#' @param covariates design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class +#' @param offsets offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class +#' @param weights an optional vector of observation weights to be used in the fitting process. +#' @param formula model formula used for fitting, extracted from the formula in the upper-level call +#' @param control a list for controlling the optimization. See details. +#' +#' @rdname ZIPLNfit_spherical +#' @importFrom R6 R6Class +#' +#' @examples +#' \dontrun{ +#' data(trichoptera) +#' trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) +#' myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(covariance = "spherical)) +#' class(myPLN) +#' print(myPLN) +#' } +ZIPLNfit_spherical <- R6Class( + classname = "ZIPLNfit_spherical", + inherit = ZIPLNfit, + public = list( + #' @description Initialize a [`PLNfit`] model + initialize = function(responses, covariates, offsets, weights, formula, control) { + super$initialize(responses, covariates, offsets, weights, formula, control) + private$optimizer$Omega <- optim_zipln_Omega_spherical + } + ), + active = list( + #' @field nb_param number of parameters in the current PLN model + nb_param = function() { + res <- self$p * self$d + 1 + + switch(private$ziparam, + "single" = 1, + "row" = self$n, + "col" = self$p, + "covar" = self$p * self$d) + as.integer(res) + }, + #' @field vcov_model character: the model used for the residual covariance + vcov_model = function() {"spherical"} + ) + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + ## END OF THE CLASS PLNfit_spherical + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +) + +## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +## CLASS ZIPLNfit_fixedcov ############################# +## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +#' An R6 Class to represent a ZIPLNfit in a standard, general framework, with fixed (inverse) residual covariance +#' +#' @param responses the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class +#' @param covariates design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class +#' @param offsets offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class +#' @param data an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which PLN is called. +#' @param weights an optional vector of observation weights to be used in the fitting process. +#' @param nullModel null model used for approximate R2 computations. Defaults to a GLM model with same design matrix but not latent variable. +#' @param formula model formula used for fitting, extracted from the formula in the upper-level call +#' @param control a list for controlling the optimization. See details. +#' @param config part of the \code{control} argument which configures the optimizer +#' +#' @rdname ZIPLNfit_fixedcov +#' @importFrom R6 R6Class +#' +#' @examples +#' \dontrun{ +#' data(trichoptera) +#' trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) +#' myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera) +#' class(myPLN) +#' print(myPLN) +#' } +ZIPLNfit_fixedcov <- R6Class( + classname = "ZIPLNfit_fixedcov", + inherit = ZIPLNfit, + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + ## PUBLIC MEMBERS ---- + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + public = list( + #' @description Initialize a [`ZIPLNfit_fixedcov`] model + initialize = function(responses, covariates, offsets, weights, formula, control) { + super$initialize(responses, covariates, offsets, weights, formula, control) + private$Omega <- control$Omega + } + ), + active = list( + #' @field nb_param number of parameters in the current PLN model + nb_param = function() { + res <- self$p * self$d + + switch(private$ziparam, + "single" = 1, + "row" = self$n, + "col" = self$p, + "covar" = self$p * self$d) + as.integer(res) + }, + #' @field vcov_model character: the model used for the residual covariance + vcov_model = function() {"fixed"} + ) + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + ## END OF THE CLASS ZIPLNfit_fixedcov + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +) diff --git a/R/utils.R b/R/utils.R index 0e0f5fd4..bcca8a3b 100644 --- a/R/utils.R +++ b/R/utils.R @@ -207,7 +207,7 @@ extract_model_zi <- function(call, envir, xlev = NULL) { ## Extract the design matrices for ZI and PLN components X <- model.matrix(tt_pln, frame, xlev = xlev$pln) - X0 <- switch(ziparam, "covar" = model.matrix(tt_zi, frame, xlev = xlev$zi), NULL) + X0 <- switch(ziparam, "covar" = model.matrix(tt_zi, frame, xlev = xlev$zi), matrix(NA,0,0)) # Offsets are only considered for the PLN component O <- model.offset(frame) @@ -222,8 +222,6 @@ extract_model_zi <- function(call, envir, xlev = NULL) { stopifnot(all(w > 0) && length(w) == nrow(Y)) } - ## Save encountered levels for predict methods as attribute of the formula - attr(call$formula, "xlevels") <- .getXlevels(terms(frame), frame) list(Y = Y, X = X, X0 = X0, O = O, w = w, formula = call$formula, ziparam = ziparam) } @@ -351,3 +349,39 @@ compute_PLN_starting_point <- function(Y, X, O, w, s = 0.1) { M = matrix(fits$residuals, n, p), S = matrix(s, n, p)) } + +#' Helper function for PLN initialization. +#' +#' @description +#' Barebone function to compute starting points for B, M and S when fitting a PLN. Mostly intended for internal use. +#' +#' @param Y Response count matrix +#' @param X Covariate matrix +#' @param O Offset matrix (in log-scale) +#' @param w Weight vector (defaults to 1) +#' @param s Scale parameter for S (defaults to 0.1) +#' @return a named list of starting values for model parameter B and variational parameters M and S used in the iterative optimization algorithm of [PLN()] +#' +#' @details The default strategy to estimate B and M is to fit a linear model with covariates `X` to the response count matrix (after adding a pseudocount of 1, scaling by the offset and taking the log). The regression matrix is used to initialize `B` and the residuals to initialize `M`. `S` is initialized as a constant conformable matrix with value `s`. +#' +#' @rdname compute_ZIPLN_starting_point +#' @examples +#' \dontrun{ +#' data(barents) +#' Y <- barents$Abundance +#' X <- model.matrix(Abundance ~ Latitude + Longitude + Depth + Temperature, data = barents) +#' O <- log(barents$Offset) +#' w <-- rep(1, nrow(Y)) +#' compute_ZIPLN_starting_point(Y, X, O, w) +#' } +#' +#' @importFrom stats lm.fit +#' @export +compute_ZIPLN_starting_point <- function(Y, X, X0, O, w, s = 0.1) { + # Y = responses, X = covariates, O = offsets (in log scale), w = weights + n <- nrow(Y); p <- ncol(Y); d <- ncol(X) + fits <- lm.fit(w * X, w * log((1 + Y)/exp(O))) + list(B = matrix(fits$coefficients, d, p), + M = matrix(fits$residuals, n, p), + S = matrix(s, n, p)) +} diff --git a/inst/case_studies/scRNA.R b/inst/case_studies/scRNA.R new file mode 100644 index 00000000..ddb89b2d --- /dev/null +++ b/inst/case_studies/scRNA.R @@ -0,0 +1,27 @@ +library(PLNmodels) +library(factoextra) + +data(scRNA) +# data subsample: only 500 random cell and the 200 most varying transcript +scRNA <- scRNA[sample.int(nrow(scRNA), 500), ] +scRNA$counts <- scRNA$counts[, 1:200] +myZIPLN <- ZIPLN(counts ~ 1 + offset(log(total_counts)), data = scRNA) +myPLN <- PLN(counts ~ 1 + offset(log(total_counts)), data = scRNA) + +data.frame( + fitted = c(as.vector(fitted(myZIPLN)), as.vector(fitted(myPLN))), + observed = rep(as.vector(scRNA$counts), 2), + method = factor(rep(c("ZIPLN", "PLN"), each = length(scRNA$counts))) +) %>% + ggplot(aes(x = observed, y = fitted)) + + geom_point(size = .5, alpha =.25 ) + + facet_wrap( ~ method) + + scale_x_log10() + + scale_y_log10() + + theme_bw() + annotation_logticks() + +prcomp(myZIPLN$latent) %>% factoextra::fviz_pca_ind(axes = c(1,2), col.ind = scRNA$cell_line) +prcomp(myZIPLN$latent_pos) %>% factoextra::fviz_pca_ind(axes = c(1,2), col.ind = scRNA$cell_line) + +prcomp(myPLN$latent) %>% factoextra::fviz_pca_ind(axes = c(1,2), col.ind = scRNA$cell_line) +prcomp(myPLN$latent_pos) %>% factoextra::fviz_pca_ind(axes = c(1,2), col.ind = scRNA$cell_line) diff --git a/inst/simus_ZIPLN/essai_ZIPLN.R b/inst/simus_ZIPLN/essai_ZIPLN.R new file mode 100644 index 00000000..eac35ae4 --- /dev/null +++ b/inst/simus_ZIPLN/essai_ZIPLN.R @@ -0,0 +1,84 @@ +library(PLNmodels) +library(MASS) +library(tidyverse) +library(parallel) + +rZIPLN <- function(n = 10, + mu = rep(0, ncol(Sigma)), + Sigma = diag(1, 5, 5), + Pi = matrix(1, n,, ncol(Sigma)), + depths = rep(1e4, n)) { + p <- ncol(Sigma) + if (any(is.vector(mu), ncol(mu) == 1)) { + mu <- matrix(rep(mu, n), ncol = p, byrow = TRUE) + } + if (length(depths) != n) { + depths <- rep(depths[1], n) + } + ## adjust depths + exp_depths <- rowSums(exp(rep(1, n) %o% diag(Sigma)/2 + mu)) ## sample-wise expected depths + offsets <- log(depths %o% rep(1, p)) - log(exp_depths) + Z <- mu + mvrnorm(n, rep(0, ncol(Sigma)), as.matrix(Sigma)) + offsets + W <- matrix(rbinom(n * p, 1, prob = Pi), n, p) + Y <- matrix(rpois(n * p, as.vector(exp(Z))), n, p) * (1 - W) + dimnames(Y) <- list(paste0("S", 1:n), paste0("Y", 1:p)) + Y +} + +### SIMULATED DATA +p <- 5 +logit <- function(x) log(x/(1-x)) +logistic <- function(x) 1 / (1 + exp(-x)) + +### Simulation parameters +## Sigma: AR/toeplitz structure +sd <- sqrt(sample.int(5, p, replace = TRUE)) +Sigma_star <- diag(sd) %*% toeplitz(0.5^(1:p - 1)) %*% diag(sd) +Omega_star <- solve(Sigma_star) +## PLN part: one intercept/grand mean with value equal to 1 +B_star <- matrix(2, 1, p) +## ZI part: proba of 0.1 for being a 0 from the ZI component +B0_star <- matrix(logit(0.2), 1, p) + +vec_n <- c(25, 50, 75, 100) +one_simu <- function(i) { + cat(i) + n <- max(vec_n)*2 + # X <- cbind(rep(1, n)) + X <- rnorm(n, 2, 1) + mu_star <- cbind(X) %*% B_star + Pi_star <- logistic(cbind(X) %*% B0_star) + Y <- rZIPLN(n, mu = mu_star, Sigma = Sigma_star, Pi = Pi_star) + Y <- Y[rowSums(Y) > 0, ] + X <- X[rowSums(Y) > 0] + err_ZIPLN <- sapply(vec_n, function(n_) { + Y_ <- Y[1:n_, ]; X_ <- X[1:n_] + myZIPLN <- ZIPLN(Y_ ~ 1 + X_ + offset(log(rowSums(Y_))), control = ZIPLN_param(trace = 0)) + c(pred_Y = sqrt(mean((myZIPLN$fitted - Y_)^2)), + rmse_B = sqrt(mean((myZIPLN$model_par$B[2, ] - B_star)^2)), + rmse_Omega = sqrt(mean((myZIPLN$model_par$Omega - Omega_star)^2))) + }) + err_PLN <- sapply(vec_n, function(n_) { + Y_ <- Y[1:n_, ]; X_ <- X[1:n_] + myPLN <- PLN(Y_ ~ 1 + X_ + offset(log(rowSums(Y_))) , control = PLN_param(trace = 0)) + c(pred = sqrt(mean((myPLN$fitted - Y_)^2)), + rmse_B = sqrt(mean((myPLN$model_par$B[2, ] - B_star)^2)), + rmse_Sigma = sqrt(mean((myPLN$model_par$Omega - Omega_star)^2)) + ) + }) + data.frame(rbind(t(err_ZIPLN), t(err_PLN)), + method = rep(c("ZIPLN","PLN"), each = length(vec_n)), + n = c(vec_n, vec_n), simu = rep(i, 2*length(vec_n))) +} + +res <- do.call(rbind, lapply(1:50, one_simu)) + +p <- ggplot(res) + aes(x = factor(n), y = pred_Y, fill = factor(method)) + geom_violin() + theme_bw() + + scale_y_log10() + ylim(c(0,2)) +p + +p <- ggplot(res) + aes(x = factor(n), y = rmse_B, fill = factor(method)) + geom_violin() + theme_bw() + scale_y_log10() + ylim(c(0,3)) +p + +p <- ggplot(res) + aes(x = factor(n), y = rmse_Omega, fill = factor(method)) + geom_violin() + theme_bw() + scale_y_log10() + ylim(c(0,0.5)) +p From 65c1239096b6652a2d6fe377160b3a1d14d97354 Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Fri, 12 Jan 2024 16:45:21 +0100 Subject: [PATCH 06/36] zipln: row, co and single running --- NAMESPACE | 1 + R/ZIPLN.R | 15 ++++++++------- R/ZIPLNfit-class.R | 14 ++++++++------ R/utils.R | 8 ++++---- inst/simus_ZIPLN/essai_ZIPLN.R | 2 +- man/ZIPLN.Rd | 16 ++++++++++++++-- man/ZIPLN_param.Rd | 1 - man/ZIPLNfit.Rd | 10 ++++++---- 8 files changed, 42 insertions(+), 25 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 03992aaa..238c5ff5 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -49,6 +49,7 @@ export(ZIPLN) export(ZIPLN_param) export(coefficient_path) export(compute_PLN_starting_point) +export(compute_ZIPLN_starting_point) export(compute_offset) export(extract_probs) export(getBestModel) diff --git a/R/ZIPLN.R b/R/ZIPLN.R index 41e100a0..dd2d7ff7 100644 --- a/R/ZIPLN.R +++ b/R/ZIPLN.R @@ -5,8 +5,9 @@ #' @inheritParams PLN #' @param control a list-like structure for controlling the optimization, with default generated by [ZIPLN_param()]. See the associated documentation #' for details. -#' @param lambda a positive number controlling the level of sparsity in the regression parameters of the PLN component. Default to 0 (no sparsity). -#' @param rho a positive number controlling the level of sparsity in the inverse covariance matrix. Default to 0 (no sparsity). +#' @param zi a character describing the model for zero inflation in "single" (one global parameter, the default), "col" (one parameter per variable) +#' and "row" (one parameter per individuals). If covariates are specified in the formula RHS, after bar pipe like "~ PLN effect | ZI effect", `zi` +#' is ignored and the covariates extracted by model matrix will be used to account for the ZI effect. #' #' @return an R6 object with class [`ZIPLNfit`] #' @@ -17,22 +18,22 @@ #' # data subsample: only 100 random cell and the 50 most varying transcript #' scRNA <- scRNA[sample.int(nrow(scRNA), 100), ] #' scRNA$counts <- scRNA$counts[, 1:50] -#' myPLN_full <- ZIPLN(counts ~ 1 + cell_line + offset(log(total_counts)) | 1, data = scRNA) +#' myPLN_full <- ZIPLN(counts ~ 1 + cell_line + offset(log(total_counts)), data = scRNA) #' myPLN_sparse <- ZIPLN(counts ~ 1 + offset(log(total_counts)), rho = .5, data = scRNA) #' myPLN_full$criteria # better BIC with sparse version #' myPLN_sparse$criteria #' @seealso The class [`ZIPLNfit`] #' @importFrom stats model.frame model.matrix model.response model.offset terms as.formula #' @export -ZIPLN <- function(formula, data, subset, rho = 0, lambda = 0, control = ZIPLN_param()) { +ZIPLN <- function(formula, data, subset, zi = c("single", "row", "col"), control = ZIPLN_param()) { ## extract the data matrices and weights args <- extract_model_zi(match.call(expand.dots = FALSE), parent.frame()) ## define default control parameters for optim and eventually overwrite them by user-defined parameters - control$lambda <- lambda - control$rho <- rho - control$ziparam <- args$ziparam + control$lambda <- 0 + control$rho <- 0 + control$ziparam <- ifelse((args$zicovar), "covar", match.arg(zi)) control$penalize_intercept <- FALSE if (control$rho > 0) control$covariance <- "sparse" diff --git a/R/ZIPLNfit-class.R b/R/ZIPLNfit-class.R index 00075a4c..2dfc0f66 100644 --- a/R/ZIPLNfit-class.R +++ b/R/ZIPLNfit-class.R @@ -22,7 +22,8 @@ #' data(scRNA) #' # data subsample: only 100 random cell and the 50 most varying transcript #' subset <- sample.int(nrow(scRNA), 100) -#' myPLN <- ZIPLN(counts[, 1:50] ~ 1 + offset(log(total_counts)), subset = subset, data = scRNA) +#' myZIPLN_1 <- ZIPLN(counts[, 1:50] ~ 1 + offset(log(total_counts)), zi = "single", subset = subset, data = scRNA) +#' myZIPLN_2 <- ZIPLN(counts[, 1:50] ~ 1 + offset(log(total_counts)), zi = "row", subset = subset, data = scRNA) #' } ZIPLNfit <- R6Class( classname = "ZIPLNfit", @@ -83,11 +84,12 @@ ZIPLNfit <- R6Class( for (j in 1:p) { y = responses[, j] if (min(y) == 0) { - zip_out <- switch(control$ziparam, - "single" = pscl::zeroinfl(y ~ 0 + covariates$PLN | 1, offset = offsets[, j]), - "row" = pscl::zeroinfl(y ~ 0 + covariates$PLN | 1:n, offset = offsets[, j]), - "col" = pscl::zeroinfl(y ~ 0 + covariates$PLN | 1, offset = offsets[, j]), - "covar" = pscl::zeroinfl(y ~ 0 + covariates$PLN | covariates$ZI, offset = offsets[, j])) # offset only for the count model + suppressWarnings( + zip_out <- switch(control$ziparam, + "row" = pscl::zeroinfl(y ~ 0 + covariates$PLN | 0 + factor(1:n), offset = offsets[, j]), + "covar" = pscl::zeroinfl(y ~ 0 + covariates$PLN | covariates$ZI , offset = offsets[, j]), + pscl::zeroinfl(y ~ 0 + covariates$PLN | 1, offset = offsets[, j])) # offset only for the count model + ) B0[,j] <- coef(zip_out, "zero") B[,j] <- coef(zip_out, "count") R[, j] <- predict(zip_out, type = "zero") diff --git a/R/utils.R b/R/utils.R index bcca8a3b..9666254e 100644 --- a/R/utils.R +++ b/R/utils.R @@ -184,19 +184,19 @@ extract_model_zi <- function(call, envir, xlev = NULL) { ## Check if a ZI specific formula has been if (length(ff[[3]]) > 1 && identical(ff[[3]][[1]], as.name("|"))) { + zicovar <- TRUE ff_zi <- ~. ; ff_zi[[3]] <- ff[[3]][[3]] ; ff_zi[[2]] <- NULL ff_pln <- ~. ; ff_pln[[3]] <- ff[[3]][[2]] ; ff_pln[[2]] <- NULL tt_zi <- terms(ff_zi) ; attr(tt_zi , "offset") <- NULL tt_pln <- terms(ff_pln) ; attr(tt_pln, "offset") <- NULL formula[[3]][1] <- call("+") - ziparam <- "covar" if (tt_zi[[2]] == "row") {ziparam <- "row"; formula[[3]][[3]] <- NULL} if (tt_zi[[2]] == "col") {ziparam <- "col"; formula[[3]][[3]] <- NULL} call_frame$formula <- formula } else { ff_pln <- ff tt_pln <- terms(ff_pln) ; attr(tt_pln, "offset") <- NULL - ziparam <- "single" + zicovar <- FALSE } ## eval the call in the parent environment @@ -207,7 +207,7 @@ extract_model_zi <- function(call, envir, xlev = NULL) { ## Extract the design matrices for ZI and PLN components X <- model.matrix(tt_pln, frame, xlev = xlev$pln) - X0 <- switch(ziparam, "covar" = model.matrix(tt_zi, frame, xlev = xlev$zi), matrix(NA,0,0)) + if (zicovar) X0 <- model.matrix(tt_zi, frame, xlev = xlev$zi) else X0 <- matrix(NA,0,0) # Offsets are only considered for the PLN component O <- model.offset(frame) @@ -222,7 +222,7 @@ extract_model_zi <- function(call, envir, xlev = NULL) { stopifnot(all(w > 0) && length(w) == nrow(Y)) } - list(Y = Y, X = X, X0 = X0, O = O, w = w, formula = call$formula, ziparam = ziparam) + list(Y = Y, X = X, X0 = X0, O = O, w = w, formula = call$formula, zicovar = zicovar) } edge_to_node <- function(x, n = max(x)) { diff --git a/inst/simus_ZIPLN/essai_ZIPLN.R b/inst/simus_ZIPLN/essai_ZIPLN.R index eac35ae4..6396d640 100644 --- a/inst/simus_ZIPLN/essai_ZIPLN.R +++ b/inst/simus_ZIPLN/essai_ZIPLN.R @@ -53,7 +53,7 @@ one_simu <- function(i) { X <- X[rowSums(Y) > 0] err_ZIPLN <- sapply(vec_n, function(n_) { Y_ <- Y[1:n_, ]; X_ <- X[1:n_] - myZIPLN <- ZIPLN(Y_ ~ 1 + X_ + offset(log(rowSums(Y_))), control = ZIPLN_param(trace = 0)) + myZIPLN <- ZIPLN(Y_ ~ 0 + X_ + offset(log(rowSums(Y_))) | X_ , control = ZIPLN_param(trace = 0)) c(pred_Y = sqrt(mean((myZIPLN$fitted - Y_)^2)), rmse_B = sqrt(mean((myZIPLN$model_par$B[2, ] - B_star)^2)), rmse_Omega = sqrt(mean((myZIPLN$model_par$Omega - Omega_star)^2))) diff --git a/man/ZIPLN.Rd b/man/ZIPLN.Rd index 9f77949e..1c686615 100644 --- a/man/ZIPLN.Rd +++ b/man/ZIPLN.Rd @@ -4,7 +4,15 @@ \alias{ZIPLN} \title{Zero Inflated Poisson lognormal model} \usage{ -ZIPLN(formula, data, subset, rho = 0, lambda = 0, control = ZIPLN_param()) +ZIPLN( + formula, + data, + subset, + zi = c("single", "row", "col"), + rho = 0, + lambda = 0, + control = ZIPLN_param() +) } \arguments{ \item{formula}{an object of class "formula": a symbolic description of the model to be fitted.} @@ -13,6 +21,10 @@ ZIPLN(formula, data, subset, rho = 0, lambda = 0, control = ZIPLN_param()) \item{subset}{an optional vector specifying a subset of observations to be used in the fitting process.} +\item{zi}{a character describing the model for zero inflation in "single" (one global parameter, the default), "col" (one parameter per variable) +and "row" (one parameter per individuals). If covariates are specified in the formula RHS, after bar pipe like "~ PLN effect | ZI effect", \code{zi} +is ignored and the covariates extracted by model matrix will be used to account for the ZI effect.} + \item{rho}{a positive number controlling the level of sparsity in the inverse covariance matrix. Default to 0 (no sparsity).} \item{lambda}{a positive number controlling the level of sparsity in the regression parameters of the PLN component. Default to 0 (no sparsity).} @@ -31,7 +43,7 @@ data(scRNA) # data subsample: only 100 random cell and the 50 most varying transcript scRNA <- scRNA[sample.int(nrow(scRNA), 100), ] scRNA$counts <- scRNA$counts[, 1:50] -myPLN_full <- ZIPLN(counts ~ 1 + cell_line + offset(log(total_counts)) | 1, data = scRNA) +myPLN_full <- ZIPLN(counts ~ 1 + cell_line + offset(log(total_counts)), data = scRNA) myPLN_sparse <- ZIPLN(counts ~ 1 + offset(log(total_counts)), rho = .5, data = scRNA) myPLN_full$criteria # better BIC with sparse version myPLN_sparse$criteria diff --git a/man/ZIPLN_param.Rd b/man/ZIPLN_param.Rd index 97d4631d..1324d776 100644 --- a/man/ZIPLN_param.Rd +++ b/man/ZIPLN_param.Rd @@ -11,7 +11,6 @@ ZIPLN_param( Omega = NULL, config_post = list(), config_optim = list(), - penalize_intercept = FALSE, inception = NULL ) } diff --git a/man/ZIPLNfit.Rd b/man/ZIPLNfit.Rd index 497b1b87..abce45cd 100644 --- a/man/ZIPLNfit.Rd +++ b/man/ZIPLNfit.Rd @@ -26,6 +26,8 @@ myPLN <- ZIPLN(counts[, 1:50] ~ 1 + offset(log(total_counts)), subset = subset, \item{\code{p}}{number of species} +\item{\code{q}}{number of dimensions of the latent space} + \item{\code{d}}{number of covariates} \item{\code{latent}}{a matrix: values of the latent vector (Z in the model)} @@ -42,6 +44,8 @@ myPLN <- ZIPLN(counts[, 1:50] ~ 1 + offset(log(total_counts)), subset = subset, \item{\code{vcov_model}}{character: the model used for the covariance (either "spherical", "diagonal" or "full")} +\item{\code{zi_model}}{character: the model used for the zero inflation (either "single", "row", "col" or "covar")} + \item{\code{optim_par}}{a list with parameters useful for monitoring the optimization} \item{\code{loglik}}{(weighted) variational lower bound of the loglikelihood} @@ -134,7 +138,7 @@ Update the current \code{\link{ZIPLNfit}} object \subsection{Method \code{new()}}{ Initialize a \code{\link{ZIPLNfit}} model \subsection{Usage}{ -\if{html}{\out{
}}\preformatted{ZIPLNfit$new(responses, covariates, offsets, formula, xlevels, control)}\if{html}{\out{
}} +\if{html}{\out{
}}\preformatted{ZIPLNfit$new(responses, covariates, offsets, weights, formula, control)}\if{html}{\out{
}} } \subsection{Arguments}{ @@ -148,8 +152,6 @@ Initialize a \code{\link{ZIPLNfit}} model \item{\code{formula}}{model formula used for fitting, extracted from the formula in the upper-level call} -\item{\code{xlevels}}{named listed of factor levels included in the models, extracted from the formula in the upper-level call and used for predictions.} - \item{\code{control}}{a list for controlling the optimization. See details.} } \if{html}{\out{}} @@ -161,7 +163,7 @@ Initialize a \code{\link{ZIPLNfit}} model \subsection{Method \code{optimize()}}{ Call to the Cpp optimizer and update of the relevant fields \subsection{Usage}{ -\if{html}{\out{
}}\preformatted{ZIPLNfit$optimize(responses, covariates, offsets, control)}\if{html}{\out{
}} +\if{html}{\out{
}}\preformatted{ZIPLNfit$optimize(responses, covariates, offsets, weights, control)}\if{html}{\out{
}} } \subsection{Arguments}{ From 1c717b8f1ad4a58b42a59cb8b8d2e694d75139e8 Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Mon, 15 Jan 2024 21:35:41 +0100 Subject: [PATCH 07/36] fixing coef method in ZI --- R/ZIPLNfits-S3methods.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/ZIPLNfits-S3methods.R b/R/ZIPLNfits-S3methods.R index 18b80155..1abbc8be 100644 --- a/R/ZIPLNfits-S3methods.R +++ b/R/ZIPLNfits-S3methods.R @@ -48,8 +48,8 @@ isZIPLNfit <- function(Robject) {inherits(Robject, "ZIPLNfit")} coef.ZIPLNfit <- function(object, type = c("mainPLN", "mainZI", "precision"), ...) { stopifnot(isZIPLNfit(object)) switch(match.arg(type), - mainPLN = object$model_par$Theta, - mainZI = object$model_par$Theta0, + mainPLN = object$model_par$B, + mainZI = object$model_par$B0, precision = object$model_par$Omega) } From e0c6f543c5b647e747543ffc005afa1bc5541171 Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Mon, 15 Jan 2024 21:35:57 +0100 Subject: [PATCH 08/36] start testing ZI PLN --- tests/testthat/test-zipln.R | 128 ++++++++++++++++++++++++ tests/testthat/test-ziplnfit.R | 173 +++++++++++++++++++++++++++++++++ 2 files changed, 301 insertions(+) create mode 100644 tests/testthat/test-zipln.R create mode 100644 tests/testthat/test-ziplnfit.R diff --git a/tests/testthat/test-zipln.R b/tests/testthat/test-zipln.R new file mode 100644 index 00000000..0de02852 --- /dev/null +++ b/tests/testthat/test-zipln.R @@ -0,0 +1,128 @@ +context("test-pln") +require(purrr) + +data(trichoptera) +trichoptera <- prepare_data(trichoptera$Abundance[1:20, 1:5], trichoptera$Covariate[1:20, ]) + +test_that("ZIPLN: Check that ZIPLN is running and robust", { + + expect_is(ZIPLN(Abundance ~ 1, data = trichoptera), "ZIPLNfit") + + expect_is(ZIPLN(Abundance ~ 0, data = trichoptera), "ZIPLNfit") + + expect_is(ZIPLN(trichoptera$Abundance ~ 1), "ZIPLNfit") + + expect_equal(ZIPLN(trichoptera$Abundance ~ 1 + trichoptera$Wind)$fitted, + ZIPLN(Abundance ~ Wind, data = trichoptera)$fitted) +}) + +test_that("PLN: Routine comparison between the different covariance models", { + model_full <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(covariance = "full" , trace = 0)) + model_diagonal <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(covariance = "diagonal" , trace = 0)) + model_spherical <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(covariance = "spherical", trace = 0)) + expect_gte(model_full$loglik , model_diagonal$loglik) + expect_gte(model_diagonal$loglik, model_spherical$loglik) +}) + +test_that("PLN is working with a single variable data matrix", { + Y <- matrix(rpois(10, exp(0.5)), ncol = 1) + colnames(Y) <- "Y" + expect_is(ZIPLN(Y ~ 1), "ZIPLNfit") + + Y <- matrix(rpois(10, exp(0.5)), ncol = 1) + expect_is(ZIPLN(Y ~ 1), "ZIPLNfit") +}) + +test_that("PLN is working with unnamed data matrix", { + n = 10; d = 3; p = 10 + Y <- matrix(rpois(n*p, 1), n, p) + X <- matrix(rnorm(n*d), n, d) + expect_is(ZIPLN(Y ~ X), "ZIPLNfit") +}) + + test_that("ZIPLN is working with different optimization algorithm in NLopt", { + + MMA <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(config_optim = list(algorithm = "MMA"))) + CCSAQ <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(config_optim = list(algorithm = "CCSAQ"))) + LBFGS <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(config_optim = list(algorithm = "LBFGS"))) + + expect_equal(MMA$loglik, CCSAQ$loglik, tolerance = 1e-1) ## Almost equivalent, CCSAQ faster + + expect_error(ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(config_optim = list(algorithm = "nawak")))) + }) + +test_that("ZIPLN: Check that univariate ZIPLN models works, with matrix of numeric format", { + expect_no_error(uniZIPLN <- ZIPLN(Abundance[,1,drop=FALSE] ~ 1, data = trichoptera)) + expect_no_error(uniZIPLN <- ZIPLN(Abundance[,1] ~ 1, data = trichoptera)) + y <- trichoptera$Abundance[,1] + expect_no_error(uniZIPLN <- PLN(y ~ 1)) +}) + +test_that("ZIPLN: Check that all univariate ZIPLN models are equivalent with the multivariate diagonal case", { + + p <- ncol(trichoptera$Abundance) + Offset <- trichoptera$Offset + + univariate_full <- lapply(1:p, function(j) { + Abundance <- trichoptera$Abundance[, j, drop = FALSE] + ZIPLN(Abundance ~ 1 + offset(log(Offset)), control = ZIPLN_param(trace = 0)) + }) + + univariate_diagonal <- lapply(1:p, function(j) { + Abundance <- trichoptera$Abundance[, j, drop = FALSE] + ZIPLN(Abundance ~ 1 + offset(log(Offset)), control = ZIPLN_param(covariance = "diagonal", trace = 0)) + }) + + univariate_spherical <- lapply(1:p, function(j) { + Abundance <- trichoptera$Abundance[, j, drop = FALSE] + ZIPLN(Abundance ~ 1 + offset(log(Offset)), control = ZIPLN_param(covariance = "spherical", trace = 0)) + }) + + multivariate_diagonal <- + ZIPLN(Abundance ~ 1 + offset(log(Offset)), data = trichoptera, control = ZIPLN_param(covariance = "diagonal", trace = 0)) + + expect_true(all.equal( + map_dbl(univariate_spherical, "nb_param"), + map_dbl(univariate_full , "nb_param") + )) + + expect_true(all.equal( + map_dbl(univariate_spherical, "nb_param"), + map_dbl(univariate_diagonal , "nb_param") + )) + expect_true(all.equal( + map_dbl(univariate_full , "nb_param"), + map_dbl(univariate_diagonal , "nb_param") + )) + + expect_true(all.equal( + map_dbl(univariate_full, "loglik") %>% sum(), + multivariate_diagonal$loglik, tolerance = 1e-2) + ) + + expect_true(all.equal( + map_dbl(univariate_diagonal, "loglik") %>% sum(), + multivariate_diagonal$loglik, tolerance = 1e-2) + ) + + expect_true(all.equal( + map_dbl(univariate_spherical, "loglik") %>% sum(), + multivariate_diagonal$loglik, tolerance = 1e-2) + ) + + expect_true(all.equal( + map(univariate_spherical, sigma) %>% map_dbl(as.double), + map(univariate_diagonal , sigma) %>% map_dbl(as.double), tolerance = .25 + )) + + expect_true(all.equal( + map(univariate_spherical, sigma) %>% map_dbl(as.double), + map(univariate_full , sigma) %>% map_dbl(as.double), tolerance = .25 + )) + + expect_true(all.equal( + map(univariate_diagonal, sigma) %>% map_dbl(as.double), + map(univariate_full , sigma) %>% map_dbl(as.double), tolerance = .25 + )) + +}) diff --git a/tests/testthat/test-ziplnfit.R b/tests/testthat/test-ziplnfit.R new file mode 100644 index 00000000..ad89f54f --- /dev/null +++ b/tests/testthat/test-ziplnfit.R @@ -0,0 +1,173 @@ +context("test-plnfit") + +data(trichoptera) +trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) + +test_that("ZIPLN fit: check classes, getters and field access", { + + expect_output(model <- ZIPLN(Abundance ~ 1, data = trichoptera, + control = ZIPLN_param(trace = 1)), +" + Initialization... + Adjusting a ZI-PLN model with full covariance model and single specific parameter(s) in Zero inflation component. + DONE!" + ) + + expect_is(model, "ZIPLNfit") + + expect_equal(model$n, nrow(trichoptera$Abundance)) + expect_equal(model$p, ncol(trichoptera$Abundance)) + expect_equal(model$d, 1) + + ## S3 methods: values + expect_equal(coef(model), model$model_par$B) + expect_equal(coef(model, type = "covariance"), sigma(model)) + expect_equal(sigma(model), model$model_par$Sigma) + # expect_equal(vcov(model), model$vcov_coef) + + ## S3 methods: class + expect_true(inherits(coef(model), "matrix")) + expect_true(inherits(sigma(model), "matrix")) + # expect_true(inherits(vcov(model), "dsCMatrix")) + + ## S3 methods: dimensions + ## expect_equal(dim(vcov(model)), c(model$d * model$p, model$d * model$p)) + + ## R6 bindings + expect_is(model$latent, "matrix") + expect_true(is.numeric(model$latent)) + expect_equal(dim(model$latent), c(model$n, model$p)) + +}) + +test_that("PLN fit: check print message", { + + expect_output(model <- PLN(Abundance ~ 1, data = trichoptera)) + + output <- paste( +"A multivariate Poisson Lognormal fit with full covariance model. +==================================================================", +capture_output(print(as.data.frame(round(model$criteria, digits = 3), row.names = ""))), +"================================================================== +* Useful fields + $model_par, $latent, $latent_pos, $var_par, $optim_par + $loglik, $BIC, $ICL, $loglik_vec, $nb_param, $criteria +* Useful S3 methods + print(), coef(), sigma(), vcov(), fitted() + predict(), predict_cond(), standard_error()", + sep = "\n") + + expect_output(model$show(), + output, + fixed = TRUE) + ## show and print are equivalent + expect_equal(capture_output(model$show()), + capture_output(model$print())) +}) + +test_that("PLN fit: Check prediction", { + + model1 <- PLN(Abundance ~ 1, data = trichoptera, subset = 1:30) + model1_off <- PLN(Abundance ~ 1 + offset(log(Offset)), data = trichoptera, subset = 1:30) + model2 <- PLN(Abundance ~ Pressure, data = trichoptera, subset = 1:30) + + newdata <- trichoptera[31:49, ] + # newdata$Abundance <- NULL + + pred1 <- predict(model1, newdata = newdata, type = "response") + pred1_off <- predict(model1_off, newdata = newdata, type = "response") + pred2 <- predict(model2, newdata = newdata, type = "response") + pred2_ve <- predict(model2, newdata = newdata, type = "response", + responses = newdata$Abundance) + + ## predict returns fitted values if no data is provided + expect_equal(model2$predict(), model2$fitted) + + ## Adding covariates improves fit + expect_gt( + mean((newdata$Abundance - pred1)^2), + mean((newdata$Abundance - pred2)^2) + ) + + ## Doing one VE step improves fit + expect_gt( + mean((newdata$Abundance - pred2)^2), + mean((newdata$Abundance - pred2_ve)^2) + ) + + ## R6 methods + ## with offset, predictions should vary across samples + expect_gte(min(apply(pred1_off, 2, sd)), .Machine$double.eps) + newdata$Offset <- NULL + ## without offsets, predictions should be the same for all samples + expect_equal(unname(apply(pred1, 2, sd)), rep(0, ncol(pred1))) + + ## Unequal factor levels in train and prediction datasets + suppressWarnings( + toy_data <- prepare_data( + counts = matrix(c(1, 3, 1, 1), ncol = 1), + covariates = data.frame(Cov = c("A", "B", "A", "A")), + offset = rep(1, 4)) + ) + model <- PLN(Abundance ~ Cov + offset(log(Offset)), data = toy_data[1:2,]) + expect_length(predict(model, newdata = toy_data[3:4, ], type = "r"), 2L) +}) + + + +test_that("PLN fit: Check conditional prediction", { + + n_cond = 10 + p_cond = 2 + p <- ncol(trichoptera$Abundance) + + myPLN <- PLN(Abundance ~ Temperature, trichoptera) + Yc <- trichoptera$Abundance[1:n_cond, 1:p_cond, drop=FALSE] + + newX <- data.frame(1, Temperature = trichoptera$Temperature[1:n_cond]) + + pred <- predict_cond(myPLN, newX, Yc, type = "response") + + # check dimensions of the predictions (#TODO: modify pred$pred if we decide not to return M,S) + expect_equal(dim(pred), c(n_cond,p-p_cond)) + + # check if the RMSE of conditional predictions are greater than the marginal ones + expect_gt( + mean((trichoptera$Abundance[1:n_cond, (p_cond+1):p] - + predict(myPLN, newdata = newX, type = "response")[1:n_cond, (p_cond+1):p])^2), + mean((trichoptera$Abundance[1:n_cond, (p_cond+1):p] - pred)^2) + ) + + # check the dimension of the variational parameters when sent back + pred <- predict_cond(myPLN, newX, Yc, type = "response", var_par = TRUE) + expect_equal(dim(attr(pred, "M")), dim(pred)) + expect_equal(dim(attr(pred, "S")), c(p-p_cond, p-p_cond, n_cond)) + +}) + +test_that("PLN fit: Check number of parameters", { + + p <- ncol(trichoptera$Abundance) + + model <- PLN(Abundance ~ 1, data = trichoptera) + expect_equal(model$nb_param, p*(p+1)/2 + p * 1) + + model <- PLN(Abundance ~ 1 + Wind, data = trichoptera) + expect_equal(model$nb_param, p*(p+1)/2 + p * 2) + + model <- PLN(Abundance ~ Group + 0 , data = trichoptera) + expect_equal(model$nb_param, p*(p+1)/2 + p * nlevels(trichoptera$Group)) + + modelS <- PLN(Abundance ~ 1, data = trichoptera, control = PLN_param(covariance = "spherical")) + expect_equal(modelS$nb_param, 1 + p * 1) + expect_equal(modelS$vcov_model, "spherical") + + modelD <- PLN(Abundance ~ 1, data = trichoptera, control = PLN_param(covariance = "diagonal")) + expect_equal(modelD$nb_param, p + p * 1) + expect_equal(modelD$vcov_model, "diagonal") + + model <- PLN(Abundance ~ 1, data = trichoptera, control = PLN_param(covariance = "fixed", Omega = as.matrix(modelD$model_par$Omega))) + expect_equal(model$nb_param, 0 + p * 1) + expect_equal(model$vcov_model, "fixed") + +}) From 718e747a5db7102e5735fc2e25674244df2c7d42 Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Mon, 15 Jan 2024 22:20:21 +0100 Subject: [PATCH 09/36] S3 methods for ZIPLN --- R/ZIPLNfit-class.R | 26 ++++--- R/ZIPLNfits-S3methods.R | 15 ++-- man/ZIPLN.Rd | 6 -- man/ZIPLNfit.Rd | 5 +- man/ZIPLNfit_diagonal.Rd | 100 ++++++++++++++++++++++++++ man/ZIPLNfit_fixedcov.Rd | 100 ++++++++++++++++++++++++++ man/ZIPLNfit_spherical.Rd | 91 ++++++++++++++++++++++++ man/coef.ZIPLNfit.Rd | 6 +- man/compute_ZIPLN_starting_point.Rd | 39 ++++++++++ tests/testthat/test-ziplnfit.R | 106 +++++++--------------------- 10 files changed, 388 insertions(+), 106 deletions(-) create mode 100644 man/ZIPLNfit_diagonal.Rd create mode 100644 man/ZIPLNfit_fixedcov.Rd create mode 100644 man/ZIPLNfit_spherical.Rd create mode 100644 man/compute_ZIPLN_starting_point.Rd diff --git a/R/ZIPLNfit-class.R b/R/ZIPLNfit-class.R index 2dfc0f66..296b35b6 100644 --- a/R/ZIPLNfit-class.R +++ b/R/ZIPLNfit-class.R @@ -144,14 +144,17 @@ ZIPLNfit <- R6Class( # Main loop nb_iter <- 0 - criterion <- vector("numeric", control$maxit_out) + criterion <- numeric(control$maxit_out) + convergence <- numeric(control$maxit_out) + vloglik <- -Inf; objective <- Inf repeat { # Check maxeval if (control$maxit_out >= 0 && nb_iter >= control$maxit_out) { stop_reason <- "maximum number of iterations reached" - criterion <- criterion[1:nb_iter] + criterion <- criterion[1:nb_iter] + convergence <- convergence[nb_iter] break } @@ -198,6 +201,7 @@ ZIPLNfit <- R6Class( ) criterion[nb_iter] <- new_objective <- -sum(vloglik) + convergence[nb_iter] <- abs(new_objective - objective)/abs(new_objective) objective_converged <- (objective - new_objective) <= control$ftol_out | @@ -211,7 +215,8 @@ ZIPLNfit <- R6Class( if (parameters_converged | objective_converged) { parameters <- new_parameters stop_reason <- "converged" - criterion <- criterion[1:nb_iter] + criterion <- criterion[1:nb_iter] + convergence <- convergence[1:nb_iter] break } @@ -224,6 +229,7 @@ ZIPLNfit <- R6Class( B0 = parameters$B0, Pi = parameters$Pi, Omega = parameters$Omega, + Sigma = solve(parameters$Omega), M = parameters$M, S = parameters$S, R = parameters$R, @@ -231,9 +237,10 @@ ZIPLNfit <- R6Class( A = exp(offsets + parameters$M + .5 * parameters$S^2), Ji = vloglik, monitoring = list( - iterations = nb_iter, - message = stop_reason, - objective = criterion) + iterations = nb_iter, + message = stop_reason, + objective = criterion, + convergence = convergence) ) ### TODO: Should be in post-treatment @@ -241,6 +248,7 @@ ZIPLNfit <- R6Class( colnames(private$B0) <- colnames(private$B) <- colnames(responses) rownames(private$B0) <- rownames(private$B) <- colnames(covariates) rownames(private$Omega) <- colnames(private$Omega) <- colnames(private$Pi) <- colnames(responses) + dimnames(private$Sigma) <- dimnames(private$Omega) rownames(private$M) <- rownames(private$S) <- rownames(private$R) <- rownames(private$Pi) <- rownames(responses) }, @@ -279,6 +287,7 @@ ZIPLNfit <- R6Class( B0 = NA, # the model parameters for the covariate effects ('0'/Bernoulli part) Pi = NA, # the probability parameters for the '0'/Bernoulli part Omega = NA, # the precision matrix + Sigma = NA, # the covariance matrix S = NA, # the variational parameters for the variances M = NA, # the variational parameters for the means Z = NA, # the matrix of latent variable @@ -309,7 +318,7 @@ ZIPLNfit <- R6Class( #' @field latent_pos a matrix: values of the latent position vector (Z) without covariates effects or offset latent_pos = function() {private$M - private$X %*% private$B}, #' @field model_par a list with the matrices of parameters found in the model (B, Sigma, plus some others depending on the variant) - model_par = function() {list(B = private$B, B0 = private$B0, Pi = private$Pi, Omega = private$Omega)}, + model_par = function() {list(B = private$B, B0 = private$B0, Pi = private$Pi, Omega = private$Omega, Sigma = private$Sigma)}, #' @field var_par a list with two matrices, M and S2, which are the estimated parameters in the variational approximation var_par = function() {list(M = private$M, S2 = private$S^2, S = private$S, R = private$R)}, #' @field fitted a matrix: fitted values of the observations (A in the model) @@ -325,7 +334,7 @@ ZIPLNfit <- R6Class( "covar" = self$p * self$d) ) }, - #' @field vcov_model character: the model used for the covariance (either "spherical", "diagonal" or "full") + #' @field vcov_model character: the model used for the covariance (either "spherical", "diagonal", "full" or "sparse") vcov_model = function() {private$covariance}, #' @field zi_model character: the model used for the zero inflation (either "single", "row", "col" or "covar") zi_model = function() {private$ziparam}, @@ -499,6 +508,7 @@ ZIPLNfit_fixedcov <- R6Class( initialize = function(responses, covariates, offsets, weights, formula, control) { super$initialize(responses, covariates, offsets, weights, formula, control) private$Omega <- control$Omega +### TODO handled fixed cov } ), active = list( diff --git a/R/ZIPLNfits-S3methods.R b/R/ZIPLNfits-S3methods.R index 1abbc8be..4cef0874 100644 --- a/R/ZIPLNfits-S3methods.R +++ b/R/ZIPLNfits-S3methods.R @@ -30,8 +30,8 @@ isZIPLNfit <- function(Robject) {inherits(Robject, "ZIPLNfit")} #' @name coef.ZIPLNfit #' #' @param object an R6 object with class [`ZIPLNfit`] -#' @param type type of parameter that should be extracted. Either "mainPLN" (default) for \deqn{\Theta}, -#' "mainZI" (default) for \deqn{\Theta0} or "precision" for \deqn{\Omega} +#' @param type type of parameter that should be extracted. Either "mainPLN" (default) for \deqn{B}, +#' "mainZI" (default) for \deqn{B0}, "precision" for \deqn{\Omega}, "covariance" for \deqn{\Sigma} #' @param ... additional parameters for S3 compatibility. Not used #' @return A matrix of coefficients extracted from the ZIPLNfit model. #' @@ -45,12 +45,13 @@ isZIPLNfit <- function(Robject) {inherits(Robject, "ZIPLNfit")} #' myPLN <- ZIPLN(counts[, 1:50] ~ 1 + offset(log(total_counts)), subset = subset, data = scRNA) #' #' @export -coef.ZIPLNfit <- function(object, type = c("mainPLN", "mainZI", "precision"), ...) { +coef.ZIPLNfit <- function(object, type = c("mainPLN", "mainZI", "precision", "covariance"), ...) { stopifnot(isZIPLNfit(object)) switch(match.arg(type), - mainPLN = object$model_par$B, - mainZI = object$model_par$B0, - precision = object$model_par$Omega) + mainPLN = object$model_par$B, + mainZI = object$model_par$B0, + precision = object$model_par$Omega, + covariance = object$model_par$Sigma) } #' Extracts model fitted values from objects returned by [ZIPLN()] and its variants @@ -81,6 +82,6 @@ fitted.ZIPLNfit <- function(object, ...) { #' @importFrom stats sigma sigma.ZIPLNfit <- function(object, ...) { stopifnot(isZIPLNfit(object)) - solve(object$model_par$Omega) + object$model_par$Sigma } diff --git a/man/ZIPLN.Rd b/man/ZIPLN.Rd index 1c686615..026e54be 100644 --- a/man/ZIPLN.Rd +++ b/man/ZIPLN.Rd @@ -9,8 +9,6 @@ ZIPLN( data, subset, zi = c("single", "row", "col"), - rho = 0, - lambda = 0, control = ZIPLN_param() ) } @@ -25,10 +23,6 @@ ZIPLN( and "row" (one parameter per individuals). If covariates are specified in the formula RHS, after bar pipe like "~ PLN effect | ZI effect", \code{zi} is ignored and the covariates extracted by model matrix will be used to account for the ZI effect.} -\item{rho}{a positive number controlling the level of sparsity in the inverse covariance matrix. Default to 0 (no sparsity).} - -\item{lambda}{a positive number controlling the level of sparsity in the regression parameters of the PLN component. Default to 0 (no sparsity).} - \item{control}{a list-like structure for controlling the optimization, with default generated by \code{\link[=ZIPLN_param]{ZIPLN_param()}}. See the associated documentation for details.} } diff --git a/man/ZIPLNfit.Rd b/man/ZIPLNfit.Rd index abce45cd..10858476 100644 --- a/man/ZIPLNfit.Rd +++ b/man/ZIPLNfit.Rd @@ -16,7 +16,8 @@ Fields are accessed via active binding and cannot be changed by the user. data(scRNA) # data subsample: only 100 random cell and the 50 most varying transcript subset <- sample.int(nrow(scRNA), 100) -myPLN <- ZIPLN(counts[, 1:50] ~ 1 + offset(log(total_counts)), subset = subset, data = scRNA) +myZIPLN_1 <- ZIPLN(counts[, 1:50] ~ 1 + offset(log(total_counts)), zi = "single", subset = subset, data = scRNA) +myZIPLN_2 <- ZIPLN(counts[, 1:50] ~ 1 + offset(log(total_counts)), zi = "row", subset = subset, data = scRNA) } } \section{Active bindings}{ @@ -42,7 +43,7 @@ myPLN <- ZIPLN(counts[, 1:50] ~ 1 + offset(log(total_counts)), subset = subset, \item{\code{nb_param}}{number of parameters in the current PLN model} -\item{\code{vcov_model}}{character: the model used for the covariance (either "spherical", "diagonal" or "full")} +\item{\code{vcov_model}}{character: the model used for the covariance (either "spherical", "diagonal", "full" or "sparse")} \item{\code{zi_model}}{character: the model used for the zero inflation (either "single", "row", "col" or "covar")} diff --git a/man/ZIPLNfit_diagonal.Rd b/man/ZIPLNfit_diagonal.Rd new file mode 100644 index 00000000..61ac5206 --- /dev/null +++ b/man/ZIPLNfit_diagonal.Rd @@ -0,0 +1,100 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/ZIPLNfit-class.R +\name{ZIPLNfit_diagonal} +\alias{ZIPLNfit_diagonal} +\title{An R6 Class to represent a ZIPLNfit in a standard, general framework, with diagonal residual covariance} +\description{ +An R6 Class to represent a ZIPLNfit in a standard, general framework, with diagonal residual covariance + +An R6 Class to represent a ZIPLNfit in a standard, general framework, with diagonal residual covariance +} +\examples{ +\dontrun{ +data(trichoptera) +trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) +myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(covariance = "diagonal")) +class(myPLN) +print(myPLN) +} +} +\section{Super class}{ +\code{\link[PLNmodels:ZIPLNfit]{PLNmodels::ZIPLNfit}} -> \code{ZIPLNfit_diagonal} +} +\section{Active bindings}{ +\if{html}{\out{
}} +\describe{ +\item{\code{nb_param}}{number of parameters in the current PLN model} + +\item{\code{vcov_model}}{character: the model used for the residual covariance} +} +\if{html}{\out{
}} +} +\section{Methods}{ +\subsection{Public methods}{ +\itemize{ +\item \href{#method-ZIPLNfit_diagonal-new}{\code{ZIPLNfit_diagonal$new()}} +\item \href{#method-ZIPLNfit_diagonal-clone}{\code{ZIPLNfit_diagonal$clone()}} +} +} +\if{html}{\out{ +
Inherited methods + +
+}} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-ZIPLNfit_diagonal-new}{}}} +\subsection{Method \code{new()}}{ +Initialize a \code{\link{PLNfit}} model +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ZIPLNfit_diagonal$new( + responses, + covariates, + offsets, + weights, + formula, + control +)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{responses}}{the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{covariates}}{design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{offsets}}{offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{weights}}{an optional vector of observation weights to be used in the fitting process.} + +\item{\code{formula}}{model formula used for fitting, extracted from the formula in the upper-level call} + +\item{\code{control}}{a list for controlling the optimization. See details.} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-ZIPLNfit_diagonal-clone}{}}} +\subsection{Method \code{clone()}}{ +The objects of this class are cloneable with this method. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ZIPLNfit_diagonal$clone(deep = FALSE)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{deep}}{Whether to make a deep clone.} +} +\if{html}{\out{
}} +} +} +} diff --git a/man/ZIPLNfit_fixedcov.Rd b/man/ZIPLNfit_fixedcov.Rd new file mode 100644 index 00000000..d194e504 --- /dev/null +++ b/man/ZIPLNfit_fixedcov.Rd @@ -0,0 +1,100 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/ZIPLNfit-class.R +\name{ZIPLNfit_fixedcov} +\alias{ZIPLNfit_fixedcov} +\title{An R6 Class to represent a ZIPLNfit in a standard, general framework, with fixed (inverse) residual covariance} +\description{ +An R6 Class to represent a ZIPLNfit in a standard, general framework, with fixed (inverse) residual covariance + +An R6 Class to represent a ZIPLNfit in a standard, general framework, with fixed (inverse) residual covariance +} +\examples{ +\dontrun{ +data(trichoptera) +trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) +myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera) +class(myPLN) +print(myPLN) +} +} +\section{Super class}{ +\code{\link[PLNmodels:ZIPLNfit]{PLNmodels::ZIPLNfit}} -> \code{ZIPLNfit_fixedcov} +} +\section{Active bindings}{ +\if{html}{\out{
}} +\describe{ +\item{\code{nb_param}}{number of parameters in the current PLN model} + +\item{\code{vcov_model}}{character: the model used for the residual covariance} +} +\if{html}{\out{
}} +} +\section{Methods}{ +\subsection{Public methods}{ +\itemize{ +\item \href{#method-ZIPLNfit_fixedcov-new}{\code{ZIPLNfit_fixedcov$new()}} +\item \href{#method-ZIPLNfit_fixedcov-clone}{\code{ZIPLNfit_fixedcov$clone()}} +} +} +\if{html}{\out{ +
Inherited methods + +
+}} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-ZIPLNfit_fixedcov-new}{}}} +\subsection{Method \code{new()}}{ +Initialize a \code{\link{ZIPLNfit_fixedcov}} model +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ZIPLNfit_fixedcov$new( + responses, + covariates, + offsets, + weights, + formula, + control +)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{responses}}{the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{covariates}}{design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{offsets}}{offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{weights}}{an optional vector of observation weights to be used in the fitting process.} + +\item{\code{formula}}{model formula used for fitting, extracted from the formula in the upper-level call} + +\item{\code{control}}{a list for controlling the optimization. See details.} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-ZIPLNfit_fixedcov-clone}{}}} +\subsection{Method \code{clone()}}{ +The objects of this class are cloneable with this method. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ZIPLNfit_fixedcov$clone(deep = FALSE)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{deep}}{Whether to make a deep clone.} +} +\if{html}{\out{
}} +} +} +} diff --git a/man/ZIPLNfit_spherical.Rd b/man/ZIPLNfit_spherical.Rd new file mode 100644 index 00000000..f7c10652 --- /dev/null +++ b/man/ZIPLNfit_spherical.Rd @@ -0,0 +1,91 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/ZIPLNfit-class.R +\name{ZIPLNfit_spherical} +\alias{ZIPLNfit_spherical} +\title{An R6 Class to represent a ZIPLNfit in a standard, general framework, with spherical residual covariance} +\description{ +An R6 Class to represent a ZIPLNfit in a standard, general framework, with spherical residual covariance + +An R6 Class to represent a ZIPLNfit in a standard, general framework, with spherical residual covariance +} +\section{Super class}{ +\code{\link[PLNmodels:ZIPLNfit]{PLNmodels::ZIPLNfit}} -> \code{ZIPLNfit_spherical} +} +\section{Active bindings}{ +\if{html}{\out{
}} +\describe{ +\item{\code{nb_param}}{number of parameters in the current PLN model} + +\item{\code{vcov_model}}{character: the model used for the residual covariance} +} +\if{html}{\out{
}} +} +\section{Methods}{ +\subsection{Public methods}{ +\itemize{ +\item \href{#method-ZIPLNfit_spherical-new}{\code{ZIPLNfit_spherical$new()}} +\item \href{#method-ZIPLNfit_spherical-clone}{\code{ZIPLNfit_spherical$clone()}} +} +} +\if{html}{\out{ +
Inherited methods + +
+}} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-ZIPLNfit_spherical-new}{}}} +\subsection{Method \code{new()}}{ +Initialize a \code{\link{PLNfit}} model +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ZIPLNfit_spherical$new( + responses, + covariates, + offsets, + weights, + formula, + control +)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{responses}}{the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{covariates}}{design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{offsets}}{offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{weights}}{an optional vector of observation weights to be used in the fitting process.} + +\item{\code{formula}}{model formula used for fitting, extracted from the formula in the upper-level call} + +\item{\code{control}}{a list for controlling the optimization. See details.} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-ZIPLNfit_spherical-clone}{}}} +\subsection{Method \code{clone()}}{ +The objects of this class are cloneable with this method. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ZIPLNfit_spherical$clone(deep = FALSE)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{deep}}{Whether to make a deep clone.} +} +\if{html}{\out{
}} +} +} +} diff --git a/man/coef.ZIPLNfit.Rd b/man/coef.ZIPLNfit.Rd index 96d445e8..73c73091 100644 --- a/man/coef.ZIPLNfit.Rd +++ b/man/coef.ZIPLNfit.Rd @@ -4,13 +4,13 @@ \alias{coef.ZIPLNfit} \title{Extract model coefficients} \usage{ -\method{coef}{ZIPLNfit}(object, type = c("mainPLN", "mainZI", "precision"), ...) +\method{coef}{ZIPLNfit}(object, type = c("mainPLN", "mainZI", "precision", "covariance"), ...) } \arguments{ \item{object}{an R6 object with class \code{\link{ZIPLNfit}}} -\item{type}{type of parameter that should be extracted. Either "mainPLN" (default) for \deqn{\Theta}, -"mainZI" (default) for \deqn{\Theta0} or "precision" for \deqn{\Omega}} +\item{type}{type of parameter that should be extracted. Either "mainPLN" (default) for \deqn{B}, +"mainZI" (default) for \deqn{B0}, "precision" for \deqn{\Omega}, "covariance" for \deqn{\Sigma}} \item{...}{additional parameters for S3 compatibility. Not used} } diff --git a/man/compute_ZIPLN_starting_point.Rd b/man/compute_ZIPLN_starting_point.Rd new file mode 100644 index 00000000..7bfaaf7a --- /dev/null +++ b/man/compute_ZIPLN_starting_point.Rd @@ -0,0 +1,39 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/utils.R +\name{compute_ZIPLN_starting_point} +\alias{compute_ZIPLN_starting_point} +\title{Helper function for PLN initialization.} +\usage{ +compute_ZIPLN_starting_point(Y, X, X0, O, w, s = 0.1) +} +\arguments{ +\item{Y}{Response count matrix} + +\item{X}{Covariate matrix} + +\item{O}{Offset matrix (in log-scale)} + +\item{w}{Weight vector (defaults to 1)} + +\item{s}{Scale parameter for S (defaults to 0.1)} +} +\value{ +a named list of starting values for model parameter B and variational parameters M and S used in the iterative optimization algorithm of \code{\link[=PLN]{PLN()}} +} +\description{ +Barebone function to compute starting points for B, M and S when fitting a PLN. Mostly intended for internal use. +} +\details{ +The default strategy to estimate B and M is to fit a linear model with covariates \code{X} to the response count matrix (after adding a pseudocount of 1, scaling by the offset and taking the log). The regression matrix is used to initialize \code{B} and the residuals to initialize \code{M}. \code{S} is initialized as a constant conformable matrix with value \code{s}. +} +\examples{ +\dontrun{ +data(barents) +Y <- barents$Abundance +X <- model.matrix(Abundance ~ Latitude + Longitude + Depth + Temperature, data = barents) +O <- log(barents$Offset) +w <-- rep(1, nrow(Y)) +compute_ZIPLN_starting_point(Y, X, O, w) +} + +} diff --git a/tests/testthat/test-ziplnfit.R b/tests/testthat/test-ziplnfit.R index ad89f54f..191c905e 100644 --- a/tests/testthat/test-ziplnfit.R +++ b/tests/testthat/test-ziplnfit.R @@ -5,14 +5,7 @@ trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) test_that("ZIPLN fit: check classes, getters and field access", { - expect_output(model <- ZIPLN(Abundance ~ 1, data = trichoptera, - control = ZIPLN_param(trace = 1)), -" - Initialization... - Adjusting a ZI-PLN model with full covariance model and single specific parameter(s) in Zero inflation component. - DONE!" - ) - + model <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(trace = 1)) expect_is(model, "ZIPLNfit") expect_equal(model$n, nrow(trichoptera$Abundance)) @@ -21,17 +14,15 @@ test_that("ZIPLN fit: check classes, getters and field access", { ## S3 methods: values expect_equal(coef(model), model$model_par$B) - expect_equal(coef(model, type = "covariance"), sigma(model)) + expect_equal(coef(model, type = "mainPLN"), model$model_par$B) + expect_equal(coef(model, type = "mainZI"), model$model_par$B0) + expect_equal(coef(model, type = "precision"), model$model_par$Omega) + expect_equal(coef(model, type = "covariance"), model$model_par$Sigma) expect_equal(sigma(model), model$model_par$Sigma) - # expect_equal(vcov(model), model$vcov_coef) ## S3 methods: class expect_true(inherits(coef(model), "matrix")) expect_true(inherits(sigma(model), "matrix")) - # expect_true(inherits(vcov(model), "dsCMatrix")) - - ## S3 methods: dimensions - ## expect_equal(dim(vcov(model)), c(model$d * model$p, model$d * model$p)) ## R6 bindings expect_is(model$latent, "matrix") @@ -40,26 +31,10 @@ test_that("ZIPLN fit: check classes, getters and field access", { }) -test_that("PLN fit: check print message", { - - expect_output(model <- PLN(Abundance ~ 1, data = trichoptera)) - - output <- paste( -"A multivariate Poisson Lognormal fit with full covariance model. -==================================================================", -capture_output(print(as.data.frame(round(model$criteria, digits = 3), row.names = ""))), -"================================================================== -* Useful fields - $model_par, $latent, $latent_pos, $var_par, $optim_par - $loglik, $BIC, $ICL, $loglik_vec, $nb_param, $criteria -* Useful S3 methods - print(), coef(), sigma(), vcov(), fitted() - predict(), predict_cond(), standard_error()", - sep = "\n") - - expect_output(model$show(), - output, - fixed = TRUE) +test_that("ZIPLN fit: check print message", { + + expect_output(model <- ZIPLN(Abundance ~ 1, data = trichoptera)) + ## show and print are equivalent expect_equal(capture_output(model$show()), capture_output(model$print())) @@ -67,9 +42,9 @@ capture_output(print(as.data.frame(round(model$criteria, digits = 3), row.names test_that("PLN fit: Check prediction", { - model1 <- PLN(Abundance ~ 1, data = trichoptera, subset = 1:30) - model1_off <- PLN(Abundance ~ 1 + offset(log(Offset)), data = trichoptera, subset = 1:30) - model2 <- PLN(Abundance ~ Pressure, data = trichoptera, subset = 1:30) + model1 <- ZIPLN(Abundance ~ 1, data = trichoptera, subset = 1:30) + model1_off <- ZIPLN(Abundance ~ 1 + offset(log(Offset)), data = trichoptera, subset = 1:30) + model2 <- ZIPLN(Abundance ~ Pressure, data = trichoptera, subset = 1:30) newdata <- trichoptera[31:49, ] # newdata$Abundance <- NULL @@ -115,59 +90,30 @@ test_that("PLN fit: Check prediction", { -test_that("PLN fit: Check conditional prediction", { - - n_cond = 10 - p_cond = 2 - p <- ncol(trichoptera$Abundance) - - myPLN <- PLN(Abundance ~ Temperature, trichoptera) - Yc <- trichoptera$Abundance[1:n_cond, 1:p_cond, drop=FALSE] - - newX <- data.frame(1, Temperature = trichoptera$Temperature[1:n_cond]) - - pred <- predict_cond(myPLN, newX, Yc, type = "response") - - # check dimensions of the predictions (#TODO: modify pred$pred if we decide not to return M,S) - expect_equal(dim(pred), c(n_cond,p-p_cond)) - - # check if the RMSE of conditional predictions are greater than the marginal ones - expect_gt( - mean((trichoptera$Abundance[1:n_cond, (p_cond+1):p] - - predict(myPLN, newdata = newX, type = "response")[1:n_cond, (p_cond+1):p])^2), - mean((trichoptera$Abundance[1:n_cond, (p_cond+1):p] - pred)^2) - ) - - # check the dimension of the variational parameters when sent back - pred <- predict_cond(myPLN, newX, Yc, type = "response", var_par = TRUE) - expect_equal(dim(attr(pred, "M")), dim(pred)) - expect_equal(dim(attr(pred, "S")), c(p-p_cond, p-p_cond, n_cond)) - -}) - -test_that("PLN fit: Check number of parameters", { +test_that("ZIPLN fit: Check number of parameters", { p <- ncol(trichoptera$Abundance) - model <- PLN(Abundance ~ 1, data = trichoptera) - expect_equal(model$nb_param, p*(p+1)/2 + p * 1) + model <- ZIPLN(Abundance ~ 1, data = trichoptera) + expect_equal(model$nb_param, p*(p+1)/2 + p * 1 + 1) - model <- PLN(Abundance ~ 1 + Wind, data = trichoptera) - expect_equal(model$nb_param, p*(p+1)/2 + p * 2) + model <- ZIPLN(Abundance ~ 1 + Wind, data = trichoptera) + expect_equal(model$nb_param, p*(p+1)/2 + p * 2 + 1) - model <- PLN(Abundance ~ Group + 0 , data = trichoptera) - expect_equal(model$nb_param, p*(p+1)/2 + p * nlevels(trichoptera$Group)) + model <- ZIPLN(Abundance ~ Group + 0 , data = trichoptera) + expect_equal(model$nb_param, p*(p+1)/2 + p * nlevels(trichoptera$Group) + 1) - modelS <- PLN(Abundance ~ 1, data = trichoptera, control = PLN_param(covariance = "spherical")) - expect_equal(modelS$nb_param, 1 + p * 1) + modelS <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(covariance = "spherical")) + expect_equal(modelS$nb_param, 1 + p * 1 + 1) expect_equal(modelS$vcov_model, "spherical") - modelD <- PLN(Abundance ~ 1, data = trichoptera, control = PLN_param(covariance = "diagonal")) - expect_equal(modelD$nb_param, p + p * 1) + modelD <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(covariance = "diagonal")) + expect_equal(modelD$nb_param, p + p * 1 + 1) expect_equal(modelD$vcov_model, "diagonal") - model <- PLN(Abundance ~ 1, data = trichoptera, control = PLN_param(covariance = "fixed", Omega = as.matrix(modelD$model_par$Omega))) - expect_equal(model$nb_param, 0 + p * 1) + model <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(covariance = "fixed", Omega = as.matrix(modelD$model_par$Omega))) + expect_equal(model$nb_param, 0 + p * 1 + 1) + expect_equal(model$model_par$Omega, modelD$model_par$Omega) expect_equal(model$vcov_model, "fixed") }) From 16bd6b2d8626b0556fd2978000ad36ff3d6800b0 Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Tue, 16 Jan 2024 09:37:17 +0100 Subject: [PATCH 10/36] added fixed and sparse covariance for ZIPLN --- R/ZIPLN.R | 29 ++++++---- R/ZIPLNfit-class.R | 112 +++++++++++++++++++++++++++++++++--- R/optim-zipln.R | 68 +++------------------- R/utils.R | 2 +- man/ZIPLN.Rd | 13 +++-- man/ZIPLN_param.Rd | 5 +- man/ZIPLNfit.Rd | 4 ++ man/ZIPLNfit_fixed.Rd | 93 ++++++++++++++++++++++++++++++ man/ZIPLNfit_fixedcov.Rd | 37 +++++------- man/ZIPLNfit_spherical.Rd | 9 +++ man/optim_zipln_B.Rd | 19 ++++++ tests/testthat/test-zipln.R | 12 +++- 12 files changed, 291 insertions(+), 112 deletions(-) create mode 100644 man/ZIPLNfit_fixed.Rd create mode 100644 man/optim_zipln_B.Rd diff --git a/R/ZIPLN.R b/R/ZIPLN.R index dd2d7ff7..1000e590 100644 --- a/R/ZIPLN.R +++ b/R/ZIPLN.R @@ -14,12 +14,13 @@ #' @rdname ZIPLN #' @include ZIPLNfit-class.R #' @examples -#' data(scRNA) -#' # data subsample: only 100 random cell and the 50 most varying transcript -#' scRNA <- scRNA[sample.int(nrow(scRNA), 100), ] -#' scRNA$counts <- scRNA$counts[, 1:50] -#' myPLN_full <- ZIPLN(counts ~ 1 + cell_line + offset(log(total_counts)), data = scRNA) -#' myPLN_sparse <- ZIPLN(counts ~ 1 + offset(log(total_counts)), rho = .5, data = scRNA) +#' data(trichoptera) +#' trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) +#' myPLN <- PLN(Abundance ~ 1, data = trichoptera) +#' myZIPLN_1 <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "single") +#' myZIPLN_2 <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "row") +#' myZIPLN_3 <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "col") +#' myZIPLN_3 <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "col") #' myPLN_full$criteria # better BIC with sparse version #' myPLN_sparse$criteria #' @seealso The class [`ZIPLNfit`] @@ -31,7 +32,6 @@ ZIPLN <- function(formula, data, subset, zi = c("single", "row", "col"), control args <- extract_model_zi(match.call(expand.dots = FALSE), parent.frame()) ## define default control parameters for optim and eventually overwrite them by user-defined parameters - control$lambda <- 0 control$rho <- 0 control$ziparam <- ifelse((args$zicovar), "covar", match.arg(zi)) control$penalize_intercept <- FALSE @@ -46,7 +46,8 @@ ZIPLN <- function(formula, data, subset, zi = c("single", "row", "col"), control myPLN <- switch(control$covariance, "diagonal" = ZIPLNfit_diagonal$new(args$Y , list(PLN = args$X, ZI = args$X0), args$O, args$w, args$formula, control), "spherical" = ZIPLNfit_spherical$new(args$Y, list(PLN = args$X, ZI = args$X0), args$O, args$w, args$formula, control), - "fixed" = ZIPLNfit_fixedcov$new(args$Y , list(PLN = args$X, ZI = args$X0), args$O, args$w, args$formula, control), + "fixed" = ZIPLNfit_fixed$new(args$Y , list(PLN = args$X, ZI = args$X0), args$O, args$w, args$formula, control), + "sparse" = ZIPLNfit_sparse$new(args$Y , list(PLN = args$X, ZI = args$X0), args$O, args$w, args$formula, control), ZIPLNfit$new(args$Y, list(PLN = args$X, ZI = args$X0), args$O, args$w, args$formula, control)) # default: full covariance ## optimization @@ -62,13 +63,12 @@ ZIPLN <- function(formula, data, subset, zi = c("single", "row", "col"), control ## ----------------------------------------------------------------- ## Series of setter to default parameters for user's main functions -available_algorithms <- c("MMA", "CCSAQ", "LBFGS", "VAR1", "VAR2", "TNEWTON", "TNEWTON_PRECOND", "TNEWTON_PRECOND_RESTART") - #' Control of a PLN fit #' #' Helper to define list of parameters to control the PLN fit. All arguments have defaults. #' #' @inheritParams PLN_param +#' @param penalty a user defined penalty for sparsifying the residual covariance. Default is 0 (no sparsity). #' @return list of parameters configuring the fit. #' #' @inherit PLN_param details @@ -81,15 +81,19 @@ available_algorithms <- c("MMA", "CCSAQ", "LBFGS", "VAR1", "VAR2", "TNEWTON", "T ZIPLN_param <- function( backend = c("nlopt"), trace = 1, - covariance = c("full", "diagonal", "spherical", "fixed"), + covariance = c("full", "diagonal", "spherical", "fixed", "sparse"), Omega = NULL, + penalty = 0, config_post = list(), config_optim = list(), inception = NULL # pretrained ZIPLNfit used as initialization ) { covariance <- match.arg(covariance) - if (covariance == "fixed") stopifnot(inherits(Omega, "matrix") | inherits(Omega, "Matrix")) + if (covariance == "fixed") stopifnot("Omega must be provied for fixed covariance" = inherits(Omega, "matrix") | inherits(Omega, "Matrix")) |> try() + if (inherits(Omega, "matrix") | inherits(Omega, "Matrix")) covariance <- "fixed" + if (covariance == "sparse") stopifnot("You should provide a positive penalty when chosing 'sparse' covariance" = penalty > 0) |> try() + if (penalty > 0) covariance <- "sparse" if (!is.null(inception)) stopifnot(isZIPLNfit(inception)) ## post-treatment config @@ -111,6 +115,7 @@ ZIPLN_param <- function( trace = trace , covariance = covariance, Omega = Omega , + penalty = penalty , config_post = config_pst, config_optim = config_opt, inception = inception), class = "PLNmodels_param") diff --git a/R/ZIPLNfit-class.R b/R/ZIPLNfit-class.R index 296b35b6..589749e0 100644 --- a/R/ZIPLNfit-class.R +++ b/R/ZIPLNfit-class.R @@ -10,6 +10,7 @@ #' @param responses the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class #' @param covariates design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class #' @param offsets offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class +#' @param weights an optional vector of observation weights to be used in the fitting process. #' @param formula model formula used for fitting, extracted from the formula in the upper-level call #' @param control a list for controlling the optimization. See details. #' @@ -437,7 +438,7 @@ ZIPLNfit_diagonal <- R6Class( #' \dontrun{ #' data(trichoptera) #' trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) -#' myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(covariance = "spherical)) +#' myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(covariance = "spherical")) #' class(myPLN) #' print(myPLN) #' } @@ -471,7 +472,7 @@ ZIPLNfit_spherical <- R6Class( ) ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -## CLASS ZIPLNfit_fixedcov ############################# +## CLASS ZIPLNfit_fixed ############################# ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' An R6 Class to represent a ZIPLNfit in a standard, general framework, with fixed (inverse) residual covariance @@ -486,19 +487,19 @@ ZIPLNfit_spherical <- R6Class( #' @param control a list for controlling the optimization. See details. #' @param config part of the \code{control} argument which configures the optimizer #' -#' @rdname ZIPLNfit_fixedcov +#' @rdname ZIPLNfit_fixed #' @importFrom R6 R6Class #' #' @examples #' \dontrun{ #' data(trichoptera) #' trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) -#' myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera) +#' myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, contro = ZIPLN_param(Omega = diag(ncol(trichoptera$Abundance)))) #' class(myPLN) #' print(myPLN) #' } -ZIPLNfit_fixedcov <- R6Class( - classname = "ZIPLNfit_fixedcov", +ZIPLNfit_fixed <- R6Class( + classname = "ZIPLNfit_fixed", inherit = ZIPLNfit, ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ## PUBLIC MEMBERS ---- @@ -508,7 +509,7 @@ ZIPLNfit_fixedcov <- R6Class( initialize = function(responses, covariates, offsets, weights, formula, control) { super$initialize(responses, covariates, offsets, weights, formula, control) private$Omega <- control$Omega -### TODO handled fixed cov + private$optimizer$Omega <- function(M, X, B, S) {private$Omega} } ), active = list( @@ -526,6 +527,101 @@ ZIPLNfit_fixedcov <- R6Class( vcov_model = function() {"fixed"} ) ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - ## END OF THE CLASS ZIPLNfit_fixedcov + ## END OF THE CLASS ZIPLNfit_fixed + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +) + +## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +## CLASS ZIPLNfit_sparse ############################# +## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +#' An R6 Class to represent a ZIPLNfit in a standard, general framework, with sparse inverse residual covariance +#' +#' @param responses the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class +#' @param covariates design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class +#' @param offsets offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class +#' @param data an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which PLN is called. +#' @param weights an optional vector of observation weights to be used in the fitting process. +#' @param nullModel null model used for approximate R2 computations. Defaults to a GLM model with same design matrix but not latent variable. +#' @param formula model formula used for fitting, extracted from the formula in the upper-level call +#' @param control a list for controlling the optimization. See details. +#' @param config part of the \code{control} argument which configures the optimizer +#' +#' @rdname ZIPLNfit_fixedcov +#' @importFrom R6 R6Class +#' +#' @examples +#' \dontrun{ +#' data(trichoptera) +#' trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) +#' myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, control= ZIPLN_param(penalty = 0.2)) +#' class(myPLN) +#' print(myPLN) +#' } +ZIPLNfit_sparse <- R6Class( + classname = "ZIPLNfit_sparse", + inherit = ZIPLNfit, + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + ## PUBLIC MEMBERS ---- + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + public = list( + #' @description Initialize a [`ZIPLNfit_fixedcov`] model + #' @importFrom glassoFast glassoFast + initialize = function(responses, covariates, offsets, weights, formula, control) { + super$initialize(responses, covariates, offsets, weights, formula, control) + private$optimizer$Omega <- + function(M, X, B, S) { + glassoFast( crossprod(M - X %*% B)/self$n + diag(colMeans(S * S), self$p, self$p), rho = control$penalty )$wi + } + } + ), + active = list( + #' @field nb_param number of parameters in the current PLN model + nb_param = function() { + res <- self$p * self$d + (sum(private$Omega != 0) - self$p)/2L + + switch(private$ziparam, + "single" = 1, + "row" = self$n, + "col" = self$p, + "covar" = self$p * self$d) + as.integer(res) + }, + #' @field vcov_model character: the model used for the residual covariance + vcov_model = function() {"sparse"} + ) + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + ## END OF THE CLASS ZIPLNfit_sparse ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ) + + +# Test convergence for a named list of parameters +# oldp, newp: named list of parameters +# xtol_rel: double ; negative or NULL = disabled +# xtol_abs: double ; negative or NULL = disabled +# Returns boolean +parameter_list_converged <- function(oldp, newp, xtol_abs = NULL, xtol_rel = NULL) { + # Strategy is to compare each pair of list elements with matching names. + stopifnot(is.list(oldp), is.list(newp)) + oldp <- oldp[order(names(oldp))] + newp <- newp[order(names(newp))] + stopifnot(all(names(oldp) == names(newp))) + + # Check convergence with xtol_rel if enabled + if(is.double(xtol_rel) && xtol_rel > 0) { + if(all(mapply(function(o, n) { all(abs(n - o) <= xtol_rel * abs(o)) }, oldp, newp))) { + return(TRUE) + } + } + + # Check convergence with xtol_abs (homogeneous) if enabled + if(is.double(xtol_abs) && xtol_abs > 0) { + if(all(mapply(function(o, n) { all(abs(n - o) <= xtol_abs) }, oldp, newp))) { + return(TRUE) + } + } + + # If no criteria has triggered, indicate no convergence + FALSE +} + diff --git a/R/optim-zipln.R b/R/optim-zipln.R index 4a93f2ed..1bc9a836 100644 --- a/R/optim-zipln.R +++ b/R/optim-zipln.R @@ -19,7 +19,7 @@ optimize_zi <- function(init_parameters, Y, X, O, configuration) { n <- nrow(Y); p <- ncol(Y); d <- ncol(X) - + # Link to the approximate function to optimize Omega ,depending on the target structure optim_zipln_Omega <- switch( configuration$covariance, @@ -36,7 +36,7 @@ optimize_zi <- function(init_parameters, Y, X, O, configuration) { "col" = function(init_B0, X, R, config) list(Pi = matrix(colMeans(R), n, p, byrow = TRUE), B0 = matrix(NA, d, p)), "covar" = optim_zipln_zipar_covar ) - + maxit_out <- if("maxit_out" %in% names(configuration)) { configuration$maxit_out } else { 50 } # Main loop @@ -63,7 +63,7 @@ optimize_zi <- function(init_parameters, Y, X, O, configuration) { new_B <- optim_zipln_B( M = parameters$M, X = X, Omega = new_Omega, configuration ) - + optim_new_zipar <- optim_zipln_zipar( init_B0 = parameters$B0, X = X, R = parameters$R, config = configuration ) @@ -125,11 +125,11 @@ optimize_zi <- function(init_parameters, Y, X, O, configuration) { } } -#' @importFrom glassoFast glassoFast -optim_zipln_Omega_sparse <- function(M, X, B, S, rho) { - n <- nrow(M); p <- ncol(M) - glassoFast::glassoFast( crossprod(M - X %*% B)/n + diag(colMeans(S * S), p, p), rho = rho )$wi -} +#' #' @importFrom glassoFast glassoFast +#' optim_zipln_Omega_sparse <- function(M, X, B, S, rho) { +#' n <- nrow(M); p <- ncol(M) +#' glassoFast::glassoFast( crossprod(M - X %*% B)/n + diag(colMeans(S * S), p, p), rho = rho )$wi +#' } #' @importFrom glmnet glmnet optim_zipln_B <- function(M, X, Omega, config) { @@ -163,55 +163,3 @@ optim_zipln_B <- function(M, X, Omega, config) { B } -# Test convergence for a named list of parameters -# oldp, newp: named list of parameters -# xtol_rel: double ; negative or NULL = disabled -# xtol_abs: double ; negative or NULL = disabled -# Returns boolean -parameter_list_converged <- function(oldp, newp, xtol_abs = NULL, xtol_rel = NULL) { - # Strategy is to compare each pair of list elements with matching names. - # Named lists are just vectors (T,str) using order of insertion. - # mapply() is handy to do the pair tests, but it works on the underlying vector order (ignoring names). - # So reorder lists by their names to use mapply. - stopifnot(is.list(oldp), is.list(newp)) - oldp <- oldp[order(names(oldp))] - newp <- newp[order(names(newp))] - stopifnot(all(names(oldp) == names(newp))) - - # Check convergence with xtol_rel if enabled - if(is.double(xtol_rel) && xtol_rel > 0) { - if(all(mapply(function(o, n) { all(abs(n - o) <= xtol_rel * abs(o)) }, oldp, newp))) { - return(TRUE) - } - } - - # Check convergence with xtol_abs (homogeneous) if enabled - if(is.double(xtol_abs) && xtol_abs > 0) { - if(all(mapply(function(o, n) { all(abs(n - o) <= xtol_abs) }, oldp, newp))) { - return(TRUE) - } - } - - # Check convergence with xtol_abs as list(xtol_abs for each param_name) - if(is.list(xtol_abs)) { - xtol_abs <- xtol_abs[order(names(xtol_abs))] - stopifnot(all(names(oldp) == names(xtol_abs))) - # Due to the possible presence of NULLs, mapply may return a list. unlist allows all() to operate anyway. - if(all(unlist(mapply( - function(o, n, tol) { - if((is.double(tol) && tol > 0) || is.matrix(tol)) { - all(abs(n - o) <= tol) - } else { - NULL # Ignore comparison in outer all() - } - }, - oldp, newp, xtol_abs - )))) { - return(TRUE) - } - } - - # If no criteria has triggered, indicate no convergence - FALSE -} - diff --git a/R/utils.R b/R/utils.R index 9666254e..e5d7fd3c 100644 --- a/R/utils.R +++ b/R/utils.R @@ -1,4 +1,4 @@ -available_algorithms_nlopt <- c("MMA", "CCSAQ", "LBFGS", "LBFGS_NOCEDAL", "VAR1", "VAR2") +available_algorithms_nlopt <- c("MMA", "CCSAQ", "LBFGS", "LBFGS_NOCEDAL", "VAR1", "VAR2") #"TNEWTON", "TNEWTON_PRECOND", "TNEWTON_PRECOND_RESTART"# available_algorithms_torch <- c("RPROP", "RMSPROP", "ADAM", "ADAGRAD") config_default_nlopt <- diff --git a/man/ZIPLN.Rd b/man/ZIPLN.Rd index 026e54be..64d9ba07 100644 --- a/man/ZIPLN.Rd +++ b/man/ZIPLN.Rd @@ -33,12 +33,13 @@ an R6 object with class \code{\link{ZIPLNfit}} Fit the multivariate Zero Inflated Poisson lognormal model with a variational algorithm. Use the (g)lm syntax for model specification (covariates, offsets, subset). } \examples{ -data(scRNA) -# data subsample: only 100 random cell and the 50 most varying transcript -scRNA <- scRNA[sample.int(nrow(scRNA), 100), ] -scRNA$counts <- scRNA$counts[, 1:50] -myPLN_full <- ZIPLN(counts ~ 1 + cell_line + offset(log(total_counts)), data = scRNA) -myPLN_sparse <- ZIPLN(counts ~ 1 + offset(log(total_counts)), rho = .5, data = scRNA) +data(trichoptera) +trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) +myPLN <- PLN(Abundance ~ 1, data = trichoptera) +myZIPLN_1 <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "single") +myZIPLN_2 <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "row") +myZIPLN_3 <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "col") +myZIPLN_3 <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "col") myPLN_full$criteria # better BIC with sparse version myPLN_sparse$criteria } diff --git a/man/ZIPLN_param.Rd b/man/ZIPLN_param.Rd index 1324d776..cf977d32 100644 --- a/man/ZIPLN_param.Rd +++ b/man/ZIPLN_param.Rd @@ -7,8 +7,9 @@ ZIPLN_param( backend = c("nlopt"), trace = 1, - covariance = c("full", "diagonal", "spherical", "fixed"), + covariance = c("full", "diagonal", "spherical", "fixed", "sparse"), Omega = NULL, + penalty = 0, config_post = list(), config_optim = list(), inception = NULL @@ -23,6 +24,8 @@ ZIPLN_param( \item{Omega}{precision matrix of the latent variables. Inverse of Sigma. Must be specified if \code{covariance} is "fixed"} +\item{penalty}{a user defined penalty for sparsifying the residual covariance. Default is 0 (no sparsity).} + \item{config_post}{a list for controlling the post-treatments (optional bootstrap, jackknife, R2, etc.). See details} \item{config_optim}{a list for controlling the optimizer (either "nlopt" or "torch" backend). See details} diff --git a/man/ZIPLNfit.Rd b/man/ZIPLNfit.Rd index 10858476..0d2daff4 100644 --- a/man/ZIPLNfit.Rd +++ b/man/ZIPLNfit.Rd @@ -151,6 +151,8 @@ Initialize a \code{\link{ZIPLNfit}} model \item{\code{offsets}}{offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class} +\item{\code{weights}}{an optional vector of observation weights to be used in the fitting process.} + \item{\code{formula}}{model formula used for fitting, extracted from the formula in the upper-level call} \item{\code{control}}{a list for controlling the optimization. See details.} @@ -176,6 +178,8 @@ Call to the Cpp optimizer and update of the relevant fields \item{\code{offsets}}{offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class} +\item{\code{weights}}{an optional vector of observation weights to be used in the fitting process.} + \item{\code{control}}{a list for controlling the optimization. See details.} } \if{html}{\out{}} diff --git a/man/ZIPLNfit_fixed.Rd b/man/ZIPLNfit_fixed.Rd new file mode 100644 index 00000000..d6cc0a7e --- /dev/null +++ b/man/ZIPLNfit_fixed.Rd @@ -0,0 +1,93 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/ZIPLNfit-class.R +\name{ZIPLNfit_fixed} +\alias{ZIPLNfit_fixed} +\title{An R6 Class to represent a ZIPLNfit in a standard, general framework, with fixed (inverse) residual covariance} +\description{ +An R6 Class to represent a ZIPLNfit in a standard, general framework, with fixed (inverse) residual covariance + +An R6 Class to represent a ZIPLNfit in a standard, general framework, with fixed (inverse) residual covariance +} +\examples{ +\dontrun{ +data(trichoptera) +trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) +myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera) +class(myPLN) +print(myPLN) +} +} +\section{Super class}{ +\code{\link[PLNmodels:ZIPLNfit]{PLNmodels::ZIPLNfit}} -> \code{ZIPLNfit_fixed} +} +\section{Active bindings}{ +\if{html}{\out{
}} +\describe{ +\item{\code{nb_param}}{number of parameters in the current PLN model} + +\item{\code{vcov_model}}{character: the model used for the residual covariance} +} +\if{html}{\out{
}} +} +\section{Methods}{ +\subsection{Public methods}{ +\itemize{ +\item \href{#method-ZIPLNfit_fixed-new}{\code{ZIPLNfit_fixed$new()}} +\item \href{#method-ZIPLNfit_fixed-clone}{\code{ZIPLNfit_fixed$clone()}} +} +} +\if{html}{\out{ +
Inherited methods + +
+}} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-ZIPLNfit_fixed-new}{}}} +\subsection{Method \code{new()}}{ +Initialize a \code{\link{ZIPLNfit_fixedcov}} model +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ZIPLNfit_fixed$new(responses, covariates, offsets, weights, formula, control)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{responses}}{the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{covariates}}{design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{offsets}}{offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{weights}}{an optional vector of observation weights to be used in the fitting process.} + +\item{\code{formula}}{model formula used for fitting, extracted from the formula in the upper-level call} + +\item{\code{control}}{a list for controlling the optimization. See details.} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-ZIPLNfit_fixed-clone}{}}} +\subsection{Method \code{clone()}}{ +The objects of this class are cloneable with this method. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ZIPLNfit_fixed$clone(deep = FALSE)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{deep}}{Whether to make a deep clone.} +} +\if{html}{\out{
}} +} +} +} diff --git a/man/ZIPLNfit_fixedcov.Rd b/man/ZIPLNfit_fixedcov.Rd index d194e504..c73dd4d1 100644 --- a/man/ZIPLNfit_fixedcov.Rd +++ b/man/ZIPLNfit_fixedcov.Rd @@ -1,24 +1,24 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/ZIPLNfit-class.R -\name{ZIPLNfit_fixedcov} -\alias{ZIPLNfit_fixedcov} -\title{An R6 Class to represent a ZIPLNfit in a standard, general framework, with fixed (inverse) residual covariance} +\name{ZIPLNfit_sparse} +\alias{ZIPLNfit_sparse} +\title{An R6 Class to represent a ZIPLNfit in a standard, general framework, with sparse inverse residual covariance} \description{ -An R6 Class to represent a ZIPLNfit in a standard, general framework, with fixed (inverse) residual covariance +An R6 Class to represent a ZIPLNfit in a standard, general framework, with sparse inverse residual covariance -An R6 Class to represent a ZIPLNfit in a standard, general framework, with fixed (inverse) residual covariance +An R6 Class to represent a ZIPLNfit in a standard, general framework, with sparse inverse residual covariance } \examples{ \dontrun{ data(trichoptera) trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) -myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera) +myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, control= ZIPLN_param(penalty = 0.2)) class(myPLN) print(myPLN) } } \section{Super class}{ -\code{\link[PLNmodels:ZIPLNfit]{PLNmodels::ZIPLNfit}} -> \code{ZIPLNfit_fixedcov} +\code{\link[PLNmodels:ZIPLNfit]{PLNmodels::ZIPLNfit}} -> \code{ZIPLNfit_sparse} } \section{Active bindings}{ \if{html}{\out{
}} @@ -32,8 +32,8 @@ print(myPLN) \section{Methods}{ \subsection{Public methods}{ \itemize{ -\item \href{#method-ZIPLNfit_fixedcov-new}{\code{ZIPLNfit_fixedcov$new()}} -\item \href{#method-ZIPLNfit_fixedcov-clone}{\code{ZIPLNfit_fixedcov$clone()}} +\item \href{#method-ZIPLNfit_sparse-new}{\code{ZIPLNfit_sparse$new()}} +\item \href{#method-ZIPLNfit_sparse-clone}{\code{ZIPLNfit_sparse$clone()}} } } \if{html}{\out{ @@ -47,19 +47,12 @@ print(myPLN) }} \if{html}{\out{
}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-ZIPLNfit_fixedcov-new}{}}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-ZIPLNfit_sparse-new}{}}} \subsection{Method \code{new()}}{ Initialize a \code{\link{ZIPLNfit_fixedcov}} model \subsection{Usage}{ -\if{html}{\out{
}}\preformatted{ZIPLNfit_fixedcov$new( - responses, - covariates, - offsets, - weights, - formula, - control -)}\if{html}{\out{
}} +\if{html}{\out{
}}\preformatted{ZIPLNfit_sparse$new(responses, covariates, offsets, weights, formula, control)}\if{html}{\out{
}} } \subsection{Arguments}{ @@ -81,12 +74,12 @@ Initialize a \code{\link{ZIPLNfit_fixedcov}} model } } \if{html}{\out{
}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-ZIPLNfit_fixedcov-clone}{}}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-ZIPLNfit_sparse-clone}{}}} \subsection{Method \code{clone()}}{ The objects of this class are cloneable with this method. \subsection{Usage}{ -\if{html}{\out{
}}\preformatted{ZIPLNfit_fixedcov$clone(deep = FALSE)}\if{html}{\out{
}} +\if{html}{\out{
}}\preformatted{ZIPLNfit_sparse$clone(deep = FALSE)}\if{html}{\out{
}} } \subsection{Arguments}{ diff --git a/man/ZIPLNfit_spherical.Rd b/man/ZIPLNfit_spherical.Rd index f7c10652..08664797 100644 --- a/man/ZIPLNfit_spherical.Rd +++ b/man/ZIPLNfit_spherical.Rd @@ -8,6 +8,15 @@ An R6 Class to represent a ZIPLNfit in a standard, general framework, with spher An R6 Class to represent a ZIPLNfit in a standard, general framework, with spherical residual covariance } +\examples{ +\dontrun{ +data(trichoptera) +trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) +myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(covariance = "spherical")) +class(myPLN) +print(myPLN) +} +} \section{Super class}{ \code{\link[PLNmodels:ZIPLNfit]{PLNmodels::ZIPLNfit}} -> \code{ZIPLNfit_spherical} } diff --git a/man/optim_zipln_B.Rd b/man/optim_zipln_B.Rd new file mode 100644 index 00000000..770f11ee --- /dev/null +++ b/man/optim_zipln_B.Rd @@ -0,0 +1,19 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/optim-zipln.R +\name{optim_zipln_B} +\alias{optim_zipln_B} +\title{#' @importFrom glassoFast glassoFast +optim_zipln_Omega_sparse <- function(M, X, B, S, rho) { +n <- nrow(M); p <- ncol(M) +glassoFast::glassoFast( crossprod(M - X \%*\% B)/n + diag(colMeans(S * S), p, p), rho = rho )$wi +}} +\usage{ +optim_zipln_B(M, X, Omega, config) +} +\description{ +#' @importFrom glassoFast glassoFast +optim_zipln_Omega_sparse <- function(M, X, B, S, rho) { +n <- nrow(M); p <- ncol(M) +glassoFast::glassoFast( crossprod(M - X \%*\% B)/n + diag(colMeans(S * S), p, p), rho = rho )$wi +} +} diff --git a/tests/testthat/test-zipln.R b/tests/testthat/test-zipln.R index 0de02852..3bfa433c 100644 --- a/tests/testthat/test-zipln.R +++ b/tests/testthat/test-zipln.R @@ -1,12 +1,20 @@ context("test-pln") require(purrr) + +#### TODO +#### Should work without intercept +#### Should work with covariates + data(trichoptera) trichoptera <- prepare_data(trichoptera$Abundance[1:20, 1:5], trichoptera$Covariate[1:20, ]) test_that("ZIPLN: Check that ZIPLN is running and robust", { - expect_is(ZIPLN(Abundance ~ 1, data = trichoptera), "ZIPLNfit") + expect_is(zi_single <- ZIPLN(Abundance ~ 1, data = trichoptera), "ZIPLNfit") + expect_is(zi_row <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "row"), "ZIPLNfit") + expect_is(zi_col <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "col"), "ZIPLNfit") + expect_is(zi_covar <- ZIPLN(Abundance ~ 1 | Wind, data = trichoptera), "ZIPLNfit") expect_is(ZIPLN(Abundance ~ 0, data = trichoptera), "ZIPLNfit") @@ -16,7 +24,7 @@ test_that("ZIPLN: Check that ZIPLN is running and robust", { ZIPLN(Abundance ~ Wind, data = trichoptera)$fitted) }) -test_that("PLN: Routine comparison between the different covariance models", { +test_that("ZIPLN: Routine comparison between the different covariance models", { model_full <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(covariance = "full" , trace = 0)) model_diagonal <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(covariance = "diagonal" , trace = 0)) model_spherical <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(covariance = "spherical", trace = 0)) From c09eff60611a8cccce13044b2b9f5495c1f03a55 Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Tue, 16 Jan 2024 10:54:20 +0100 Subject: [PATCH 11/36] passing firt bunch of tests --- DESCRIPTION | 2 - NAMESPACE | 1 - R/RcppExports.R | 4 +- R/ZIPLN.R | 17 ++-- R/ZIPLNfit-class.R | 61 +++++++++--- R/optim-zipln.R | 165 --------------------------------- R/utils.R | 2 + man/ZIPLN.Rd | 17 ++-- man/ZIPLNfit.Rd | 12 ++- man/ZIPLNfit_diagonal.Rd | 1 + man/ZIPLNfit_fixed.Rd | 3 +- man/ZIPLNfit_fixedcov.Rd | 1 + man/ZIPLNfit_spherical.Rd | 1 + man/optim_zipln_B.Rd | 19 ---- src/RcppExports.cpp | 8 +- src/optim_zi-pln.cpp | 50 ++-------- tests/testthat/test-zipln.R | 17 ++-- tests/testthat/test-ziplnfit.R | 96 +++++++++---------- 18 files changed, 159 insertions(+), 318 deletions(-) delete mode 100644 R/optim-zipln.R delete mode 100644 man/optim_zipln_B.Rd diff --git a/DESCRIPTION b/DESCRIPTION index 68214c4d..f2b1eed9 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -38,7 +38,6 @@ Imports: R6, glassoFast, pscl, - glmnet, Matrix, Rcpp, nloptr, @@ -95,7 +94,6 @@ Collate: 'import_utils.R' 'mollusk.R' 'oaks.R' - 'optim-zipln.R' 'plot_utils.R' 'scRNA.R' 'trichoptera.R' diff --git a/NAMESPACE b/NAMESPACE index 238c5ff5..cd4a39a2 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -77,7 +77,6 @@ importFrom(corrplot,corrplot) importFrom(future.apply,future_lapply) importFrom(future.apply,future_sapply) importFrom(glassoFast,glassoFast) -importFrom(glmnet,glmnet) importFrom(grid,nullGrob) importFrom(grid,textGrob) importFrom(gridExtra,arrangeGrob) diff --git a/R/RcppExports.R b/R/RcppExports.R index b320c8c3..6f186120 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -65,8 +65,8 @@ optim_zipln_B_dense <- function(M, X) { .Call('_PLNmodels_optim_zipln_B_dense', PACKAGE = 'PLNmodels', M, X) } -optim_zipln_zipar_covar <- function(init_B0, X, R, configuration) { - .Call('_PLNmodels_optim_zipln_zipar_covar', PACKAGE = 'PLNmodels', init_B0, X, R, configuration) +optim_zipln_zipar_covar <- function(init_B0, X0, R, configuration) { + .Call('_PLNmodels_optim_zipln_zipar_covar', PACKAGE = 'PLNmodels', init_B0, X0, R, configuration) } optim_zipln_R <- function(Y, X, O, M, S, Pi) { diff --git a/R/ZIPLN.R b/R/ZIPLN.R index 1000e590..b857418d 100644 --- a/R/ZIPLN.R +++ b/R/ZIPLN.R @@ -17,12 +17,17 @@ #' data(trichoptera) #' trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) #' myPLN <- PLN(Abundance ~ 1, data = trichoptera) -#' myZIPLN_1 <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "single") -#' myZIPLN_2 <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "row") -#' myZIPLN_3 <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "col") -#' myZIPLN_3 <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "col") -#' myPLN_full$criteria # better BIC with sparse version -#' myPLN_sparse$criteria +#' myZIPLN_single <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "single") +#' myZIPLN_row <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "row") +#' myZIPLN_col <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "col") +#' myZIPLN_covar <- ZIPLN(Abundance ~ 1 | 1 + Wind, data = trichoptera) +#' dplyr::bind_rows( +#' myPLN$criteria, +#' myZIPLN_single$criteria, +#' myZIPLN_row$criteria, +#' myZIPLN_col$criteria, +#' myZIPLN_covar$criteria +#' ) #' @seealso The class [`ZIPLNfit`] #' @importFrom stats model.frame model.matrix model.response model.offset terms as.formula #' @export diff --git a/R/ZIPLNfit-class.R b/R/ZIPLNfit-class.R index 589749e0..182fc6f1 100644 --- a/R/ZIPLNfit-class.R +++ b/R/ZIPLNfit-class.R @@ -19,13 +19,16 @@ #' @importFrom R6 R6Class #' #' @examples +#' @examples #' \dontrun{ -#' data(scRNA) -#' # data subsample: only 100 random cell and the 50 most varying transcript -#' subset <- sample.int(nrow(scRNA), 100) -#' myZIPLN_1 <- ZIPLN(counts[, 1:50] ~ 1 + offset(log(total_counts)), zi = "single", subset = subset, data = scRNA) -#' myZIPLN_2 <- ZIPLN(counts[, 1:50] ~ 1 + offset(log(total_counts)), zi = "row", subset = subset, data = scRNA) +#' # See other examples in function ZIPLN +#' data(trichoptera) +#' trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) +#' myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera) +#' class(myPLN) +#' print(myPLN) #' } +#' ZIPLNfit <- R6Class( classname = "ZIPLNfit", ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @@ -74,6 +77,7 @@ ZIPLNfit <- R6Class( ## save the formula call as specified by the user private$formula <- formula private$X <- covariates$PLN + private$X0 <- covariates$ZI ## initialize the covariance model private$covariance <- control$covariance private$ziparam <- control$ziparam @@ -88,7 +92,7 @@ ZIPLNfit <- R6Class( suppressWarnings( zip_out <- switch(control$ziparam, "row" = pscl::zeroinfl(y ~ 0 + covariates$PLN | 0 + factor(1:n), offset = offsets[, j]), - "covar" = pscl::zeroinfl(y ~ 0 + covariates$PLN | covariates$ZI , offset = offsets[, j]), + "covar" = pscl::zeroinfl(y ~ 0 + covariates$PLN | 0 + covariates$ZI , offset = offsets[, j]), pscl::zeroinfl(y ~ 0 + covariates$PLN | 1, offset = offsets[, j])) # offset only for the count model ) B0[,j] <- coef(zip_out, "zero") @@ -120,13 +124,12 @@ ZIPLNfit <- R6Class( private$S <- matrix(.1, n, p) ## Link to functions performing the optimization - private$optimizer$main <- optimize_zi private$optimizer$B <- function(M, X, Omega, control) optim_zipln_B_dense(M, X) private$optimizer$zi <- switch( control$ziparam, - "single" = function(init_B0, X, R, config) list(Pi = matrix( mean(R), n, p) , B0 = matrix(NA, d0, p)), - "row" = function(init_B0, X, R, config) list(Pi = matrix(rowMeans(R), n, p) , B0 = matrix(NA, d0, p)), - "col" = function(init_B0, X, R, config) list(Pi = matrix(colMeans(R), n, p, byrow = TRUE), B0 = matrix(NA, d0, p)), + "single" = function(init_B0, X0, R, config) list(Pi = matrix( mean(R), n, p) , B0 = matrix(NA, d0, p)), + "row" = function(init_B0, X0, R, config) list(Pi = matrix(rowMeans(R), n, p) , B0 = matrix(NA, d0, p)), + "col" = function(init_B0, X0, R, config) list(Pi = matrix(colMeans(R), n, p, byrow = TRUE), B0 = matrix(NA, d0, p)), "covar" = optim_zipln_zipar_covar ) private$optimizer$Omega <- optim_zipln_Omega_full @@ -168,7 +171,7 @@ ZIPLNfit <- R6Class( ) optim_new_zipar <- private$optimizer$zi( - init_B0 = parameters$B0, X = data$X, R = parameters$R, config = control + init_B0 = parameters$B0, X0 = data$X0, R = parameters$R, config = control ) new_B0 <- optim_new_zipar$B0 new_Pi <- optim_new_zipar$Pi @@ -383,6 +386,7 @@ ZIPLNfit <- R6Class( #' #' @examples #' \dontrun{ +#' # See other examples in function ZIPLN #' data(trichoptera) #' trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) #' myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(covariance = "diagonal")) @@ -436,6 +440,7 @@ ZIPLNfit_diagonal <- R6Class( #' #' @examples #' \dontrun{ +#' # See other examples in function ZIPLN #' data(trichoptera) #' trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) #' myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(covariance = "spherical")) @@ -492,6 +497,7 @@ ZIPLNfit_spherical <- R6Class( #' #' @examples #' \dontrun{ +#' # See other examples in function ZIPLN #' data(trichoptera) #' trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) #' myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, contro = ZIPLN_param(Omega = diag(ncol(trichoptera$Abundance)))) @@ -552,6 +558,7 @@ ZIPLNfit_fixed <- R6Class( #' #' @examples #' \dontrun{ +#' # See other examples in function ZIPLN #' data(trichoptera) #' trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) #' myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, control= ZIPLN_param(penalty = 0.2)) @@ -625,3 +632,35 @@ parameter_list_converged <- function(oldp, newp, xtol_abs = NULL, xtol_rel = NUL FALSE } +#' #' @importFrom glmnet glmnet +#' optim_zipln_B <- function(M, X, Omega, config) { +#' +#' if(config$lambda > 0) { +#' if (!is.null(config$ind_intercept)) { +#' m_bar <- colMeans(M) +#' x_bar <- colMeans(X[, -config$ind_intercept]) +#' X <- scale(X[, -config$ind_intercept], x_bar, FALSE) +#' M <- scale(M, m_bar, FALSE) +#' } +#' p <- ncol(M); d <- ncol(X) +#' if (d > 0) { +#' Omega12 <- chol(Omega) +#' y <- as.vector(M %*% t(Omega12)) +#' x <- kronecker(Omega12, X) +#' glmnet_out <- glmnet(x, y, lambda = config$lambda, intercept = FALSE, standardize = FALSE) +#' B <- matrix(as.numeric(glmnet_out$beta), nrow = d, ncol = p) +#' } else { +#' B <- matrix(0, nrow = d, ncol = p) +#' } +#' +#' if (!is.null(config$ind_intercept)) { +#' mu0 <- m_bar - as.vector(crossprod(B, x_bar)) +#' B <- rbind(mu0, B) +#' } +#' +#' } else { +#' B <- optim_zipln_B_dense(M, X) +#' } +#' B +#' } +#' diff --git a/R/optim-zipln.R b/R/optim-zipln.R deleted file mode 100644 index 1bc9a836..00000000 --- a/R/optim-zipln.R +++ /dev/null @@ -1,165 +0,0 @@ -# ZI optimization: -# -# init_parameters: named list(B, B0, Omega, M, S2, R) -# Y, X, O: observed data, constant -# configuration: named list(algorithm, xtol_rel, xtol_abs, ftol_rel, ftol_abs, maxeval, maxtime) -# -# Returns named list(parameters: named list, nb_iter: int, stop_reason: str) -# -# Configuration : -# Forwarded to nloptr iterative steps (c,e,f). -# Except for algorithm, the keys are optional termination criteria. Not all of them are needed. -# A missing key or a key set to a negative number means disabled. -# xtol_abs: double for every parameter, or named list with values for each parameter. -# -# xtol_abs, xtol_rel, maxeval are also used for overall loop. -# ftol_abs and ftol_rel could maybe be used if defined on J(params), but I'm not sure. -# -# Dimensions are checked only on C++ side. -optimize_zi <- function(init_parameters, Y, X, O, configuration) { - - n <- nrow(Y); p <- ncol(Y); d <- ncol(X) - - # Link to the approximate function to optimize Omega ,depending on the target structure - optim_zipln_Omega <- switch( - configuration$covariance, - "spherical" = optim_zipln_Omega_spherical, - "diagonal" = optim_zipln_Omega_diagonal, - "full" = optim_zipln_Omega_full, - "sparse" = function(M, X, B, S) optim_zipln_Omega_sparse(M, X, B, S, rho = configuration$rho) - ) - - optim_zipln_zipar <- switch( - configuration$ziparam, - "single" = function(init_B0, X, R, config) list(Pi = matrix( mean(R), n, p) , B0 = matrix(NA, d, p)), - "row" = function(init_B0, X, R, config) list(Pi = matrix(rowMeans(R), n, p) , B0 = matrix(NA, d, p)), - "col" = function(init_B0, X, R, config) list(Pi = matrix(colMeans(R), n, p, byrow = TRUE), B0 = matrix(NA, d, p)), - "covar" = optim_zipln_zipar_covar - ) - - maxit_out <- if("maxit_out" %in% names(configuration)) { configuration$maxit_out } else { 50 } - - # Main loop - nb_iter <- 0 - parameters <- init_parameters - criterion <- vector("numeric", maxit_out) - vloglik <- -Inf; objective <- Inf - repeat { - # Check maxeval - if(maxit_out >= 0 && nb_iter >= maxit_out) { - return(list( - parameters = parameters, - nb_iter = nb_iter, - stop_reason = "maximum number of iterations reached", - criterion = criterion[1:nb_iter], - vloglik = vloglik - )) - } - - # M Step - new_Omega <- optim_zipln_Omega( - M = parameters$M, X = X, B = parameters$B, S = parameters$S - ) - new_B <- optim_zipln_B( - M = parameters$M, X = X, Omega = new_Omega, configuration - ) - - optim_new_zipar <- optim_zipln_zipar( - init_B0 = parameters$B0, X = X, R = parameters$R, config = configuration - ) - new_B0 <- optim_new_zipar$B0 - new_Pi <- optim_new_zipar$Pi - - # VE Step - new_R <- optim_zipln_R( - Y = Y, X = X, O = O, M = parameters$M, S = parameters$S, Pi = new_Pi - ) - optim_new_M <- optim_zipln_M( - init_M = parameters$M, - Y = Y, X = X, O = O, R = new_R, S = parameters$S, B = new_B, Omega = new_Omega, - configuration = configuration - ) - new_M <- optim_new_M$M - optim_new_S <- optim_zipln_S( - init_S = parameters$S, - O = O, M = new_M, R = new_R, B = new_B, diag_Omega = diag(new_Omega), - configuration = configuration - ) - new_S <- optim_new_S$S - # print(optim_new_S$status) - - # Check convergence - new_parameters <- list( - Omega = new_Omega, B = new_B, B0 = new_B0, Pi = new_Pi, - R = new_R, M = new_M, S = new_S - ) - nb_iter <- nb_iter + 1 - - vloglik <- zipln_vloglik( - Y, X, O, new_Pi, new_Omega, new_B, new_R, new_M, new_S - ) - - criterion[nb_iter] <- new_objective <- -sum(vloglik) - - objective_converged <- - (objective - new_objective) < configuration$ftol_out | - (objective - new_objective)/abs(new_objective) < configuration$ftol_out - - parameters_converged <- parameter_list_converged( - parameters, new_parameters, - xtol_abs = configuration$xtol_abs, xtol_rel = configuration$xtol_rel - ) - - if (parameters_converged | objective_converged) { - return(list( - parameters = new_parameters, - nb_iter = nb_iter, - stop_reason = "converged", - criterion = criterion[1:nb_iter], - vloglik = vloglik - )) - } - - parameters <- new_parameters - objective <- new_objective - } -} - -#' #' @importFrom glassoFast glassoFast -#' optim_zipln_Omega_sparse <- function(M, X, B, S, rho) { -#' n <- nrow(M); p <- ncol(M) -#' glassoFast::glassoFast( crossprod(M - X %*% B)/n + diag(colMeans(S * S), p, p), rho = rho )$wi -#' } - -#' @importFrom glmnet glmnet -optim_zipln_B <- function(M, X, Omega, config) { - - if(config$lambda > 0) { - if (!is.null(config$ind_intercept)) { - m_bar <- colMeans(M) - x_bar <- colMeans(X[, -config$ind_intercept]) - X <- scale(X[, -config$ind_intercept], x_bar, FALSE) - M <- scale(M, m_bar, FALSE) - } - p <- ncol(M); d <- ncol(X) - if (d > 0) { - Omega12 <- chol(Omega) - y <- as.vector(M %*% t(Omega12)) - x <- kronecker(Omega12, X) - glmnet_out <- glmnet(x, y, lambda = config$lambda, intercept = FALSE, standardize = FALSE) - B <- matrix(as.numeric(glmnet_out$beta), nrow = d, ncol = p) - } else { - B <- matrix(0, nrow = d, ncol = p) - } - - if (!is.null(config$ind_intercept)) { - mu0 <- m_bar - as.vector(crossprod(B, x_bar)) - B <- rbind(mu0, B) - } - - } else { - B <- optim_zipln_B_dense(M, X) - } - B -} - diff --git a/R/utils.R b/R/utils.R index e5d7fd3c..f7dd875b 100644 --- a/R/utils.R +++ b/R/utils.R @@ -203,6 +203,8 @@ extract_model_zi <- function(call, envir, xlev = NULL) { frame <- eval(call_frame, envir) ## create the set of matrices to fit the PLN model Y <- frame[[1L]] ## model.response oversimplifies into a numeric when a single variable is involved + ## model.response oversimplifies into a numeric when a single variable is involved + if (is.null(dim(Y))) Y <- matrix(Y, nrow = length(Y), ncol = 1) if (ncol(Y) == 1 & is.null(colnames(Y))) colnames(Y) <- "Y" ## Extract the design matrices for ZI and PLN components diff --git a/man/ZIPLN.Rd b/man/ZIPLN.Rd index 64d9ba07..8c0f58df 100644 --- a/man/ZIPLN.Rd +++ b/man/ZIPLN.Rd @@ -36,12 +36,17 @@ Fit the multivariate Zero Inflated Poisson lognormal model with a variational al data(trichoptera) trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) myPLN <- PLN(Abundance ~ 1, data = trichoptera) -myZIPLN_1 <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "single") -myZIPLN_2 <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "row") -myZIPLN_3 <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "col") -myZIPLN_3 <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "col") -myPLN_full$criteria # better BIC with sparse version -myPLN_sparse$criteria +myZIPLN_single <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "single") +myZIPLN_row <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "row") +myZIPLN_col <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "col") +myZIPLN_covar <- ZIPLN(Abundance ~ 1 | 1 + Wind, data = trichoptera) +dplyr::bind_rows( + myPLN$criteria, + myZIPLN_single$criteria, + myZIPLN_row$criteria, + myZIPLN_col$criteria, + myZIPLN_covar$criteria +) } \seealso{ The class \code{\link{ZIPLNfit}} diff --git a/man/ZIPLNfit.Rd b/man/ZIPLNfit.Rd index 0d2daff4..249e2927 100644 --- a/man/ZIPLNfit.Rd +++ b/man/ZIPLNfit.Rd @@ -13,12 +13,14 @@ Fields are accessed via active binding and cannot be changed by the user. } \examples{ \dontrun{ -data(scRNA) -# data subsample: only 100 random cell and the 50 most varying transcript -subset <- sample.int(nrow(scRNA), 100) -myZIPLN_1 <- ZIPLN(counts[, 1:50] ~ 1 + offset(log(total_counts)), zi = "single", subset = subset, data = scRNA) -myZIPLN_2 <- ZIPLN(counts[, 1:50] ~ 1 + offset(log(total_counts)), zi = "row", subset = subset, data = scRNA) +# See other examples in function ZIPLN +data(trichoptera) +trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) +myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera) +class(myPLN) +print(myPLN) } + } \section{Active bindings}{ \if{html}{\out{
}} diff --git a/man/ZIPLNfit_diagonal.Rd b/man/ZIPLNfit_diagonal.Rd index 61ac5206..e6f292d6 100644 --- a/man/ZIPLNfit_diagonal.Rd +++ b/man/ZIPLNfit_diagonal.Rd @@ -10,6 +10,7 @@ An R6 Class to represent a ZIPLNfit in a standard, general framework, with diago } \examples{ \dontrun{ +# See other examples in function ZIPLN data(trichoptera) trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(covariance = "diagonal")) diff --git a/man/ZIPLNfit_fixed.Rd b/man/ZIPLNfit_fixed.Rd index d6cc0a7e..e47252df 100644 --- a/man/ZIPLNfit_fixed.Rd +++ b/man/ZIPLNfit_fixed.Rd @@ -10,9 +10,10 @@ An R6 Class to represent a ZIPLNfit in a standard, general framework, with fixed } \examples{ \dontrun{ +# See other examples in function ZIPLN data(trichoptera) trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) -myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera) +myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, contro = ZIPLN_param(Omega = diag(ncol(trichoptera$Abundance)))) class(myPLN) print(myPLN) } diff --git a/man/ZIPLNfit_fixedcov.Rd b/man/ZIPLNfit_fixedcov.Rd index c73dd4d1..37dbf475 100644 --- a/man/ZIPLNfit_fixedcov.Rd +++ b/man/ZIPLNfit_fixedcov.Rd @@ -10,6 +10,7 @@ An R6 Class to represent a ZIPLNfit in a standard, general framework, with spars } \examples{ \dontrun{ +# See other examples in function ZIPLN data(trichoptera) trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, control= ZIPLN_param(penalty = 0.2)) diff --git a/man/ZIPLNfit_spherical.Rd b/man/ZIPLNfit_spherical.Rd index 08664797..74cfc688 100644 --- a/man/ZIPLNfit_spherical.Rd +++ b/man/ZIPLNfit_spherical.Rd @@ -10,6 +10,7 @@ An R6 Class to represent a ZIPLNfit in a standard, general framework, with spher } \examples{ \dontrun{ +# See other examples in function ZIPLN data(trichoptera) trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(covariance = "spherical")) diff --git a/man/optim_zipln_B.Rd b/man/optim_zipln_B.Rd deleted file mode 100644 index 770f11ee..00000000 --- a/man/optim_zipln_B.Rd +++ /dev/null @@ -1,19 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/optim-zipln.R -\name{optim_zipln_B} -\alias{optim_zipln_B} -\title{#' @importFrom glassoFast glassoFast -optim_zipln_Omega_sparse <- function(M, X, B, S, rho) { -n <- nrow(M); p <- ncol(M) -glassoFast::glassoFast( crossprod(M - X \%*\% B)/n + diag(colMeans(S * S), p, p), rho = rho )$wi -}} -\usage{ -optim_zipln_B(M, X, Omega, config) -} -\description{ -#' @importFrom glassoFast glassoFast -optim_zipln_Omega_sparse <- function(M, X, B, S, rho) { -n <- nrow(M); p <- ncol(M) -glassoFast::glassoFast( crossprod(M - X \%*\% B)/n + diag(colMeans(S * S), p, p), rho = rho )$wi -} -} diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp index 49414744..c464d23f 100644 --- a/src/RcppExports.cpp +++ b/src/RcppExports.cpp @@ -237,16 +237,16 @@ BEGIN_RCPP END_RCPP } // optim_zipln_zipar_covar -Rcpp::List optim_zipln_zipar_covar(const arma::mat& init_B0, const arma::mat& X, const arma::mat& R, const Rcpp::List& configuration); -RcppExport SEXP _PLNmodels_optim_zipln_zipar_covar(SEXP init_B0SEXP, SEXP XSEXP, SEXP RSEXP, SEXP configurationSEXP) { +Rcpp::List optim_zipln_zipar_covar(const arma::mat& init_B0, const arma::mat& X0, const arma::mat& R, const Rcpp::List& configuration); +RcppExport SEXP _PLNmodels_optim_zipln_zipar_covar(SEXP init_B0SEXP, SEXP X0SEXP, SEXP RSEXP, SEXP configurationSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< const arma::mat& >::type init_B0(init_B0SEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type X(XSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type X0(X0SEXP); Rcpp::traits::input_parameter< const arma::mat& >::type R(RSEXP); Rcpp::traits::input_parameter< const Rcpp::List& >::type configuration(configurationSEXP); - rcpp_result_gen = Rcpp::wrap(optim_zipln_zipar_covar(init_B0, X, R, configuration)); + rcpp_result_gen = Rcpp::wrap(optim_zipln_zipar_covar(init_B0, X0, R, configuration)); return rcpp_result_gen; END_RCPP } diff --git a/src/optim_zi-pln.cpp b/src/optim_zi-pln.cpp index 0f6c3cc7..24e371ab 100644 --- a/src/optim_zi-pln.cpp +++ b/src/optim_zi-pln.cpp @@ -19,7 +19,6 @@ arma::vec zipln_vloglik( const arma::mat & M, // (n,p) const arma::mat & S // (n,p) ) { - const arma::uword n = Y.n_rows; const arma::uword p = Y.n_cols; const arma::mat S2 = S % S ; @@ -69,7 +68,6 @@ arma::mat optim_zipln_Omega_diagonal( const arma::mat & S // (n,p) ) { const arma::uword n = M.n_rows; - const arma::uword p = M.n_cols; return arma::diagmat(double(n) / sum( pow(M - X * B, 2) + S % S, 0)) ; } @@ -84,8 +82,8 @@ arma::mat optim_zipln_B_dense( // [[Rcpp::export]] Rcpp::List optim_zipln_zipar_covar( - const arma::mat & init_B0, // (d,p) - const arma::mat & X, // covariates (n,d) + const arma::mat & init_B0, // (d0,p) + const arma::mat & X0, // covariates (n,d0) const arma::mat & R, // (n,p) const Rcpp::List & configuration // List of config values ; xtol_abs is B0 only (double or mat) ) { @@ -96,26 +94,17 @@ Rcpp::List optim_zipln_zipar_covar( metadata.map(parameters.data()) = init_B0; auto optimizer = new_nlopt_optimizer(configuration, parameters.size()); - if(configuration.containsElementNamed("xtol_abs")) { - SEXP value = configuration["xtol_abs"]; - if(Rcpp::is(value)) { - set_uniform_xtol_abs(optimizer.get(), Rcpp::as(value)); - } else { - auto packed = std::vector(metadata.packed_size); - metadata.map(packed.data()) = Rcpp::as(value); - set_per_value_xtol_abs(optimizer.get(), packed); - } - } + set_uniform_xtol_abs(optimizer.get(), Rcpp::as(configuration["xtol_abs"])); - const arma::mat Xt_R = X.t() * R; + const arma::mat Xt_R = X0.t() * R; // Optimize - auto objective_and_grad = [&metadata, &X, &R, &Xt_R](const double * params, double * grad) -> double { + auto objective_and_grad = [&metadata, &X0, &R, &Xt_R](const double * params, double * grad) -> double { const arma::mat B0 = metadata.map(params); - arma::mat e_mu0 = exp(X * B0); + arma::mat e_mu0 = exp(X0 * B0); double objective = -trace(Xt_R.t() * B0) + accu(log(1. + e_mu0)); - metadata.map(grad) = -Xt_R + X.t() * (e_mu0 % pow(1. + e_mu0, -1)) ; + metadata.map(grad) = -Xt_R + X0.t() * (e_mu0 % pow(1. + e_mu0, -1)) ; return objective; }; OptimizerResult result = minimize_objective_on_parameters(optimizer.get(), objective_and_grad, parameters); @@ -125,7 +114,7 @@ Rcpp::List optim_zipln_zipar_covar( Rcpp::Named("status") = static_cast(result.status), Rcpp::Named("iterations") = result.nb_iterations, Rcpp::Named("B0") = B0, - Rcpp::Named("Pi") = logistic(X * B0)); + Rcpp::Named("Pi") = logistic(X0 * B0)); } // [[Rcpp::export]] @@ -174,16 +163,7 @@ Rcpp::List optim_zipln_M( metadata.map(parameters.data()) = init_M; auto optimizer = new_nlopt_optimizer(configuration, parameters.size()); - if(configuration.containsElementNamed("xtol_abs")) { - SEXP value = configuration["xtol_abs"]; - if(Rcpp::is(value)) { - set_uniform_xtol_abs(optimizer.get(), Rcpp::as(value)); - } else { - auto packed = std::vector(metadata.packed_size); - metadata.map(packed.data()) = Rcpp::as(value); - set_per_value_xtol_abs(optimizer.get(), packed); - } - } + set_uniform_xtol_abs(optimizer.get(), Rcpp::as(configuration["xtol_abs"])); const arma::mat X_B = X * B; // (n,p) const arma::mat O_S2 = O + 0.5 * S % S; // (n,p) @@ -226,16 +206,7 @@ Rcpp::List optim_zipln_S( metadata.map(parameters.data()) = init_S; auto optimizer = new_nlopt_optimizer(configuration, parameters.size()); - if(configuration.containsElementNamed("xtol_abs")) { - SEXP value = configuration["xtol_abs"]; - if(Rcpp::is(value)) { - set_uniform_xtol_abs(optimizer.get(), Rcpp::as(value)); - } else { - auto packed = std::vector(metadata.packed_size); - metadata.map(packed.data()) = Rcpp::as(value); - set_per_value_xtol_abs(optimizer.get(), packed); - } - } + set_uniform_xtol_abs(optimizer.get(), Rcpp::as(configuration["xtol_abs"])); const arma::mat O_M = O + M; @@ -243,7 +214,6 @@ Rcpp::List optim_zipln_S( auto objective_and_grad = [&metadata, &O_M, &R, &diag_Omega](const double * params, double * grad) -> double { const arma::mat S = metadata.map(params); - arma::uword n = S.n_rows; arma::mat A = exp(O_M + 0.5 * S % S); // (n,p) // trace(1^T log(S)) == accu(log(S)). diff --git a/tests/testthat/test-zipln.R b/tests/testthat/test-zipln.R index 3bfa433c..5dec3c28 100644 --- a/tests/testthat/test-zipln.R +++ b/tests/testthat/test-zipln.R @@ -1,11 +1,6 @@ -context("test-pln") +context("test-zipln") require(purrr) - -#### TODO -#### Should work without intercept -#### Should work with covariates - data(trichoptera) trichoptera <- prepare_data(trichoptera$Abundance[1:20, 1:5], trichoptera$Covariate[1:20, ]) @@ -15,13 +10,19 @@ test_that("ZIPLN: Check that ZIPLN is running and robust", { expect_is(zi_row <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "row"), "ZIPLNfit") expect_is(zi_col <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "col"), "ZIPLNfit") expect_is(zi_covar <- ZIPLN(Abundance ~ 1 | Wind, data = trichoptera), "ZIPLNfit") + expect_is(zi_covar <- ZIPLN(Abundance ~ 0 + Wind | Wind, data = trichoptera), "ZIPLNfit") - expect_is(ZIPLN(Abundance ~ 0, data = trichoptera), "ZIPLNfit") + ## initialization could not work without any regressor... + expect_error(ZIPLN(Abundance ~ 0, data = trichoptera)) expect_is(ZIPLN(trichoptera$Abundance ~ 1), "ZIPLNfit") expect_equal(ZIPLN(trichoptera$Abundance ~ 1 + trichoptera$Wind)$fitted, ZIPLN(Abundance ~ Wind, data = trichoptera)$fitted) + + expect_is(model_sparse <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(penalty = 0.2, trace = 0)), "ZIPLNfit_sparse") + expect_is(model_fixed <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(Omega = zi_single$model_par$Omega, trace = 0)), "ZIPLNfit_fixed") + }) test_that("ZIPLN: Routine comparison between the different covariance models", { @@ -52,7 +53,7 @@ test_that("PLN is working with unnamed data matrix", { MMA <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(config_optim = list(algorithm = "MMA"))) CCSAQ <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(config_optim = list(algorithm = "CCSAQ"))) - LBFGS <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(config_optim = list(algorithm = "LBFGS"))) + LBFGS <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(config_optim = list(algorithm = "LBFGS_NOCEDAL"))) expect_equal(MMA$loglik, CCSAQ$loglik, tolerance = 1e-1) ## Almost equivalent, CCSAQ faster diff --git a/tests/testthat/test-ziplnfit.R b/tests/testthat/test-ziplnfit.R index 191c905e..8fea8402 100644 --- a/tests/testthat/test-ziplnfit.R +++ b/tests/testthat/test-ziplnfit.R @@ -1,4 +1,4 @@ -context("test-plnfit") +context("test-ziplnfit") data(trichoptera) trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) @@ -40,53 +40,53 @@ test_that("ZIPLN fit: check print message", { capture_output(model$print())) }) -test_that("PLN fit: Check prediction", { - - model1 <- ZIPLN(Abundance ~ 1, data = trichoptera, subset = 1:30) - model1_off <- ZIPLN(Abundance ~ 1 + offset(log(Offset)), data = trichoptera, subset = 1:30) - model2 <- ZIPLN(Abundance ~ Pressure, data = trichoptera, subset = 1:30) - - newdata <- trichoptera[31:49, ] - # newdata$Abundance <- NULL - - pred1 <- predict(model1, newdata = newdata, type = "response") - pred1_off <- predict(model1_off, newdata = newdata, type = "response") - pred2 <- predict(model2, newdata = newdata, type = "response") - pred2_ve <- predict(model2, newdata = newdata, type = "response", - responses = newdata$Abundance) - - ## predict returns fitted values if no data is provided - expect_equal(model2$predict(), model2$fitted) - - ## Adding covariates improves fit - expect_gt( - mean((newdata$Abundance - pred1)^2), - mean((newdata$Abundance - pred2)^2) - ) - - ## Doing one VE step improves fit - expect_gt( - mean((newdata$Abundance - pred2)^2), - mean((newdata$Abundance - pred2_ve)^2) - ) - - ## R6 methods - ## with offset, predictions should vary across samples - expect_gte(min(apply(pred1_off, 2, sd)), .Machine$double.eps) - newdata$Offset <- NULL - ## without offsets, predictions should be the same for all samples - expect_equal(unname(apply(pred1, 2, sd)), rep(0, ncol(pred1))) - - ## Unequal factor levels in train and prediction datasets - suppressWarnings( - toy_data <- prepare_data( - counts = matrix(c(1, 3, 1, 1), ncol = 1), - covariates = data.frame(Cov = c("A", "B", "A", "A")), - offset = rep(1, 4)) - ) - model <- PLN(Abundance ~ Cov + offset(log(Offset)), data = toy_data[1:2,]) - expect_length(predict(model, newdata = toy_data[3:4, ], type = "r"), 2L) -}) +# test_that("PLN fit: Check prediction", { +# +# model1 <- ZIPLN(Abundance ~ 1, data = trichoptera, subset = 1:30) +# model1_off <- ZIPLN(Abundance ~ 1 + offset(log(Offset)), data = trichoptera, subset = 1:30) +# model2 <- ZIPLN(Abundance ~ Pressure, data = trichoptera, subset = 1:30) +# +# newdata <- trichoptera[31:49, ] +# # newdata$Abundance <- NULL +# +# pred1 <- predict(model1, newdata = newdata, type = "response") +# pred1_off <- predict(model1_off, newdata = newdata, type = "response") +# pred2 <- predict(model2, newdata = newdata, type = "response") +# pred2_ve <- predict(model2, newdata = newdata, type = "response", +# responses = newdata$Abundance) +# +# ## predict returns fitted values if no data is provided +# expect_equal(model2$predict(), model2$fitted) +# +# ## Adding covariates improves fit +# expect_gt( +# mean((newdata$Abundance - pred1)^2), +# mean((newdata$Abundance - pred2)^2) +# ) +# +# ## Doing one VE step improves fit +# expect_gt( +# mean((newdata$Abundance - pred2)^2), +# mean((newdata$Abundance - pred2_ve)^2) +# ) +# +# ## R6 methods +# ## with offset, predictions should vary across samples +# expect_gte(min(apply(pred1_off, 2, sd)), .Machine$double.eps) +# newdata$Offset <- NULL +# ## without offsets, predictions should be the same for all samples +# expect_equal(unname(apply(pred1, 2, sd)), rep(0, ncol(pred1))) +# +# ## Unequal factor levels in train and prediction datasets +# suppressWarnings( +# toy_data <- prepare_data( +# counts = matrix(c(1, 3, 1, 1), ncol = 1), +# covariates = data.frame(Cov = c("A", "B", "A", "A")), +# offset = rep(1, 4)) +# ) +# model <- PLN(Abundance ~ Cov + offset(log(Offset)), data = toy_data[1:2,]) +# expect_length(predict(model, newdata = toy_data[3:4, ], type = "r"), 2L) +# }) From d6170c9c19cfc1db5372756d962575c525a4169b Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Tue, 16 Jan 2024 11:06:22 +0100 Subject: [PATCH 12/36] slight modifs to pass test --- NAMESPACE | 1 - R/ZIPLNfit-class.R | 3 +- R/ZIPLNfits-S3methods.R | 10 ++--- R/utils.R | 70 +++++++++++++++-------------- man/ZIPLNfit_fixed.Rd | 3 +- man/coef.ZIPLNfit.Rd | 6 +-- man/compute_ZIPLN_starting_point.Rd | 39 ---------------- 7 files changed, 48 insertions(+), 84 deletions(-) delete mode 100644 man/compute_ZIPLN_starting_point.Rd diff --git a/NAMESPACE b/NAMESPACE index cd4a39a2..d0e323f3 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -49,7 +49,6 @@ export(ZIPLN) export(ZIPLN_param) export(coefficient_path) export(compute_PLN_starting_point) -export(compute_ZIPLN_starting_point) export(compute_offset) export(extract_probs) export(getBestModel) diff --git a/R/ZIPLNfit-class.R b/R/ZIPLNfit-class.R index 182fc6f1..f1bd4c03 100644 --- a/R/ZIPLNfit-class.R +++ b/R/ZIPLNfit-class.R @@ -500,7 +500,8 @@ ZIPLNfit_spherical <- R6Class( #' # See other examples in function ZIPLN #' data(trichoptera) #' trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) -#' myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, contro = ZIPLN_param(Omega = diag(ncol(trichoptera$Abundance)))) +#' myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, +#' control = ZIPLN_param(Omega = diag(ncol(trichoptera$Abundance)))) #' class(myPLN) #' print(myPLN) #' } diff --git a/R/ZIPLNfits-S3methods.R b/R/ZIPLNfits-S3methods.R index 4cef0874..130621b3 100644 --- a/R/ZIPLNfits-S3methods.R +++ b/R/ZIPLNfits-S3methods.R @@ -30,8 +30,8 @@ isZIPLNfit <- function(Robject) {inherits(Robject, "ZIPLNfit")} #' @name coef.ZIPLNfit #' #' @param object an R6 object with class [`ZIPLNfit`] -#' @param type type of parameter that should be extracted. Either "mainPLN" (default) for \deqn{B}, -#' "mainZI" (default) for \deqn{B0}, "precision" for \deqn{\Omega}, "covariance" for \deqn{\Sigma} +#' @param type type of parameter that should be extracted. Either "count" (default) for \deqn{B}, +#' "zero" for \deqn{B0}, "precision" for \deqn{\Omega}, "covariance" for \deqn{\Sigma} #' @param ... additional parameters for S3 compatibility. Not used #' @return A matrix of coefficients extracted from the ZIPLNfit model. #' @@ -45,11 +45,11 @@ isZIPLNfit <- function(Robject) {inherits(Robject, "ZIPLNfit")} #' myPLN <- ZIPLN(counts[, 1:50] ~ 1 + offset(log(total_counts)), subset = subset, data = scRNA) #' #' @export -coef.ZIPLNfit <- function(object, type = c("mainPLN", "mainZI", "precision", "covariance"), ...) { +coef.ZIPLNfit <- function(object, type = c("count", "zero", "precision", "covariance"), ...) { stopifnot(isZIPLNfit(object)) switch(match.arg(type), - mainPLN = object$model_par$B, - mainZI = object$model_par$B0, + count = object$model_par$B, + zero = object$model_par$B0, precision = object$model_par$Omega, covariance = object$model_par$Sigma) } diff --git a/R/utils.R b/R/utils.R index f7dd875b..2206b575 100644 --- a/R/utils.R +++ b/R/utils.R @@ -352,38 +352,40 @@ compute_PLN_starting_point <- function(Y, X, O, w, s = 0.1) { S = matrix(s, n, p)) } -#' Helper function for PLN initialization. -#' -#' @description -#' Barebone function to compute starting points for B, M and S when fitting a PLN. Mostly intended for internal use. -#' -#' @param Y Response count matrix -#' @param X Covariate matrix -#' @param O Offset matrix (in log-scale) -#' @param w Weight vector (defaults to 1) -#' @param s Scale parameter for S (defaults to 0.1) -#' @return a named list of starting values for model parameter B and variational parameters M and S used in the iterative optimization algorithm of [PLN()] -#' -#' @details The default strategy to estimate B and M is to fit a linear model with covariates `X` to the response count matrix (after adding a pseudocount of 1, scaling by the offset and taking the log). The regression matrix is used to initialize `B` and the residuals to initialize `M`. `S` is initialized as a constant conformable matrix with value `s`. -#' -#' @rdname compute_ZIPLN_starting_point -#' @examples -#' \dontrun{ -#' data(barents) -#' Y <- barents$Abundance -#' X <- model.matrix(Abundance ~ Latitude + Longitude + Depth + Temperature, data = barents) -#' O <- log(barents$Offset) -#' w <-- rep(1, nrow(Y)) -#' compute_ZIPLN_starting_point(Y, X, O, w) +#' #' Helper function for PLN initialization. +#' #' +#' #' @description +#' #' Barebone function to compute starting points for B, M and S when fitting a PLN. Mostly intended for internal use. +#' #' +#' #' @param Y Response count matrix +#' #' @param X Design matrix for the count (PLN) component +#' #' @param X0 Design matrix for the zero (Bernoulli) component +#' #' @param O Offset matrix (in log-scale) +#' #' @param w Weight vector (defaults to 1) +#' #' @param s Scale parameter for S (defaults to 0.1) +#' #' @return a named list of starting values for model parameter B and variational parameters M and S used in the iterative optimization algorithm of [PLN()] +#' #' +#' #' @details The default strategy to estimate B and M is to fit a linear model with covariates `X` to the response count matrix (after adding a pseudocount of 1, scaling by the offset and taking the log). The regression matrix is used to initialize `B` and the residuals to initialize `M`. `S` is initialized as a constant conformable matrix with value `s`. +#' #' +#' #' @rdname compute_ZIPLN_starting_point +#' #' @examples +#' #' \dontrun{ +#' #' data(barents) +#' #' Y <- barents$Abundance +#' #' X <- model.matrix(Abundance ~ 1 + Temperature, data = barents) +#' #' X0 <- model.matrix(Abundance ~ 0 + Longitude, data = barents) +#' #' O <- log(barents$Offset) +#' #' w <-- rep(1, nrow(Y)) +#' #' compute_ZIPLN_starting_point(Y, X, O, w) +#' #' } +#' #' +#' #' @importFrom stats lm.fit +#' #' @export +#' compute_ZIPLN_starting_point <- function(Y, X, X0, O, w, s = 0.1) { +#' # Y = responses, X = covariates, O = offsets (in log scale), w = weights +#' n <- nrow(Y); p <- ncol(Y); d <- ncol(X) +#' fits <- lm.fit(w * X, w * log((1 + Y)/exp(O))) +#' list(B = matrix(fits$coefficients, d, p), +#' M = matrix(fits$residuals, n, p), +#' S = matrix(s, n, p)) #' } -#' -#' @importFrom stats lm.fit -#' @export -compute_ZIPLN_starting_point <- function(Y, X, X0, O, w, s = 0.1) { - # Y = responses, X = covariates, O = offsets (in log scale), w = weights - n <- nrow(Y); p <- ncol(Y); d <- ncol(X) - fits <- lm.fit(w * X, w * log((1 + Y)/exp(O))) - list(B = matrix(fits$coefficients, d, p), - M = matrix(fits$residuals, n, p), - S = matrix(s, n, p)) -} diff --git a/man/ZIPLNfit_fixed.Rd b/man/ZIPLNfit_fixed.Rd index e47252df..4a80de5b 100644 --- a/man/ZIPLNfit_fixed.Rd +++ b/man/ZIPLNfit_fixed.Rd @@ -13,7 +13,8 @@ An R6 Class to represent a ZIPLNfit in a standard, general framework, with fixed # See other examples in function ZIPLN data(trichoptera) trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) -myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, contro = ZIPLN_param(Omega = diag(ncol(trichoptera$Abundance)))) +myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, + control = ZIPLN_param(Omega = diag(ncol(trichoptera$Abundance)))) class(myPLN) print(myPLN) } diff --git a/man/coef.ZIPLNfit.Rd b/man/coef.ZIPLNfit.Rd index 73c73091..fec306ea 100644 --- a/man/coef.ZIPLNfit.Rd +++ b/man/coef.ZIPLNfit.Rd @@ -4,13 +4,13 @@ \alias{coef.ZIPLNfit} \title{Extract model coefficients} \usage{ -\method{coef}{ZIPLNfit}(object, type = c("mainPLN", "mainZI", "precision", "covariance"), ...) +\method{coef}{ZIPLNfit}(object, type = c("count", "zero", "precision", "covariance"), ...) } \arguments{ \item{object}{an R6 object with class \code{\link{ZIPLNfit}}} -\item{type}{type of parameter that should be extracted. Either "mainPLN" (default) for \deqn{B}, -"mainZI" (default) for \deqn{B0}, "precision" for \deqn{\Omega}, "covariance" for \deqn{\Sigma}} +\item{type}{type of parameter that should be extracted. Either "count" (default) for \deqn{B}, +"zero" for \deqn{B0}, "precision" for \deqn{\Omega}, "covariance" for \deqn{\Sigma}} \item{...}{additional parameters for S3 compatibility. Not used} } diff --git a/man/compute_ZIPLN_starting_point.Rd b/man/compute_ZIPLN_starting_point.Rd deleted file mode 100644 index 7bfaaf7a..00000000 --- a/man/compute_ZIPLN_starting_point.Rd +++ /dev/null @@ -1,39 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/utils.R -\name{compute_ZIPLN_starting_point} -\alias{compute_ZIPLN_starting_point} -\title{Helper function for PLN initialization.} -\usage{ -compute_ZIPLN_starting_point(Y, X, X0, O, w, s = 0.1) -} -\arguments{ -\item{Y}{Response count matrix} - -\item{X}{Covariate matrix} - -\item{O}{Offset matrix (in log-scale)} - -\item{w}{Weight vector (defaults to 1)} - -\item{s}{Scale parameter for S (defaults to 0.1)} -} -\value{ -a named list of starting values for model parameter B and variational parameters M and S used in the iterative optimization algorithm of \code{\link[=PLN]{PLN()}} -} -\description{ -Barebone function to compute starting points for B, M and S when fitting a PLN. Mostly intended for internal use. -} -\details{ -The default strategy to estimate B and M is to fit a linear model with covariates \code{X} to the response count matrix (after adding a pseudocount of 1, scaling by the offset and taking the log). The regression matrix is used to initialize \code{B} and the residuals to initialize \code{M}. \code{S} is initialized as a constant conformable matrix with value \code{s}. -} -\examples{ -\dontrun{ -data(barents) -Y <- barents$Abundance -X <- model.matrix(Abundance ~ Latitude + Longitude + Depth + Temperature, data = barents) -O <- log(barents$Offset) -w <-- rep(1, nrow(Y)) -compute_ZIPLN_starting_point(Y, X, O, w) -} - -} From a6b0f722719efb0d675bcf23f68cd9c0876992e0 Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Tue, 16 Jan 2024 11:24:07 +0100 Subject: [PATCH 13/36] pkgdown modif --- _pkgdown.yml | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/_pkgdown.yml b/_pkgdown.yml index 68f1a379..d479b0c0 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -40,6 +40,7 @@ reference: contents: - '`PLNmodels`' - '`PLN`' + - '`ZIPLN`' - '`PLNLDA`' - '`PLNPCA`' - '`PLNnetwork`' @@ -58,7 +59,16 @@ reference: - '`predict_cond.PLNfit`' - '`fitted.PLNfit`' - '`standard_error.PLNfit`' -- title: 'Linear discriminant analysis via a Poisson lognormal fit' +- title: 'Zero Inflated Poisson lognormal fit' + desc: > + Description of the ZIPLNfit object and methods for its manipulation. + contents: + - starts_with('ZIPLNfit') + - '`ZIPLN_param`' + - '`coef.ZIPLNfit`' + - '`sigma.ZIPLNfit`' + - '`fitted.ZIPLNfit`' +=- title: 'Linear discriminant analysis via a Poisson lognormal fit' desc: > Description of the PLNLDAfit object and methods for its manipulation. contents: @@ -123,4 +133,5 @@ reference: - '`oaks`' - '`barents`' - '`mollusk`' + - '`scRNA`' From b240847e3aae0e6b0cc449e765a7d90d7d994baa Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Tue, 16 Jan 2024 11:28:44 +0100 Subject: [PATCH 14/36] pkgdown modif --- _pkgdown.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_pkgdown.yml b/_pkgdown.yml index d479b0c0..325c5223 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -68,7 +68,7 @@ reference: - '`coef.ZIPLNfit`' - '`sigma.ZIPLNfit`' - '`fitted.ZIPLNfit`' -=- title: 'Linear discriminant analysis via a Poisson lognormal fit' +- title: 'Linear discriminant analysis via a Poisson lognormal fit' desc: > Description of the PLNLDAfit object and methods for its manipulation. contents: From 7039630737ac712078dca05cf1fa8731436c092f Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Tue, 16 Jan 2024 11:37:03 +0100 Subject: [PATCH 15/36] updating tests in zipln --- tests/testthat/test-ziplnfit.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/testthat/test-ziplnfit.R b/tests/testthat/test-ziplnfit.R index 8fea8402..ae39ae95 100644 --- a/tests/testthat/test-ziplnfit.R +++ b/tests/testthat/test-ziplnfit.R @@ -14,8 +14,8 @@ test_that("ZIPLN fit: check classes, getters and field access", { ## S3 methods: values expect_equal(coef(model), model$model_par$B) - expect_equal(coef(model, type = "mainPLN"), model$model_par$B) - expect_equal(coef(model, type = "mainZI"), model$model_par$B0) + expect_equal(coef(model, type = "count"), model$model_par$B) + expect_equal(coef(model, type = "zero"), model$model_par$B0) expect_equal(coef(model, type = "precision"), model$model_par$Omega) expect_equal(coef(model, type = "covariance"), model$model_par$Sigma) expect_equal(sigma(model), model$model_par$Sigma) From 557cdcd8a4c79e3985edfa50fad89fa78fbb6e8a Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Tue, 16 Jan 2024 11:58:40 +0100 Subject: [PATCH 16/36] updating rdname --- R/ZIPLNfit-class.R | 6 +-- man/ZIPLNfit_fixed.Rd | 89 ++++++++++++++++++++++++++++++++++++- man/ZIPLNfit_fixedcov.Rd | 94 ---------------------------------------- 3 files changed, 91 insertions(+), 98 deletions(-) delete mode 100644 man/ZIPLNfit_fixedcov.Rd diff --git a/R/ZIPLNfit-class.R b/R/ZIPLNfit-class.R index f1bd4c03..86e18445 100644 --- a/R/ZIPLNfit-class.R +++ b/R/ZIPLNfit-class.R @@ -512,7 +512,7 @@ ZIPLNfit_fixed <- R6Class( ## PUBLIC MEMBERS ---- ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% public = list( - #' @description Initialize a [`ZIPLNfit_fixedcov`] model + #' @description Initialize a [`ZIPLNfit_fixed`] model initialize = function(responses, covariates, offsets, weights, formula, control) { super$initialize(responses, covariates, offsets, weights, formula, control) private$Omega <- control$Omega @@ -554,7 +554,7 @@ ZIPLNfit_fixed <- R6Class( #' @param control a list for controlling the optimization. See details. #' @param config part of the \code{control} argument which configures the optimizer #' -#' @rdname ZIPLNfit_fixedcov +#' @rdname ZIPLNfit_fixed #' @importFrom R6 R6Class #' #' @examples @@ -573,7 +573,7 @@ ZIPLNfit_sparse <- R6Class( ## PUBLIC MEMBERS ---- ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% public = list( - #' @description Initialize a [`ZIPLNfit_fixedcov`] model + #' @description Initialize a [`ZIPLNfit_fixed`] model #' @importFrom glassoFast glassoFast initialize = function(responses, covariates, offsets, weights, formula, control) { super$initialize(responses, covariates, offsets, weights, formula, control) diff --git a/man/ZIPLNfit_fixed.Rd b/man/ZIPLNfit_fixed.Rd index 4a80de5b..e2fed081 100644 --- a/man/ZIPLNfit_fixed.Rd +++ b/man/ZIPLNfit_fixed.Rd @@ -2,11 +2,16 @@ % Please edit documentation in R/ZIPLNfit-class.R \name{ZIPLNfit_fixed} \alias{ZIPLNfit_fixed} +\alias{ZIPLNfit_sparse} \title{An R6 Class to represent a ZIPLNfit in a standard, general framework, with fixed (inverse) residual covariance} \description{ An R6 Class to represent a ZIPLNfit in a standard, general framework, with fixed (inverse) residual covariance An R6 Class to represent a ZIPLNfit in a standard, general framework, with fixed (inverse) residual covariance + +An R6 Class to represent a ZIPLNfit in a standard, general framework, with sparse inverse residual covariance + +An R6 Class to represent a ZIPLNfit in a standard, general framework, with sparse inverse residual covariance } \examples{ \dontrun{ @@ -18,6 +23,14 @@ myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, class(myPLN) print(myPLN) } +\dontrun{ +# See other examples in function ZIPLN +data(trichoptera) +trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) +myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, control= ZIPLN_param(penalty = 0.2)) +class(myPLN) +print(myPLN) +} } \section{Super class}{ \code{\link[PLNmodels:ZIPLNfit]{PLNmodels::ZIPLNfit}} -> \code{ZIPLNfit_fixed} @@ -52,7 +65,7 @@ print(myPLN) \if{html}{\out{}} \if{latex}{\out{\hypertarget{method-ZIPLNfit_fixed-new}{}}} \subsection{Method \code{new()}}{ -Initialize a \code{\link{ZIPLNfit_fixedcov}} model +Initialize a \code{\link{ZIPLNfit_fixed}} model \subsection{Usage}{ \if{html}{\out{
}}\preformatted{ZIPLNfit_fixed$new(responses, covariates, offsets, weights, formula, control)}\if{html}{\out{
}} } @@ -93,3 +106,77 @@ The objects of this class are cloneable with this method. } } } +\section{Super class}{ +\code{\link[PLNmodels:ZIPLNfit]{PLNmodels::ZIPLNfit}} -> \code{ZIPLNfit_sparse} +} +\section{Active bindings}{ +\if{html}{\out{
}} +\describe{ +\item{\code{nb_param}}{number of parameters in the current PLN model} + +\item{\code{vcov_model}}{character: the model used for the residual covariance} +} +\if{html}{\out{
}} +} +\section{Methods}{ +\subsection{Public methods}{ +\itemize{ +\item \href{#method-ZIPLNfit_sparse-new}{\code{ZIPLNfit_sparse$new()}} +\item \href{#method-ZIPLNfit_sparse-clone}{\code{ZIPLNfit_sparse$clone()}} +} +} +\if{html}{\out{ +
Inherited methods + +
+}} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-ZIPLNfit_sparse-new}{}}} +\subsection{Method \code{new()}}{ +Initialize a \code{\link{ZIPLNfit_fixed}} model +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ZIPLNfit_sparse$new(responses, covariates, offsets, weights, formula, control)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{responses}}{the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{covariates}}{design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{offsets}}{offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{weights}}{an optional vector of observation weights to be used in the fitting process.} + +\item{\code{formula}}{model formula used for fitting, extracted from the formula in the upper-level call} + +\item{\code{control}}{a list for controlling the optimization. See details.} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-ZIPLNfit_sparse-clone}{}}} +\subsection{Method \code{clone()}}{ +The objects of this class are cloneable with this method. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ZIPLNfit_sparse$clone(deep = FALSE)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{deep}}{Whether to make a deep clone.} +} +\if{html}{\out{
}} +} +} +} diff --git a/man/ZIPLNfit_fixedcov.Rd b/man/ZIPLNfit_fixedcov.Rd deleted file mode 100644 index 37dbf475..00000000 --- a/man/ZIPLNfit_fixedcov.Rd +++ /dev/null @@ -1,94 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/ZIPLNfit-class.R -\name{ZIPLNfit_sparse} -\alias{ZIPLNfit_sparse} -\title{An R6 Class to represent a ZIPLNfit in a standard, general framework, with sparse inverse residual covariance} -\description{ -An R6 Class to represent a ZIPLNfit in a standard, general framework, with sparse inverse residual covariance - -An R6 Class to represent a ZIPLNfit in a standard, general framework, with sparse inverse residual covariance -} -\examples{ -\dontrun{ -# See other examples in function ZIPLN -data(trichoptera) -trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) -myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, control= ZIPLN_param(penalty = 0.2)) -class(myPLN) -print(myPLN) -} -} -\section{Super class}{ -\code{\link[PLNmodels:ZIPLNfit]{PLNmodels::ZIPLNfit}} -> \code{ZIPLNfit_sparse} -} -\section{Active bindings}{ -\if{html}{\out{
}} -\describe{ -\item{\code{nb_param}}{number of parameters in the current PLN model} - -\item{\code{vcov_model}}{character: the model used for the residual covariance} -} -\if{html}{\out{
}} -} -\section{Methods}{ -\subsection{Public methods}{ -\itemize{ -\item \href{#method-ZIPLNfit_sparse-new}{\code{ZIPLNfit_sparse$new()}} -\item \href{#method-ZIPLNfit_sparse-clone}{\code{ZIPLNfit_sparse$clone()}} -} -} -\if{html}{\out{ -
Inherited methods - -
-}} -\if{html}{\out{
}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-ZIPLNfit_sparse-new}{}}} -\subsection{Method \code{new()}}{ -Initialize a \code{\link{ZIPLNfit_fixedcov}} model -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{ZIPLNfit_sparse$new(responses, covariates, offsets, weights, formula, control)}\if{html}{\out{
}} -} - -\subsection{Arguments}{ -\if{html}{\out{
}} -\describe{ -\item{\code{responses}}{the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class} - -\item{\code{covariates}}{design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class} - -\item{\code{offsets}}{offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class} - -\item{\code{weights}}{an optional vector of observation weights to be used in the fitting process.} - -\item{\code{formula}}{model formula used for fitting, extracted from the formula in the upper-level call} - -\item{\code{control}}{a list for controlling the optimization. See details.} -} -\if{html}{\out{
}} -} -} -\if{html}{\out{
}} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-ZIPLNfit_sparse-clone}{}}} -\subsection{Method \code{clone()}}{ -The objects of this class are cloneable with this method. -\subsection{Usage}{ -\if{html}{\out{
}}\preformatted{ZIPLNfit_sparse$clone(deep = FALSE)}\if{html}{\out{
}} -} - -\subsection{Arguments}{ -\if{html}{\out{
}} -\describe{ -\item{\code{deep}}{Whether to make a deep clone.} -} -\if{html}{\out{
}} -} -} -} From e5c88daebe51db15c657daff51a9f2af04f73e0a Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Wed, 17 Jan 2024 18:50:18 +0100 Subject: [PATCH 17/36] advances in predict and optim_ve_step for ZIPLN --- DESCRIPTION | 1 + NAMESPACE | 2 + R/PLNfit-class.R | 2 +- R/ZIPLNfit-class.R | 221 ++++++++++++++++++++++++++++----- R/ZIPLNfits-S3methods.R | 33 ++--- R/utils-zipln.R | 102 +++++++++++++++ R/utils.R | 93 -------------- man/ZIPLNfit.Rd | 109 ++++++++++++++-- man/ZIPLNfit_diagonal.Rd | 4 +- man/ZIPLNfit_fixed.Rd | 91 +------------- man/ZIPLNfit_sparse.Rd | 96 ++++++++++++++ man/ZIPLNfit_spherical.Rd | 4 +- man/predict.ZIPLNfit.Rd | 23 ++++ tests/testthat/test-zipln.R | 4 +- tests/testthat/test-ziplnfit.R | 100 +++++++-------- 15 files changed, 596 insertions(+), 289 deletions(-) create mode 100644 R/utils-zipln.R create mode 100644 man/ZIPLNfit_sparse.Rd create mode 100644 man/predict.ZIPLNfit.Rd diff --git a/DESCRIPTION b/DESCRIPTION index f2b1eed9..f23718c1 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -98,6 +98,7 @@ Collate: 'scRNA.R' 'trichoptera.R' 'utils-pipe.R' + 'utils-zipln.R' 'utils.R' 'zzz.R' Language: en-US diff --git a/NAMESPACE b/NAMESPACE index d0e323f3..e3105216 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -24,6 +24,7 @@ S3method(plot,PLNnetworkfit) S3method(predict,PLNLDAfit) S3method(predict,PLNfit) S3method(predict,PLNmixturefit) +S3method(predict,ZIPLNfit) S3method(predict_cond,PLNfit) S3method(sigma,PLNfit) S3method(sigma,PLNmixturefit) @@ -116,6 +117,7 @@ importFrom(stats,model.offset) importFrom(stats,model.response) importFrom(stats,model.weights) importFrom(stats,poisson) +importFrom(stats,predict) importFrom(stats,quantile) importFrom(stats,residuals) importFrom(stats,rnorm) diff --git a/R/PLNfit-class.R b/R/PLNfit-class.R index cca92193..44e071a4 100644 --- a/R/PLNfit-class.R +++ b/R/PLNfit-class.R @@ -546,7 +546,7 @@ PLNfit <- R6Class( S <- VE$S } else { # otherwise set M = 0 and S = diag(Sigma) - M <- matrix(1, nrow = n_new, ncol = self$p) + M <- matrix(0, nrow = n_new, ncol = self$p) S <- matrix(diag(private$Sigma), nrow = n_new, ncol = self$p, byrow = TRUE) } diff --git a/R/ZIPLNfit-class.R b/R/ZIPLNfit-class.R index 86e18445..de4ddf2c 100644 --- a/R/ZIPLNfit-class.R +++ b/R/ZIPLNfit-class.R @@ -19,7 +19,6 @@ #' @importFrom R6 R6Class #' #' @examples -#' @examples #' \dontrun{ #' # See other examples in function ZIPLN #' data(trichoptera) @@ -127,13 +126,12 @@ ZIPLNfit <- R6Class( private$optimizer$B <- function(M, X, Omega, control) optim_zipln_B_dense(M, X) private$optimizer$zi <- switch( control$ziparam, - "single" = function(init_B0, X0, R, config) list(Pi = matrix( mean(R), n, p) , B0 = matrix(NA, d0, p)), - "row" = function(init_B0, X0, R, config) list(Pi = matrix(rowMeans(R), n, p) , B0 = matrix(NA, d0, p)), - "col" = function(init_B0, X0, R, config) list(Pi = matrix(colMeans(R), n, p, byrow = TRUE), B0 = matrix(NA, d0, p)), + "single" = function(init_B0, X0, R, config) list(Pi = matrix( mean(R), nrow(R), p) , B0 = matrix(NA, d0, p)), + "row" = function(init_B0, X0, R, config) list(Pi = matrix(rowMeans(R), nrow(R), p) , B0 = matrix(NA, d0, p)), + "col" = function(init_B0, X0, R, config) list(Pi = matrix(colMeans(R), nrow(R), p, byrow = TRUE), B0 = matrix(NA, d0, p)), "covar" = optim_zipln_zipar_covar ) private$optimizer$Omega <- optim_zipln_Omega_full - # private$optimizer$vestep <- xxxx }, @@ -257,6 +255,173 @@ ZIPLNfit <- R6Class( }, + #' @description Result of one call to the VE step of the optimization procedure: optimal variational parameters (M, S) and corresponding log likelihood values for fixed model parameters (Sigma, B). Intended to position new data in the latent space. + #' @param B Optional fixed value of the regression parameters + #' @param Sigma variance-covariance matrix of the latent variables + #' @return A list with three components: + #' * the matrix `M` of variational means, + #' * the matrix `S2` of variational variances + #' * the vector `log.lik` of (variational) log-likelihood of each new observation + optimize_vestep = function(covariates, offsets, responses, weights, + B = self$model_par$B, + B0 = self$model_par$B0, + Omega = self$model_par$Omega, + control = ZIPLN_param(backend = "nlopt")$config_optim) { + + n <- nrow(responses) + data <- list(Y = responses, X = covariates$PLN, X0 = covariates$ZI, O = offsets) + parameters <- + list(M = matrix(0, n, self$p), S = matrix(0.1, n, self$p), R = matrix(0, n, self$p)) + + # Main loop + nb_iter <- 0 + criterion <- numeric(control$maxit_out) + convergence <- numeric(control$maxit_out) + + vloglik <- -Inf; objective <- Inf + + repeat { + + # Check maxeval + if (control$maxit_out >= 0 && nb_iter >= control$maxit_out) { + stop_reason <- "maximum number of iterations reached" + criterion <- criterion[1:nb_iter] + convergence <- convergence[nb_iter] + break + } + + Pi <- private$optimizer$zi( + init_B0 = B0, X0 = data$X0, R = parameters$R, config = config_default_nlopt + )$Pi + + # VE Step + new_R <- optim_zipln_R( + Y = data$Y, X = data$X, O = data$O, M = parameters$M, S = parameters$S, Pi = Pi + ) + optim_new_M <- optim_zipln_M( + init_M = parameters$M, + Y = data$Y, X = data$X, O = data$O, R = new_R, S = parameters$S, B = B, Omega = Omega, + configuration = control + ) + new_M <- optim_new_M$M + optim_new_S <- optim_zipln_S( + init_S = parameters$S, + O = data$O, M = new_M, R = new_R, B = B, diag_Omega = diag(Omega), + configuration = control + ) + new_S <- optim_new_S$S + # Check convergence + new_parameters <- list(R = new_R, M = new_M, S = new_S) + nb_iter <- nb_iter + 1 + + vloglik <- zipln_vloglik( + data$Y, data$X, data$O, Pi, Omega, B, new_R, new_M, new_S + ) + + criterion[nb_iter] <- new_objective <- -sum(vloglik) + convergence[nb_iter] <- abs(new_objective - objective)/abs(new_objective) + + objective_converged <- + (objective - new_objective) <= control$ftol_out | + (objective - new_objective)/abs(new_objective) <= control$ftol_out + + parameters_converged <- parameter_list_converged( + parameters, new_parameters, + xtol_abs = control$xtol_abs, xtol_rel = control$xtol_rel + ) + + if (parameters_converged | objective_converged) { + parameters <- new_parameters + stop_reason <- "converged" + criterion <- criterion[1:nb_iter] + convergence <- convergence[1:nb_iter] + break + } + } + + list( + M = parameters$M, + S = parameters$S, + R = parameters$R, + Ji = vloglik, + monitoring = list( + iterations = nb_iter, + message = stop_reason, + objective = criterion, + convergence = convergence) + ) + }, + + #' @description Predict position, scores or observations of new data. + #' @param newdata A data frame in which to look for variables with which to predict. If omitted, the fitted values are used. + #' @param responses Optional data frame containing the count of the observed variables (matching the names of the provided as data in the PLN function), assuming the interest in in testing the model. + #' @param type Scale used for the prediction. Either `link` (default, predicted positions in the latent space) or `response` (predicted counts). + #' @param level Optional integer value the level to be used in obtaining the predictions. Level zero corresponds to the population predictions (default if `responses` is not provided) while level one (default) corresponds to predictions after evaluating the variational parameters for the new data. + #' @param envir Environment in which the prediction is evaluated + #' + #' @details + #' Note that `level = 1` can only be used if responses are provided, + #' as the variational parameters can't be estimated otherwise. In the absence of responses, `level` is ignored and the fitted values are returned + #' @return A matrix with predictions scores or counts. + predict = function(newdata, responses = NULL, type = c("link", "response"), level = 1, envir = parent.frame()) { + + ## Ignore everything if newdata is not provided + if (missing(newdata)) { + return(self$fitted) + } + + n_new <- nrow(newdata) + ## Set level to 0 (to bypass VE step) if responses are not provided + if (is.null(responses)) { + level <- 0 + } + + terms <- .extract_terms_zi(as.formula(private$formula)) + + ## Extract the model matrices from the new data set with initial formula + X <- model.matrix(terms$PLN, newdata, xlev = attr(private$formula, "xlevels")$PLN) + if (!is.null(terms$ZI)) X0 <- model.matrix(terms$ZI, frame, xlev = attr(private$formula, "xlevels")$ZI) else X0 <- matrix(NA,0,0) + + O <- model.offset(model.frame(formula(private$formula)[-2], newdata)) + if (is.null(O)) O <- matrix(0, n_new, self$p) + + ## Optimize M and S if responses are provided, + if (level == 1) { + VE <- self$optimize_vestep( + covariates = list(PLN = X, ZI = X0), + offsets = O, + responses = as.matrix(responses), + weights = rep(1, n_new), + B = private$B, + B0 = private$B0, + Omega = private$Omega + ) + R <- VE$R + M <- VE$M + S <- VE$S + } else { + # otherwise set R to Pi, M to 0 and S to diag(Sigma) + R <- private$Pi[1:nrow(newdata), ] + M <- X %*% private$B + S <- matrix(diag(private$Sigma), nrow = n_new, ncol = self$p, byrow = TRUE) + } + + ## mean latent positions in the parameter space (covariates/offset only) + EZ <- O + M + rownames(EZ) <- rownames(newdata) + colnames(EZ) <- colnames(private$Sigma) + + type <- match.arg(type) + results <- switch( + type, + link = EZ, + response = R + (1 - R) * exp(EZ + .5 * S^2), + + ) + attr(results, "type") <- type + results + }, + ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ## Print functions ----------------------- #' @description User friendly print method @@ -309,41 +474,43 @@ ZIPLNfit <- R6Class( ## ACTIVE BINDINGS ---- ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% active = list( - #' @field n number of samples + #' @field n number of samples/sites n = function() {nrow(private$M)}, - #' @field p number of species - p = function() {ncol(private$M)}, #' @field q number of dimensions of the latent space q = function() {ncol(private$M)}, - #' @field d number of covariates + #' @field p number of variables/species + p = function() {ncol(private$B)}, + #' @field d number of covariates in the PLN componente d = function() {nrow(private$B)}, - #' @field latent a matrix: values of the latent vector (Z in the model) - latent = function() {private$Z}, - #' @field latent_pos a matrix: values of the latent position vector (Z) without covariates effects or offset - latent_pos = function() {private$M - private$X %*% private$B}, - #' @field model_par a list with the matrices of parameters found in the model (B, Sigma, plus some others depending on the variant) - model_par = function() {list(B = private$B, B0 = private$B0, Pi = private$Pi, Omega = private$Omega, Sigma = private$Sigma)}, - #' @field var_par a list with two matrices, M and S2, which are the estimated parameters in the variational approximation - var_par = function() {list(M = private$M, S2 = private$S^2, S = private$S, R = private$R)}, - #' @field fitted a matrix: fitted values of the observations (A in the model) - fitted = function() {private$R * private$zeros + (1 - private$R) * private$A}, + #' @field d0 number of covariates in the ZI componente + d0 = function() {nrow(private$B0)}, #' @field nb_param number of parameters in the current PLN model nb_param = function() { as.integer( self$p * self$d + self$p * (self$p + 1)/2 + switch(private$ziparam, - "single" = 1, - "row" = self$n, - "col" = self$p, - "covar" = self$p * self$d) + "single" = 1, + "row" = self$n, + "col" = self$p, + "covar" = self$p * self$d) ) }, + #' @field model_par a list with the matrices of parameters found in the model (B, Sigma, plus some others depending on the variant) + model_par = function() {list(B = private$B, B0 = private$B0, Pi = private$Pi, Omega = private$Omega, Sigma = private$Sigma)}, + #' @field var_par a list with two matrices, M and S2, which are the estimated parameters in the variational approximation + var_par = function() {list(M = private$M, S2 = private$S^2, S = private$S, R = private$R)}, + #' @field optim_par a list with parameters useful for monitoring the optimization + optim_par = function() {private$monitoring}, + #' @field latent a matrix: values of the latent vector (Z in the model) + latent = function() {private$Z}, + #' @field latent_pos a matrix: values of the latent position vector (Z) without covariates effects or offset + latent_pos = function() {private$M - private$X %*% private$B}, + #' @field fitted a matrix: fitted values of the observations (A in the model) + fitted = function() {private$R * private$zeros + (1 - private$R) * private$A}, #' @field vcov_model character: the model used for the covariance (either "spherical", "diagonal", "full" or "sparse") vcov_model = function() {private$covariance}, #' @field zi_model character: the model used for the zero inflation (either "single", "row", "col" or "covar") zi_model = function() {private$ziparam}, - #' @field optim_par a list with parameters useful for monitoring the optimization - optim_par = function() {private$monitoring}, #' @field loglik (weighted) variational lower bound of the loglikelihood loglik = function() {sum(private$Ji) }, #' @field loglik_vec element-wise variational lower bound of the loglikelihood @@ -381,7 +548,6 @@ ZIPLNfit <- R6Class( #' @param formula model formula used for fitting, extracted from the formula in the upper-level call #' @param control a list for controlling the optimization. See details. #' -#' @rdname ZIPLNfit_diagonal #' @importFrom R6 R6Class #' #' @examples @@ -435,7 +601,6 @@ ZIPLNfit_diagonal <- R6Class( #' @param formula model formula used for fitting, extracted from the formula in the upper-level call #' @param control a list for controlling the optimization. See details. #' -#' @rdname ZIPLNfit_spherical #' @importFrom R6 R6Class #' #' @examples @@ -492,7 +657,6 @@ ZIPLNfit_spherical <- R6Class( #' @param control a list for controlling the optimization. See details. #' @param config part of the \code{control} argument which configures the optimizer #' -#' @rdname ZIPLNfit_fixed #' @importFrom R6 R6Class #' #' @examples @@ -554,7 +718,6 @@ ZIPLNfit_fixed <- R6Class( #' @param control a list for controlling the optimization. See details. #' @param config part of the \code{control} argument which configures the optimizer #' -#' @rdname ZIPLNfit_fixed #' @importFrom R6 R6Class #' #' @examples diff --git a/R/ZIPLNfits-S3methods.R b/R/ZIPLNfits-S3methods.R index 130621b3..b989da7b 100644 --- a/R/ZIPLNfits-S3methods.R +++ b/R/ZIPLNfits-S3methods.R @@ -7,21 +7,24 @@ ## Auxiliary functions to check the given class of an objet isZIPLNfit <- function(Robject) {inherits(Robject, "ZIPLNfit")} -# #' Predict counts of a new sample -# #' -# #' @name predict.ZIPLNfit -# #' -# #' @param object an R6 object with class [`ZIPLNfit`] -# #' @param newdata A data frame in which to look for variables and offsets with which to predict -# #' @param type The type of prediction required. The default is on the scale of the linear predictors (i.e. log average count) -# #' @param ... additional parameters for S3 compatibility. Not used -# #' @return A matrix of predicted log-counts (if `type = "link"`) or predicted counts (if `type = "response"`). -# #' @importFrom stats predict -# #' @export -# predict.ZIPLNfit <- function(object, newdata, type = c("link", "response"), ...) { -# stopifnot(isZIPLNfit(object)) -# object$predict(newdata, type, parent.frame()) -# } +#' Predict counts of a new sample +#' +#' @name predict.ZIPLNfit +#' +#' @param object an R6 object with class [`ZIPLNfit`] +#' @param newdata A data frame in which to look for variables and offsets with which to predict +#' @param responses Optional data frame containing the count of the observed variables (matching the names of the provided as data in the PLN function), assuming the interest in in testing the model. +#' @param type The type of prediction required. The default is on the scale of the linear predictors (i.e. log average count) +#' @param level Optional integer value the level to be used in obtaining the predictions. Level zero corresponds to the population predictions (default if `responses` is not provided) while level one (default) corresponds to predictions after evaluating the variational parameters for the new data. +#' @param ... additional parameters for S3 compatibility. Not used +#' @return A matrix of predicted log-counts (if `type = "link"`) or predicted counts (if `type = "response"`). +#' @export +predict.ZIPLNfit <- function(object, newdata, responses = NULL, level = 1, type = c("link", "response"), ...) { + stopifnot(isZIPLNfit(object)) + object$predict(newdata = newdata, type = type, envir = parent.frame(), level = level, responses = responses) +} + + #' Extract model coefficients #' diff --git a/R/utils-zipln.R b/R/utils-zipln.R new file mode 100644 index 00000000..38fa82c5 --- /dev/null +++ b/R/utils-zipln.R @@ -0,0 +1,102 @@ +.extract_terms_zi <- function(formula) { + + ## Check if a ZI specific formula has been + if (length(formula[[3]]) > 1 && identical(formula[[3]][[1]], as.name("|"))) { + zicovar <- TRUE + ff_zi <- ~. ; ff_zi[[3]] <- formula[[3]][[3]] ; ff_zi[[2]] <- NULL + ff_pln <- ~. ; ff_pln[[3]] <- formula[[3]][[2]] ; ff_pln[[2]] <- NULL + tt_zi <- terms(ff_zi) ; attr(tt_zi , "offset") <- NULL + tt_pln <- terms(ff_pln) ; attr(tt_pln, "offset") <- NULL + formula[[3]][1] <- call("+") + xlevels <- list(PLN = .getXlevels(tt_pln, ff_pln), ZI = .getXlevels(tt_zi, ff_zi)) + } else { + ff_pln <- formula + ff_zi <- NULL + tt_pln <- terms(ff_pln) ; attr(tt_pln, "offset") <- NULL + tt_zi <- NULL + zicovar <- FALSE + xlevels <- list(PLN = .getXlevels(tt_pln, ff_pln), ZI = NULL) + } + + list(ZI = tt_zi, PLN = tt_pln, formula = formula, xlevels = xlevels, zicovar = zicovar) +} + +#' @importFrom stats .getXlevels +extract_model_zi <- function(call, envir, xlev = NULL) { + + ## create the call for the model frame + call_args <- call[match(c("formula", "data", "subset", "weights"), names(call), 0L)] + call_args <- c(as.list(call_args), list(xlev = attr(call$formula, "xlevels"), na.action = NULL)) + + ## Extract terms for ZI and PLN components + terms <- .extract_terms_zi(as.formula(call$formula)) + + ## eval the call in the parent environment with adjustement due to ZI terms + call_args$formula <- terms$formula + attr(call$formula, "xlevels") <- terms$xlevels + frame <- do.call(stats::model.frame, call_args, envir = envir) + + ## Create the set of matrices to fit the PLN model + + X <- model.matrix(terms$PLN, frame, xlev = terms$xlevel$PLN) + if (terms$zicovar) X0 <- model.matrix(terms$ZI, frame, xlev = terms$xlevel$ZI) else X0 <- matrix(NA,0,0) + + Y <- model.response(frame) + ## model.response oversimplifies into a numeric when a single variable is involved + if (is.null(dim(Y))) Y <- matrix(Y, nrow = length(Y), ncol = 1) + if (ncol(Y) == 1 & is.null(colnames(Y))) colnames(Y) <- "Y" + + # Offsets are only considered for the PLN component + O <- model.offset(frame) + if (is.null(O)) O <- matrix(0, nrow(Y), ncol(Y)) + if (is.vector(O)) O <- O %o% rep(1, ncol(Y)) + + # Model weights + w <- model.weights(frame) + if (is.null(w)) { + w <- rep(1.0, nrow(Y)) + } else { + stopifnot(all(w > 0) && length(w) == nrow(Y)) + } + + list(Y = Y, X = X, X0 = X0, O = O, w = w, formula = call$formula, zicovar = terms$zicovar) +} + + +#' #' Helper function for ZIPLN initialization. +#' #' +#' #' @description +#' #' Barebone function to compute starting points for B, M and S when fitting a PLN. Mostly intended for internal use. +#' #' +#' #' @param Y Response count matrix +#' #' @param X Design matrix for the count (PLN) component +#' #' @param X0 Design matrix for the zero (Bernoulli) component +#' #' @param O Offset matrix (in log-scale) +#' #' @param w Weight vector (defaults to 1) +#' #' @param s Scale parameter for S (defaults to 0.1) +#' #' @return a named list of starting values for model parameter B and variational parameters M and S used in the iterative optimization algorithm of [PLN()] +#' #' +#' #' @details The default strategy to estimate B and M is to fit a linear model with covariates `X` to the response count matrix (after adding a pseudocount of 1, scaling by the offset and taking the log). The regression matrix is used to initialize `B` and the residuals to initialize `M`. `S` is initialized as a constant conformable matrix with value `s`. +#' #' +#' #' @rdname compute_ZIPLN_starting_point +#' #' @examples +#' #' \dontrun{ +#' #' data(barents) +#' #' Y <- barents$Abundance +#' #' X <- model.matrix(Abundance ~ 1 + Temperature, data = barents) +#' #' X0 <- model.matrix(Abundance ~ 0 + Longitude, data = barents) +#' #' O <- log(barents$Offset) +#' #' w <-- rep(1, nrow(Y)) +#' #' compute_ZIPLN_starting_point(Y, X, O, w) +#' #' } +#' #' +#' #' @importFrom stats lm.fit +#' #' @export +#' compute_ZIPLN_starting_point <- function(Y, X, X0, O, w, s = 0.1) { +#' # Y = responses, X = covariates, O = offsets (in log scale), w = weights +#' n <- nrow(Y); p <- ncol(Y); d <- ncol(X) +#' fits <- lm.fit(w * X, w * log((1 + Y)/exp(O))) +#' list(B = matrix(fits$coefficients, d, p), +#' M = matrix(fits$residuals, n, p), +#' S = matrix(s, n, p)) +#' } diff --git a/R/utils.R b/R/utils.R index 2206b575..2ccba19c 100644 --- a/R/utils.R +++ b/R/utils.R @@ -172,61 +172,6 @@ extract_model <- function(call, envir) { list(Y = Y, X = X, O = O, miss = is.na(Y), w = w, formula = call$formula) } -#' @importFrom stats .getXlevels -extract_model_zi <- function(call, envir, xlev = NULL) { - - ## create the call for the model frame - call_frame <- call[c(1L, match(c("formula", "data", "subset"), names(call), 0L))] - call_frame[[1]] <- quote(stats::model.frame) - - formula <- as.formula(call$formula) - ff <- formula - - ## Check if a ZI specific formula has been - if (length(ff[[3]]) > 1 && identical(ff[[3]][[1]], as.name("|"))) { - zicovar <- TRUE - ff_zi <- ~. ; ff_zi[[3]] <- ff[[3]][[3]] ; ff_zi[[2]] <- NULL - ff_pln <- ~. ; ff_pln[[3]] <- ff[[3]][[2]] ; ff_pln[[2]] <- NULL - tt_zi <- terms(ff_zi) ; attr(tt_zi , "offset") <- NULL - tt_pln <- terms(ff_pln) ; attr(tt_pln, "offset") <- NULL - formula[[3]][1] <- call("+") - if (tt_zi[[2]] == "row") {ziparam <- "row"; formula[[3]][[3]] <- NULL} - if (tt_zi[[2]] == "col") {ziparam <- "col"; formula[[3]][[3]] <- NULL} - call_frame$formula <- formula - } else { - ff_pln <- ff - tt_pln <- terms(ff_pln) ; attr(tt_pln, "offset") <- NULL - zicovar <- FALSE - } - - ## eval the call in the parent environment - frame <- eval(call_frame, envir) - ## create the set of matrices to fit the PLN model - Y <- frame[[1L]] ## model.response oversimplifies into a numeric when a single variable is involved - ## model.response oversimplifies into a numeric when a single variable is involved - if (is.null(dim(Y))) Y <- matrix(Y, nrow = length(Y), ncol = 1) - if (ncol(Y) == 1 & is.null(colnames(Y))) colnames(Y) <- "Y" - - ## Extract the design matrices for ZI and PLN components - X <- model.matrix(tt_pln, frame, xlev = xlev$pln) - if (zicovar) X0 <- model.matrix(tt_zi, frame, xlev = xlev$zi) else X0 <- matrix(NA,0,0) - - # Offsets are only considered for the PLN component - O <- model.offset(frame) - if (is.null(O)) O <- matrix(0, nrow(Y), ncol(Y)) - if (is.vector(O)) O <- O %o% rep(1, ncol(Y)) - - # Model weights - w <- model.weights(frame) - if (is.null(w)) { - w <- rep(1.0, nrow(Y)) - } else { - stopifnot(all(w > 0) && length(w) == nrow(Y)) - } - - list(Y = Y, X = X, X0 = X0, O = O, w = w, formula = call$formula, zicovar = zicovar) -} - edge_to_node <- function(x, n = max(x)) { x <- x - 1 ## easier for arithmetic to number edges starting from 0 n.node <- round((1 + sqrt(1 + 8*n)) / 2) ## n.node * (n.node -1) / 2 = n (if integer) @@ -351,41 +296,3 @@ compute_PLN_starting_point <- function(Y, X, O, w, s = 0.1) { M = matrix(fits$residuals, n, p), S = matrix(s, n, p)) } - -#' #' Helper function for PLN initialization. -#' #' -#' #' @description -#' #' Barebone function to compute starting points for B, M and S when fitting a PLN. Mostly intended for internal use. -#' #' -#' #' @param Y Response count matrix -#' #' @param X Design matrix for the count (PLN) component -#' #' @param X0 Design matrix for the zero (Bernoulli) component -#' #' @param O Offset matrix (in log-scale) -#' #' @param w Weight vector (defaults to 1) -#' #' @param s Scale parameter for S (defaults to 0.1) -#' #' @return a named list of starting values for model parameter B and variational parameters M and S used in the iterative optimization algorithm of [PLN()] -#' #' -#' #' @details The default strategy to estimate B and M is to fit a linear model with covariates `X` to the response count matrix (after adding a pseudocount of 1, scaling by the offset and taking the log). The regression matrix is used to initialize `B` and the residuals to initialize `M`. `S` is initialized as a constant conformable matrix with value `s`. -#' #' -#' #' @rdname compute_ZIPLN_starting_point -#' #' @examples -#' #' \dontrun{ -#' #' data(barents) -#' #' Y <- barents$Abundance -#' #' X <- model.matrix(Abundance ~ 1 + Temperature, data = barents) -#' #' X0 <- model.matrix(Abundance ~ 0 + Longitude, data = barents) -#' #' O <- log(barents$Offset) -#' #' w <-- rep(1, nrow(Y)) -#' #' compute_ZIPLN_starting_point(Y, X, O, w) -#' #' } -#' #' -#' #' @importFrom stats lm.fit -#' #' @export -#' compute_ZIPLN_starting_point <- function(Y, X, X0, O, w, s = 0.1) { -#' # Y = responses, X = covariates, O = offsets (in log scale), w = weights -#' n <- nrow(Y); p <- ncol(Y); d <- ncol(X) -#' fits <- lm.fit(w * X, w * log((1 + Y)/exp(O))) -#' list(B = matrix(fits$coefficients, d, p), -#' M = matrix(fits$residuals, n, p), -#' S = matrix(s, n, p)) -#' } diff --git a/man/ZIPLNfit.Rd b/man/ZIPLNfit.Rd index 249e2927..c9eb95f4 100644 --- a/man/ZIPLNfit.Rd +++ b/man/ZIPLNfit.Rd @@ -25,32 +25,34 @@ print(myPLN) \section{Active bindings}{ \if{html}{\out{
}} \describe{ -\item{\code{n}}{number of samples} - -\item{\code{p}}{number of species} +\item{\code{n}}{number of samples/sites} \item{\code{q}}{number of dimensions of the latent space} -\item{\code{d}}{number of covariates} +\item{\code{p}}{number of variables/species} -\item{\code{latent}}{a matrix: values of the latent vector (Z in the model)} +\item{\code{d}}{number of covariates in the PLN componente} -\item{\code{latent_pos}}{a matrix: values of the latent position vector (Z) without covariates effects or offset} +\item{\code{d0}}{number of covariates in the ZI componente} + +\item{\code{nb_param}}{number of parameters in the current PLN model} \item{\code{model_par}}{a list with the matrices of parameters found in the model (B, Sigma, plus some others depending on the variant)} \item{\code{var_par}}{a list with two matrices, M and S2, which are the estimated parameters in the variational approximation} -\item{\code{fitted}}{a matrix: fitted values of the observations (A in the model)} +\item{\code{optim_par}}{a list with parameters useful for monitoring the optimization} -\item{\code{nb_param}}{number of parameters in the current PLN model} +\item{\code{latent}}{a matrix: values of the latent vector (Z in the model)} + +\item{\code{latent_pos}}{a matrix: values of the latent position vector (Z) without covariates effects or offset} + +\item{\code{fitted}}{a matrix: fitted values of the observations (A in the model)} \item{\code{vcov_model}}{character: the model used for the covariance (either "spherical", "diagonal", "full" or "sparse")} \item{\code{zi_model}}{character: the model used for the zero inflation (either "single", "row", "col" or "covar")} -\item{\code{optim_par}}{a list with parameters useful for monitoring the optimization} - \item{\code{loglik}}{(weighted) variational lower bound of the loglikelihood} \item{\code{loglik_vec}}{element-wise variational lower bound of the loglikelihood} @@ -75,6 +77,8 @@ print(myPLN) \item \href{#method-ZIPLNfit-update}{\code{ZIPLNfit$update()}} \item \href{#method-ZIPLNfit-new}{\code{ZIPLNfit$new()}} \item \href{#method-ZIPLNfit-optimize}{\code{ZIPLNfit$optimize()}} +\item \href{#method-ZIPLNfit-optimize_vestep}{\code{ZIPLNfit$optimize_vestep()}} +\item \href{#method-ZIPLNfit-predict}{\code{ZIPLNfit$predict()}} \item \href{#method-ZIPLNfit-show}{\code{ZIPLNfit$show()}} \item \href{#method-ZIPLNfit-print}{\code{ZIPLNfit$print()}} \item \href{#method-ZIPLNfit-clone}{\code{ZIPLNfit$clone()}} @@ -188,6 +192,91 @@ Call to the Cpp optimizer and update of the relevant fields } } \if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-ZIPLNfit-optimize_vestep}{}}} +\subsection{Method \code{optimize_vestep()}}{ +Result of one call to the VE step of the optimization procedure: optimal variational parameters (M, S) and corresponding log likelihood values for fixed model parameters (Sigma, B). Intended to position new data in the latent space. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ZIPLNfit$optimize_vestep( + covariates, + offsets, + responses, + weights, + B = self$model_par$B, + B0 = self$model_par$B0, + Omega = self$model_par$Omega, + control = ZIPLN_param(backend = "nlopt") +)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{covariates}}{design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{offsets}}{offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{responses}}{the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{weights}}{an optional vector of observation weights to be used in the fitting process.} + +\item{\code{B}}{Optional fixed value of the regression parameters} + +\item{\code{control}}{a list for controlling the optimization. See details.} + +\item{\code{Sigma}}{variance-covariance matrix of the latent variables} +} +\if{html}{\out{
}} +} +\subsection{Returns}{ +A list with three components: +\itemize{ +\item the matrix \code{M} of variational means, +\item the matrix \code{S2} of variational variances +\item the vector \code{log.lik} of (variational) log-likelihood of each new observation +} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-ZIPLNfit-predict}{}}} +\subsection{Method \code{predict()}}{ +Predict position, scores or observations of new data. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ZIPLNfit$predict( + newdata, + responses = NULL, + type = c("link", "response"), + level = 1, + envir = parent.frame() +)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{newdata}}{A data frame in which to look for variables with which to predict. If omitted, the fitted values are used.} + +\item{\code{responses}}{Optional data frame containing the count of the observed variables (matching the names of the provided as data in the PLN function), assuming the interest in in testing the model.} + +\item{\code{type}}{Scale used for the prediction. Either \code{link} (default, predicted positions in the latent space) or \code{response} (predicted counts).} + +\item{\code{level}}{Optional integer value the level to be used in obtaining the predictions. Level zero corresponds to the population predictions (default if \code{responses} is not provided) while level one (default) corresponds to predictions after evaluating the variational parameters for the new data.} + +\item{\code{envir}}{Environment in which the prediction is evaluated} +} +\if{html}{\out{
}} +} +\subsection{Details}{ +Note that \code{level = 1} can only be used if responses are provided, +as the variational parameters can't be estimated otherwise. In the absence of responses, \code{level} is ignored and the fitted values are returned +} + +\subsection{Returns}{ +A matrix with predictions scores or counts. +} +} +\if{html}{\out{
}} \if{html}{\out{}} \if{latex}{\out{\hypertarget{method-ZIPLNfit-show}{}}} \subsection{Method \code{show()}}{ diff --git a/man/ZIPLNfit_diagonal.Rd b/man/ZIPLNfit_diagonal.Rd index e6f292d6..226bb7ca 100644 --- a/man/ZIPLNfit_diagonal.Rd +++ b/man/ZIPLNfit_diagonal.Rd @@ -38,9 +38,11 @@ print(myPLN) } } \if{html}{\out{ -
Inherited methods +
Inherited methods
  • PLNmodels::ZIPLNfit$optimize()
    • +
  • PLNmodels::ZIPLNfit$optimize_vestep()
    • +
  • PLNmodels::ZIPLNfit$predict()
  • PLNmodels::ZIPLNfit$print()
  • PLNmodels::ZIPLNfit$show()
  • PLNmodels::ZIPLNfit$update()
    • diff --git a/man/ZIPLNfit_fixed.Rd b/man/ZIPLNfit_fixed.Rd index e2fed081..d57a5bd7 100644 --- a/man/ZIPLNfit_fixed.Rd +++ b/man/ZIPLNfit_fixed.Rd @@ -2,16 +2,11 @@ % Please edit documentation in R/ZIPLNfit-class.R \name{ZIPLNfit_fixed} \alias{ZIPLNfit_fixed} -\alias{ZIPLNfit_sparse} \title{An R6 Class to represent a ZIPLNfit in a standard, general framework, with fixed (inverse) residual covariance} \description{ An R6 Class to represent a ZIPLNfit in a standard, general framework, with fixed (inverse) residual covariance An R6 Class to represent a ZIPLNfit in a standard, general framework, with fixed (inverse) residual covariance - -An R6 Class to represent a ZIPLNfit in a standard, general framework, with sparse inverse residual covariance - -An R6 Class to represent a ZIPLNfit in a standard, general framework, with sparse inverse residual covariance } \examples{ \dontrun{ @@ -23,14 +18,6 @@ myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, class(myPLN) print(myPLN) } -\dontrun{ -# See other examples in function ZIPLN -data(trichoptera) -trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) -myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, control= ZIPLN_param(penalty = 0.2)) -class(myPLN) -print(myPLN) -} } \section{Super class}{ \code{\link[PLNmodels:ZIPLNfit]{PLNmodels::ZIPLNfit}} -> \code{ZIPLNfit_fixed} @@ -52,9 +39,11 @@ print(myPLN) } } \if{html}{\out{ -
    Inherited methods +
    Inherited methods
    • PLNmodels::ZIPLNfit$optimize()
      • +
    • PLNmodels::ZIPLNfit$optimize_vestep()
      • +
    • PLNmodels::ZIPLNfit$predict()
    • PLNmodels::ZIPLNfit$print()
    • PLNmodels::ZIPLNfit$show()
    • PLNmodels::ZIPLNfit$update()
      • @@ -106,77 +95,3 @@ The objects of this class are cloneable with this method. } } } -\section{Super class}{ -\code{\link[PLNmodels:ZIPLNfit]{PLNmodels::ZIPLNfit}} -> \code{ZIPLNfit_sparse} -} -\section{Active bindings}{ -\if{html}{\out{
      }} -\describe{ -\item{\code{nb_param}}{number of parameters in the current PLN model} - -\item{\code{vcov_model}}{character: the model used for the residual covariance} -} -\if{html}{\out{
      }} -} -\section{Methods}{ -\subsection{Public methods}{ -\itemize{ -\item \href{#method-ZIPLNfit_sparse-new}{\code{ZIPLNfit_sparse$new()}} -\item \href{#method-ZIPLNfit_sparse-clone}{\code{ZIPLNfit_sparse$clone()}} -} -} -\if{html}{\out{ -
      Inherited methods - -
      -}} -\if{html}{\out{
      }} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-ZIPLNfit_sparse-new}{}}} -\subsection{Method \code{new()}}{ -Initialize a \code{\link{ZIPLNfit_fixed}} model -\subsection{Usage}{ -\if{html}{\out{
      }}\preformatted{ZIPLNfit_sparse$new(responses, covariates, offsets, weights, formula, control)}\if{html}{\out{
      }} -} - -\subsection{Arguments}{ -\if{html}{\out{
      }} -\describe{ -\item{\code{responses}}{the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class} - -\item{\code{covariates}}{design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class} - -\item{\code{offsets}}{offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class} - -\item{\code{weights}}{an optional vector of observation weights to be used in the fitting process.} - -\item{\code{formula}}{model formula used for fitting, extracted from the formula in the upper-level call} - -\item{\code{control}}{a list for controlling the optimization. See details.} -} -\if{html}{\out{
      }} -} -} -\if{html}{\out{
      }} -\if{html}{\out{}} -\if{latex}{\out{\hypertarget{method-ZIPLNfit_sparse-clone}{}}} -\subsection{Method \code{clone()}}{ -The objects of this class are cloneable with this method. -\subsection{Usage}{ -\if{html}{\out{
      }}\preformatted{ZIPLNfit_sparse$clone(deep = FALSE)}\if{html}{\out{
      }} -} - -\subsection{Arguments}{ -\if{html}{\out{
      }} -\describe{ -\item{\code{deep}}{Whether to make a deep clone.} -} -\if{html}{\out{
      }} -} -} -} diff --git a/man/ZIPLNfit_sparse.Rd b/man/ZIPLNfit_sparse.Rd new file mode 100644 index 00000000..6894af91 --- /dev/null +++ b/man/ZIPLNfit_sparse.Rd @@ -0,0 +1,96 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/ZIPLNfit-class.R +\name{ZIPLNfit_sparse} +\alias{ZIPLNfit_sparse} +\title{An R6 Class to represent a ZIPLNfit in a standard, general framework, with sparse inverse residual covariance} +\description{ +An R6 Class to represent a ZIPLNfit in a standard, general framework, with sparse inverse residual covariance + +An R6 Class to represent a ZIPLNfit in a standard, general framework, with sparse inverse residual covariance +} +\examples{ +\dontrun{ +# See other examples in function ZIPLN +data(trichoptera) +trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) +myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera, control= ZIPLN_param(penalty = 0.2)) +class(myPLN) +print(myPLN) +} +} +\section{Super class}{ +\code{\link[PLNmodels:ZIPLNfit]{PLNmodels::ZIPLNfit}} -> \code{ZIPLNfit_sparse} +} +\section{Active bindings}{ +\if{html}{\out{
      }} +\describe{ +\item{\code{nb_param}}{number of parameters in the current PLN model} + +\item{\code{vcov_model}}{character: the model used for the residual covariance} +} +\if{html}{\out{
      }} +} +\section{Methods}{ +\subsection{Public methods}{ +\itemize{ +\item \href{#method-ZIPLNfit_sparse-new}{\code{ZIPLNfit_sparse$new()}} +\item \href{#method-ZIPLNfit_sparse-clone}{\code{ZIPLNfit_sparse$clone()}} +} +} +\if{html}{\out{ +
      Inherited methods + +
      +}} +\if{html}{\out{
      }} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-ZIPLNfit_sparse-new}{}}} +\subsection{Method \code{new()}}{ +Initialize a \code{\link{ZIPLNfit_fixed}} model +\subsection{Usage}{ +\if{html}{\out{
      }}\preformatted{ZIPLNfit_sparse$new(responses, covariates, offsets, weights, formula, control)}\if{html}{\out{
      }} +} + +\subsection{Arguments}{ +\if{html}{\out{
      }} +\describe{ +\item{\code{responses}}{the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{covariates}}{design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{offsets}}{offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class} + +\item{\code{weights}}{an optional vector of observation weights to be used in the fitting process.} + +\item{\code{formula}}{model formula used for fitting, extracted from the formula in the upper-level call} + +\item{\code{control}}{a list for controlling the optimization. See details.} +} +\if{html}{\out{
      }} +} +} +\if{html}{\out{
      }} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-ZIPLNfit_sparse-clone}{}}} +\subsection{Method \code{clone()}}{ +The objects of this class are cloneable with this method. +\subsection{Usage}{ +\if{html}{\out{
      }}\preformatted{ZIPLNfit_sparse$clone(deep = FALSE)}\if{html}{\out{
      }} +} + +\subsection{Arguments}{ +\if{html}{\out{
      }} +\describe{ +\item{\code{deep}}{Whether to make a deep clone.} +} +\if{html}{\out{
      }} +} +} +} diff --git a/man/ZIPLNfit_spherical.Rd b/man/ZIPLNfit_spherical.Rd index 74cfc688..b80571cb 100644 --- a/man/ZIPLNfit_spherical.Rd +++ b/man/ZIPLNfit_spherical.Rd @@ -38,9 +38,11 @@ print(myPLN) } } \if{html}{\out{ -
      Inherited methods +
      Inherited methods
      • PLNmodels::ZIPLNfit$optimize()
        • +
      • PLNmodels::ZIPLNfit$optimize_vestep()
        • +
      • PLNmodels::ZIPLNfit$predict()
      • PLNmodels::ZIPLNfit$print()
      • PLNmodels::ZIPLNfit$show()
      • PLNmodels::ZIPLNfit$update()
        • diff --git a/man/predict.ZIPLNfit.Rd b/man/predict.ZIPLNfit.Rd new file mode 100644 index 00000000..16027d8d --- /dev/null +++ b/man/predict.ZIPLNfit.Rd @@ -0,0 +1,23 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/ZIPLNfits-S3methods.R +\name{predict.ZIPLNfit} +\alias{predict.ZIPLNfit} +\title{Predict counts of a new sample} +\usage{ +\method{predict}{ZIPLNfit}(object, newdata, type = c("link", "response"), ...) +} +\arguments{ +\item{object}{an R6 object with class \code{\link{ZIPLNfit}}} + +\item{newdata}{A data frame in which to look for variables and offsets with which to predict} + +\item{type}{The type of prediction required. The default is on the scale of the linear predictors (i.e. log average count)} + +\item{...}{additional parameters for S3 compatibility. Not used} +} +\value{ +A matrix of predicted log-counts (if \code{type = "link"}) or predicted counts (if \code{type = "response"}). +} +\description{ +Predict counts of a new sample +} diff --git a/tests/testthat/test-zipln.R b/tests/testthat/test-zipln.R index 5dec3c28..14a34809 100644 --- a/tests/testthat/test-zipln.R +++ b/tests/testthat/test-zipln.R @@ -18,7 +18,7 @@ test_that("ZIPLN: Check that ZIPLN is running and robust", { expect_is(ZIPLN(trichoptera$Abundance ~ 1), "ZIPLNfit") expect_equal(ZIPLN(trichoptera$Abundance ~ 1 + trichoptera$Wind)$fitted, - ZIPLN(Abundance ~ Wind, data = trichoptera)$fitted) + ZIPLN(Abundance ~ Wind, data = trichoptera)$fitted, tolerance = 1e-3) expect_is(model_sparse <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(penalty = 0.2, trace = 0)), "ZIPLNfit_sparse") expect_is(model_fixed <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(Omega = zi_single$model_par$Omega, trace = 0)), "ZIPLNfit_fixed") @@ -64,7 +64,7 @@ test_that("ZIPLN: Check that univariate ZIPLN models works, with matrix of numer expect_no_error(uniZIPLN <- ZIPLN(Abundance[,1,drop=FALSE] ~ 1, data = trichoptera)) expect_no_error(uniZIPLN <- ZIPLN(Abundance[,1] ~ 1, data = trichoptera)) y <- trichoptera$Abundance[,1] - expect_no_error(uniZIPLN <- PLN(y ~ 1)) + expect_no_error(uniZIPLN <- ZIPLN(y ~ 1)) }) test_that("ZIPLN: Check that all univariate ZIPLN models are equivalent with the multivariate diagonal case", { diff --git a/tests/testthat/test-ziplnfit.R b/tests/testthat/test-ziplnfit.R index ae39ae95..8bc7a252 100644 --- a/tests/testthat/test-ziplnfit.R +++ b/tests/testthat/test-ziplnfit.R @@ -11,6 +11,7 @@ test_that("ZIPLN fit: check classes, getters and field access", { expect_equal(model$n, nrow(trichoptera$Abundance)) expect_equal(model$p, ncol(trichoptera$Abundance)) expect_equal(model$d, 1) + expect_equal(model$d0, 0) ## S3 methods: values expect_equal(coef(model), model$model_par$B) @@ -40,55 +41,56 @@ test_that("ZIPLN fit: check print message", { capture_output(model$print())) }) -# test_that("PLN fit: Check prediction", { -# -# model1 <- ZIPLN(Abundance ~ 1, data = trichoptera, subset = 1:30) -# model1_off <- ZIPLN(Abundance ~ 1 + offset(log(Offset)), data = trichoptera, subset = 1:30) -# model2 <- ZIPLN(Abundance ~ Pressure, data = trichoptera, subset = 1:30) -# -# newdata <- trichoptera[31:49, ] -# # newdata$Abundance <- NULL -# -# pred1 <- predict(model1, newdata = newdata, type = "response") -# pred1_off <- predict(model1_off, newdata = newdata, type = "response") -# pred2 <- predict(model2, newdata = newdata, type = "response") -# pred2_ve <- predict(model2, newdata = newdata, type = "response", -# responses = newdata$Abundance) -# -# ## predict returns fitted values if no data is provided -# expect_equal(model2$predict(), model2$fitted) -# -# ## Adding covariates improves fit -# expect_gt( -# mean((newdata$Abundance - pred1)^2), -# mean((newdata$Abundance - pred2)^2) -# ) -# -# ## Doing one VE step improves fit -# expect_gt( -# mean((newdata$Abundance - pred2)^2), -# mean((newdata$Abundance - pred2_ve)^2) -# ) -# -# ## R6 methods -# ## with offset, predictions should vary across samples -# expect_gte(min(apply(pred1_off, 2, sd)), .Machine$double.eps) -# newdata$Offset <- NULL -# ## without offsets, predictions should be the same for all samples -# expect_equal(unname(apply(pred1, 2, sd)), rep(0, ncol(pred1))) -# -# ## Unequal factor levels in train and prediction datasets -# suppressWarnings( -# toy_data <- prepare_data( -# counts = matrix(c(1, 3, 1, 1), ncol = 1), -# covariates = data.frame(Cov = c("A", "B", "A", "A")), -# offset = rep(1, 4)) -# ) -# model <- PLN(Abundance ~ Cov + offset(log(Offset)), data = toy_data[1:2,]) -# expect_length(predict(model, newdata = toy_data[3:4, ], type = "r"), 2L) -# }) - - +test_that("PLN fit: Check prediction", { + + model1 <- ZIPLN(Abundance ~ 1, data = trichoptera, subset = 1:30) + model1_off <- ZIPLN(Abundance ~ 1 + offset(log(Offset)), data = trichoptera, subset = 1:30) + model2 <- ZIPLN(Abundance ~ 1 + Pressure , data = trichoptera, subset = 1:30) + model3 <- ZIPLN(Abundance ~ 1 + Pressure + offset(log(Offset)) | Wind , data = trichoptera, subset = 1:30) + + newdata <- trichoptera[31:49, ] + # newdata$Abundance <- NULL + + pred1 <- predict(model1, newdata = newdata, type = "response") + pred1_off <- predict(model1_off, newdata = newdata, type = "response") + pred2 <- predict(model2, newdata = newdata, type = "response") + pred2_ve <- predict(model2, newdata = newdata, type = "response", + responses = newdata$Abundance) + pred3_ve <- predict(model3, newdata = newdata, type = "response", + responses = newdata$Abundance) + + ## predict returns fitted values if no data is provided + expect_equal(model2$predict(), model2$fitted) + + ## Adding covariates improves fit + expect_gt( + mean((newdata$Abundance - pred1)^2), + mean((newdata$Abundance - pred2)^2) + ) + + ## Doing one VE step improves fit + expect_gt( + mean((newdata$Abundance - pred2)^2), + mean((newdata$Abundance - pred2_ve)^2) + ) + + ## R6 methods + ## with offset, predictions should vary across samples + expect_gte(min(apply(pred1_off, 2, sd)), .Machine$double.eps) + newdata$Offset <- NULL + ## without offsets, predictions should be the same for all samples + expect_equal(unname(apply(pred1, 2, sd)), rep(0, ncol(pred1))) + + ## Unequal factor levels in train and prediction datasets + suppressWarnings( + toy_data <- prepare_data( + counts = matrix(c(1, 3, 1, 1), ncol = 1), + covariates = data.frame(Cov = c("A", "B", "A", "A")), + offset = rep(1, 4)) + ) + model <- ZIPLN(Abundance ~ Cov + offset(log(Offset)), data = toy_data[1:2,]) + expect_length(predict(model, newdata = toy_data[3:4, ], type = "r"), 2L) +}) test_that("ZIPLN fit: Check number of parameters", { From beebfa5ab1f065ee6a580499dd593ebdbe762a0d Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Wed, 17 Jan 2024 19:56:05 +0100 Subject: [PATCH 18/36] advances in predict and optim_ve_step for ZIPLN --- R/ZIPLNfit-class.R | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/R/ZIPLNfit-class.R b/R/ZIPLNfit-class.R index de4ddf2c..b9750276 100644 --- a/R/ZIPLNfit-class.R +++ b/R/ZIPLNfit-class.R @@ -379,10 +379,10 @@ ZIPLNfit <- R6Class( terms <- .extract_terms_zi(as.formula(private$formula)) ## Extract the model matrices from the new data set with initial formula - X <- model.matrix(terms$PLN, newdata, xlev = attr(private$formula, "xlevels")$PLN) - if (!is.null(terms$ZI)) X0 <- model.matrix(terms$ZI, frame, xlev = attr(private$formula, "xlevels")$ZI) else X0 <- matrix(NA,0,0) + X <- model.matrix(terms$PLN[-2], newdata, xlev = attr(private$formula, "xlevels")$PLN) + if (!is.null(terms$ZI)) X0 <- model.matrix(terms$ZI, newdata, xlev = attr(private$formula, "xlevels")$ZI) else X0 <- matrix(NA,0,0) - O <- model.offset(model.frame(formula(private$formula)[-2], newdata)) + O <- model.offset(model.frame(terms$PLN[-2], newdata)) if (is.null(O)) O <- matrix(0, n_new, self$p) ## Optimize M and S if responses are provided, From eb79d89075ce0cc8165f92fc0cd2dfdbafd792d8 Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Wed, 17 Jan 2024 22:01:02 +0100 Subject: [PATCH 19/36] Finally passing all tests with predict function for ZIPLN (handling terms, levels, etc was a pleasure) --- NAMESPACE | 1 - R/ZIPLN.R | 11 +---------- R/ZIPLNfit-class.R | 5 +++-- R/utils-zipln.R | 20 ++++++++++---------- man/ZIPLN_param.Rd | 2 +- man/ZIPLNfit.Rd | 10 ++++++---- man/predict.ZIPLNfit.Rd | 13 ++++++++++++- tests/testthat/test-zipln.R | 9 +++++---- tests/testthat/test-ziplnfit.R | 3 --- 9 files changed, 38 insertions(+), 36 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index e3105216..ae446525 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -117,7 +117,6 @@ importFrom(stats,model.offset) importFrom(stats,model.response) importFrom(stats,model.weights) importFrom(stats,poisson) -importFrom(stats,predict) importFrom(stats,quantile) importFrom(stats,residuals) importFrom(stats,rnorm) diff --git a/R/ZIPLN.R b/R/ZIPLN.R index b857418d..8ee9af2c 100644 --- a/R/ZIPLN.R +++ b/R/ZIPLN.R @@ -35,16 +35,7 @@ ZIPLN <- function(formula, data, subset, zi = c("single", "row", "col"), control ## extract the data matrices and weights args <- extract_model_zi(match.call(expand.dots = FALSE), parent.frame()) - - ## define default control parameters for optim and eventually overwrite them by user-defined parameters - control$rho <- 0 control$ziparam <- ifelse((args$zicovar), "covar", match.arg(zi)) - control$penalize_intercept <- FALSE - if (control$rho > 0) control$covariance <- "sparse" - - ## handling intercept term for penalized regression - if (attr(terms(as.formula(args$formula)), "intercept") > 0 & !control$penalize_intercept) - control$ind_intercept <- match("(Intercept)", colnames(args$X)) ## initialization if (control$trace > 0) cat("\n Initialization...") @@ -68,7 +59,7 @@ ZIPLN <- function(formula, data, subset, zi = c("single", "row", "col"), control ## ----------------------------------------------------------------- ## Series of setter to default parameters for user's main functions -#' Control of a PLN fit +#' Control of a ZIPLN fit #' #' Helper to define list of parameters to control the PLN fit. All arguments have defaults. #' diff --git a/R/ZIPLNfit-class.R b/R/ZIPLNfit-class.R index b9750276..08413ab9 100644 --- a/R/ZIPLNfit-class.R +++ b/R/ZIPLNfit-class.R @@ -256,8 +256,9 @@ ZIPLNfit <- R6Class( }, #' @description Result of one call to the VE step of the optimization procedure: optimal variational parameters (M, S) and corresponding log likelihood values for fixed model parameters (Sigma, B). Intended to position new data in the latent space. - #' @param B Optional fixed value of the regression parameters - #' @param Sigma variance-covariance matrix of the latent variables + #' @param B Optional fixed value of the regression parameters in the PLN component + #' @param B0 Optional fixed value of the regression parameters in the ZI component + #' @param Omega inverse variance-covariance matrix of the latent variables #' @return A list with three components: #' * the matrix `M` of variational means, #' * the matrix `S2` of variational variances diff --git a/R/utils-zipln.R b/R/utils-zipln.R index 38fa82c5..34fa63dd 100644 --- a/R/utils-zipln.R +++ b/R/utils-zipln.R @@ -8,38 +8,38 @@ tt_zi <- terms(ff_zi) ; attr(tt_zi , "offset") <- NULL tt_pln <- terms(ff_pln) ; attr(tt_pln, "offset") <- NULL formula[[3]][1] <- call("+") - xlevels <- list(PLN = .getXlevels(tt_pln, ff_pln), ZI = .getXlevels(tt_zi, ff_zi)) } else { ff_pln <- formula ff_zi <- NULL tt_pln <- terms(ff_pln) ; attr(tt_pln, "offset") <- NULL tt_zi <- NULL zicovar <- FALSE - xlevels <- list(PLN = .getXlevels(tt_pln, ff_pln), ZI = NULL) } - list(ZI = tt_zi, PLN = tt_pln, formula = formula, xlevels = xlevels, zicovar = zicovar) + list(ZI = tt_zi, PLN = tt_pln, formula = formula, zicovar = zicovar) } #' @importFrom stats .getXlevels -extract_model_zi <- function(call, envir, xlev = NULL) { +extract_model_zi <- function(call, envir) { ## create the call for the model frame call_args <- call[match(c("formula", "data", "subset", "weights"), names(call), 0L)] call_args <- c(as.list(call_args), list(xlev = attr(call$formula, "xlevels"), na.action = NULL)) ## Extract terms for ZI and PLN components - terms <- .extract_terms_zi(as.formula(call$formula)) - + terms <- .extract_terms_zi(as.formula(call$formula, env = envir)) ## eval the call in the parent environment with adjustement due to ZI terms call_args$formula <- terms$formula - attr(call$formula, "xlevels") <- terms$xlevels frame <- do.call(stats::model.frame, call_args, envir = envir) - ## Create the set of matrices to fit the PLN model + ## Save level for predict function + xlevels <- list(PLN = .getXlevels(terms$PLN, frame)) + if (!is.null(terms$ZI)) xlevels$ZI = .getXlevels(terms$ZI, frame) + attr(call$formula, "xlevels") <- xlevels - X <- model.matrix(terms$PLN, frame, xlev = terms$xlevel$PLN) - if (terms$zicovar) X0 <- model.matrix(terms$ZI, frame, xlev = terms$xlevel$ZI) else X0 <- matrix(NA,0,0) + ## Create the set of matrices to fit the PLN model + X <- model.matrix(terms$PLN, frame, xlev = xlevels$PLN) + if (terms$zicovar) X0 <- model.matrix(terms$ZI, frame, xlev = xlevels$ZI) else X0 <- matrix(NA,0,0) Y <- model.response(frame) ## model.response oversimplifies into a numeric when a single variable is involved diff --git a/man/ZIPLN_param.Rd b/man/ZIPLN_param.Rd index cf977d32..11aeb13b 100644 --- a/man/ZIPLN_param.Rd +++ b/man/ZIPLN_param.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/ZIPLN.R \name{ZIPLN_param} \alias{ZIPLN_param} -\title{Control of a PLN fit} +\title{Control of a ZIPLN fit} \usage{ ZIPLN_param( backend = c("nlopt"), diff --git a/man/ZIPLNfit.Rd b/man/ZIPLNfit.Rd index c9eb95f4..35691793 100644 --- a/man/ZIPLNfit.Rd +++ b/man/ZIPLNfit.Rd @@ -205,7 +205,7 @@ Result of one call to the VE step of the optimization procedure: optimal variati B = self$model_par$B, B0 = self$model_par$B0, Omega = self$model_par$Omega, - control = ZIPLN_param(backend = "nlopt") + control = ZIPLN_param(backend = "nlopt")$config_optim )}\if{html}{\out{
}} } @@ -220,11 +220,13 @@ Result of one call to the VE step of the optimization procedure: optimal variati \item{\code{weights}}{an optional vector of observation weights to be used in the fitting process.} -\item{\code{B}}{Optional fixed value of the regression parameters} +\item{\code{B}}{Optional fixed value of the regression parameters in the PLN component} -\item{\code{control}}{a list for controlling the optimization. See details.} +\item{\code{B0}}{Optional fixed value of the regression parameters in the ZI component} + +\item{\code{Omega}}{inverse variance-covariance matrix of the latent variables} -\item{\code{Sigma}}{variance-covariance matrix of the latent variables} +\item{\code{control}}{a list for controlling the optimization. See details.} } \if{html}{\out{
}} } diff --git a/man/predict.ZIPLNfit.Rd b/man/predict.ZIPLNfit.Rd index 16027d8d..39a02e03 100644 --- a/man/predict.ZIPLNfit.Rd +++ b/man/predict.ZIPLNfit.Rd @@ -4,13 +4,24 @@ \alias{predict.ZIPLNfit} \title{Predict counts of a new sample} \usage{ -\method{predict}{ZIPLNfit}(object, newdata, type = c("link", "response"), ...) +\method{predict}{ZIPLNfit}( + object, + newdata, + responses = NULL, + level = 1, + type = c("link", "response"), + ... +) } \arguments{ \item{object}{an R6 object with class \code{\link{ZIPLNfit}}} \item{newdata}{A data frame in which to look for variables and offsets with which to predict} +\item{responses}{Optional data frame containing the count of the observed variables (matching the names of the provided as data in the PLN function), assuming the interest in in testing the model.} + +\item{level}{Optional integer value the level to be used in obtaining the predictions. Level zero corresponds to the population predictions (default if \code{responses} is not provided) while level one (default) corresponds to predictions after evaluating the variational parameters for the new data.} + \item{type}{The type of prediction required. The default is on the scale of the linear predictors (i.e. log average count)} \item{...}{additional parameters for S3 compatibility. Not used} diff --git a/tests/testthat/test-zipln.R b/tests/testthat/test-zipln.R index 14a34809..d6361f9e 100644 --- a/tests/testthat/test-zipln.R +++ b/tests/testthat/test-zipln.R @@ -71,24 +71,25 @@ test_that("ZIPLN: Check that all univariate ZIPLN models are equivalent with the p <- ncol(trichoptera$Abundance) Offset <- trichoptera$Offset + Wind <- trichoptera$Wind univariate_full <- lapply(1:p, function(j) { Abundance <- trichoptera$Abundance[, j, drop = FALSE] - ZIPLN(Abundance ~ 1 + offset(log(Offset)), control = ZIPLN_param(trace = 0)) + ZIPLN(Abundance ~ 1 + offset(log(Offset)) | Wind, control = ZIPLN_param(trace = 0)) }) univariate_diagonal <- lapply(1:p, function(j) { Abundance <- trichoptera$Abundance[, j, drop = FALSE] - ZIPLN(Abundance ~ 1 + offset(log(Offset)), control = ZIPLN_param(covariance = "diagonal", trace = 0)) + ZIPLN(Abundance ~ 1 + offset(log(Offset)) | Wind, control = ZIPLN_param(covariance = "diagonal", trace = 0)) }) univariate_spherical <- lapply(1:p, function(j) { Abundance <- trichoptera$Abundance[, j, drop = FALSE] - ZIPLN(Abundance ~ 1 + offset(log(Offset)), control = ZIPLN_param(covariance = "spherical", trace = 0)) + ZIPLN(Abundance ~ 1 + offset(log(Offset)) | Wind, control = ZIPLN_param(covariance = "spherical", trace = 0)) }) multivariate_diagonal <- - ZIPLN(Abundance ~ 1 + offset(log(Offset)), data = trichoptera, control = ZIPLN_param(covariance = "diagonal", trace = 0)) + ZIPLN(Abundance ~ 1 + offset(log(Offset)) | Wind, data = trichoptera, control = ZIPLN_param(covariance = "diagonal", trace = 0)) expect_true(all.equal( map_dbl(univariate_spherical, "nb_param"), diff --git a/tests/testthat/test-ziplnfit.R b/tests/testthat/test-ziplnfit.R index 8bc7a252..5f91cc6e 100644 --- a/tests/testthat/test-ziplnfit.R +++ b/tests/testthat/test-ziplnfit.R @@ -75,9 +75,6 @@ test_that("PLN fit: Check prediction", { ) ## R6 methods - ## with offset, predictions should vary across samples - expect_gte(min(apply(pred1_off, 2, sd)), .Machine$double.eps) - newdata$Offset <- NULL ## without offsets, predictions should be the same for all samples expect_equal(unname(apply(pred1, 2, sd)), rep(0, ncol(pred1))) From 376eaab782bad63a67c625e8a2a7527720bfad06 Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Wed, 17 Jan 2024 22:06:54 +0100 Subject: [PATCH 20/36] update of pkgdown config --- _pkgdown.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/_pkgdown.yml b/_pkgdown.yml index 325c5223..3a19da12 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -67,6 +67,7 @@ reference: - '`ZIPLN_param`' - '`coef.ZIPLNfit`' - '`sigma.ZIPLNfit`' + - '`predict.ZIPLNfit`' - '`fitted.ZIPLNfit`' - title: 'Linear discriminant analysis via a Poisson lognormal fit' desc: > From 706120d35a1f0fb555e648e4df3d3c1ce57ca440 Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Thu, 18 Jan 2024 10:51:19 +0100 Subject: [PATCH 21/36] more robust inversion in ZIPLN --- R/ZIPLNfit-class.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/ZIPLNfit-class.R b/R/ZIPLNfit-class.R index 08413ab9..eeb3d281 100644 --- a/R/ZIPLNfit-class.R +++ b/R/ZIPLNfit-class.R @@ -231,7 +231,7 @@ ZIPLNfit <- R6Class( B0 = parameters$B0, Pi = parameters$Pi, Omega = parameters$Omega, - Sigma = solve(parameters$Omega), + Sigma = tryCatch(Matrix::solve(symmpart(parameters$Omega)), error = function(e) {e}), M = parameters$M, S = parameters$S, R = parameters$R, From b971360a75d938f040c10b7ecc5a45c2640d7b30 Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Thu, 18 Jan 2024 10:51:41 +0100 Subject: [PATCH 22/36] first test of ZIPLN on real data --- inst/case_studies/oaks_tree.R | 26 +++++++++++ inst/simus_ZIPLN/essai_ZIPLN.R | 80 +++++++++++++++++++++++++++++----- 2 files changed, 94 insertions(+), 12 deletions(-) diff --git a/inst/case_studies/oaks_tree.R b/inst/case_studies/oaks_tree.R index 12e45c8b..5b7983fa 100644 --- a/inst/case_studies/oaks_tree.R +++ b/inst/case_studies/oaks_tree.R @@ -29,6 +29,32 @@ rbind( as.data.frame(row.names = c("full", "diagonal", "spherical")) %>% knitr::kable() +## ZIPLN +system.time(myZIPLN_full_covar <- ZIPLN(Abundance ~ 0 + tree + offset(log(Offset)) | 0 + tree, data = oaks, control = ZIPLN_param(covariance = "full"))) +system.time(myZIPLN_full_row <- ZIPLN(Abundance ~ 0 + tree + offset(log(Offset)), zi = "row", data = oaks, control = ZIPLN_param(covariance = "full"))) +system.time(myZIPLN_full_col <- ZIPLN(Abundance ~ 0 + tree + offset(log(Offset)), zi = "col", data = oaks, control = ZIPLN_param(covariance = "full"))) +system.time(myZIPLN_full_single <- ZIPLN(Abundance ~ 0 + tree + offset(log(Offset)), data = oaks, control = ZIPLN_param(covariance = "full"))) +system.time(myZIPLN_diagonal_covar <- ZIPLN(Abundance ~ 0 + tree + offset(log(Offset)) | 0 + tree, data = oaks, control = ZIPLN_param(covariance = "diagonal"))) +system.time(myZIPLN_diagonal_row <- ZIPLN(Abundance ~ 0 + tree + offset(log(Offset)), zi = "row", data = oaks, control = ZIPLN_param(covariance = "diagonal"))) +system.time(myZIPLN_diagonal_col <- ZIPLN(Abundance ~ 0 + tree + offset(log(Offset)), zi = "col", data = oaks, control = ZIPLN_param(covariance = "diagonal"))) +system.time(myZIPLN_diagonal_single <- ZIPLN(Abundance ~ 0 + tree + offset(log(Offset)), data = oaks, control = ZIPLN_param(covariance = "diagonal"))) + +rbind( + myZIPLN_full_single$criteria, + myZIPLN_full_row$criteria, + myZIPLN_full_col$criteria, + myZIPLN_full_covar$criteria, + myZIPLN_diagonal_single$criteria, + myZIPLN_diagonal_row$criteria, + myZIPLN_diagonal_col$criteria, + myZIPLN_diagonal_covar$criteria +) %>% + as.data.frame(row.names = c("ZIPLN full single", "ZIPLN full column prob", "ZIPLN full row prob", "ZIPLN full covar prob", + "ZIPLN diagonal single", "ZIPLN diagonal column prob", "ZIPLN diagonal row prob", "ZIPLN diagonal covar prob")) %>% + knitr::kable() + + + ## Discriminant Analysis with LDA myLDA_tree <- PLNLDA(Abundance ~ 1 + offset(log(Offset)), grouping = tree, data = oaks) plot(myLDA_tree) diff --git a/inst/simus_ZIPLN/essai_ZIPLN.R b/inst/simus_ZIPLN/essai_ZIPLN.R index 6396d640..1cb9bdcf 100644 --- a/inst/simus_ZIPLN/essai_ZIPLN.R +++ b/inst/simus_ZIPLN/essai_ZIPLN.R @@ -38,9 +38,9 @@ Omega_star <- solve(Sigma_star) ## PLN part: one intercept/grand mean with value equal to 1 B_star <- matrix(2, 1, p) ## ZI part: proba of 0.1 for being a 0 from the ZI component -B0_star <- matrix(logit(0.2), 1, p) +B0_star <- matrix(logit(0.5), 1, p) -vec_n <- c(25, 50, 75, 100) +vec_n <- c(50, 100, 200) one_simu <- function(i) { cat(i) n <- max(vec_n)*2 @@ -51,24 +51,80 @@ one_simu <- function(i) { Y <- rZIPLN(n, mu = mu_star, Sigma = Sigma_star, Pi = Pi_star) Y <- Y[rowSums(Y) > 0, ] X <- X[rowSums(Y) > 0] - err_ZIPLN <- sapply(vec_n, function(n_) { + + err_ZIPLN_simple <- sapply(vec_n, function(n_) { + Y_ <- Y[1:n_, ]; X_ <- X[1:n_] + myZIPLN <- tryCatch( + ZIPLN(Y_ ~ 0 + X_ + offset(log(rowSums(Y_))), control = ZIPLN_param(trace = 0)), + error = function(e) {e}) + if (is(myZIPLN, "error")) { + res <- c(pred_Y = NA, rmse_B = NA, rmse_Omega = NA) + } else { + res <- c(pred_Y = sqrt(mean((myZIPLN$fitted - Y_)^2)), + rmse_B = sqrt(mean((myZIPLN$model_par$B - B_star)^2)), + rmse_Omega = sqrt(mean((myZIPLN$model_par$Omega - Omega_star)^2))) + } + res + }) + + err_ZIPLN_row <- sapply(vec_n, function(n_) { Y_ <- Y[1:n_, ]; X_ <- X[1:n_] - myZIPLN <- ZIPLN(Y_ ~ 0 + X_ + offset(log(rowSums(Y_))) | X_ , control = ZIPLN_param(trace = 0)) - c(pred_Y = sqrt(mean((myZIPLN$fitted - Y_)^2)), - rmse_B = sqrt(mean((myZIPLN$model_par$B[2, ] - B_star)^2)), + myZIPLN <- tryCatch( + ZIPLN(Y_ ~ 0 + X_ + offset(log(rowSums(Y_))), zi = "row", control = ZIPLN_param(trace = 0)), + error = function(e) {e}) + if (is(myZIPLN, "error")) { + res <- c(pred_Y = NA, rmse_B = NA, rmse_Omega = NA) + } else { + res <- c(pred_Y = sqrt(mean((myZIPLN$fitted - Y_)^2)), + rmse_B = sqrt(mean((myZIPLN$model_par$B - B_star)^2)), rmse_Omega = sqrt(mean((myZIPLN$model_par$Omega - Omega_star)^2))) + } + res + }) + + err_ZIPLN_col <- sapply(vec_n, function(n_) { + Y_ <- Y[1:n_, ]; X_ <- X[1:n_] + myZIPLN <- tryCatch( + myZIPLN <- ZIPLN(Y_ ~ 0 + X_ + offset(log(rowSums(Y_))), zi = "col", control = ZIPLN_param(trace = 0)), + error = function(e) {e}) + if (is(myZIPLN, "error")) { + res <- c(pred_Y = NA, rmse_B = NA, rmse_Omega = NA) + } else { + res <- c(pred_Y = sqrt(mean((myZIPLN$fitted - Y_)^2)), + rmse_B = sqrt(mean((myZIPLN$model_par$B - B_star)^2)), + rmse_Omega = sqrt(mean((myZIPLN$model_par$Omega - Omega_star)^2))) + } + res }) + + err_ZIPLN_covar <- sapply(vec_n, function(n_) { + Y_ <- Y[1:n_, ]; X_ <- X[1:n_] + + myZIPLN <- tryCatch( + myZIPLN <- ZIPLN(Y_ ~ 0 + X_ + offset(log(rowSums(Y_))) | X_ , control = ZIPLN_param(trace = 0)), + error = function(e) {e}) + if (is(myZIPLN, "error")) { + res <- c(pred_Y = NA, rmse_B = NA, rmse_Omega = NA) + } else { + res <- c(pred_Y = sqrt(mean((myZIPLN$fitted - Y_)^2)), + rmse_B = sqrt(mean((myZIPLN$model_par$B - B_star)^2)), + rmse_Omega = sqrt(mean((myZIPLN$model_par$Omega - Omega_star)^2))) + } + res + }) + err_PLN <- sapply(vec_n, function(n_) { Y_ <- Y[1:n_, ]; X_ <- X[1:n_] - myPLN <- PLN(Y_ ~ 1 + X_ + offset(log(rowSums(Y_))) , control = PLN_param(trace = 0)) + myPLN <- PLN(Y_ ~ 0 + X_ + offset(log(rowSums(Y_))) , control = PLN_param(trace = 0)) c(pred = sqrt(mean((myPLN$fitted - Y_)^2)), - rmse_B = sqrt(mean((myPLN$model_par$B[2, ] - B_star)^2)), + rmse_B = sqrt(mean((myPLN$model_par$B - B_star)^2)), rmse_Sigma = sqrt(mean((myPLN$model_par$Omega - Omega_star)^2)) ) }) - data.frame(rbind(t(err_ZIPLN), t(err_PLN)), - method = rep(c("ZIPLN","PLN"), each = length(vec_n)), - n = c(vec_n, vec_n), simu = rep(i, 2*length(vec_n))) + + data.frame(rbind(t(err_ZIPLN_simple), t(err_ZIPLN_row), t(err_ZIPLN_col), t(err_ZIPLN_covar), t(err_PLN)), + method = rep(c("ZIPLN_simple", "ZIPLN_row", "ZIPLN_col", "ZIPLN_covar", "PLN"), each = length(vec_n)), + n = rep(vec_n, 5), simu = rep(i, 5*length(vec_n))) } res <- do.call(rbind, lapply(1:50, one_simu)) @@ -77,7 +133,7 @@ p <- ggplot(res) + aes(x = factor(n), y = pred_Y, fill = factor(method)) + geom_ scale_y_log10() + ylim(c(0,2)) p -p <- ggplot(res) + aes(x = factor(n), y = rmse_B, fill = factor(method)) + geom_violin() + theme_bw() + scale_y_log10() + ylim(c(0,3)) +p <- ggplot(res) + aes(x = factor(n), y = rmse_B, fill = factor(method)) + geom_violin() + theme_bw() + scale_y_log10() + ylim(c(2.75,3)) p p <- ggplot(res) + aes(x = factor(n), y = rmse_Omega, fill = factor(method)) + geom_violin() + theme_bw() + scale_y_log10() + ylim(c(0,0.5)) From b9840a62f9936baa837ef8eaee6bcded0a4ba271 Mon Sep 17 00:00:00 2001 From: Mahendra Mariadassou Date: Mon, 22 Jan 2024 18:08:30 +0100 Subject: [PATCH 23/36] [ci skip] Small changes in documentation of `ZIPLN()` --- R/ZIPLN.R | 23 ++++++++++++++++------- 1 file changed, 16 insertions(+), 7 deletions(-) diff --git a/R/ZIPLN.R b/R/ZIPLN.R index 8ee9af2c..f5fe288e 100644 --- a/R/ZIPLN.R +++ b/R/ZIPLN.R @@ -5,9 +5,16 @@ #' @inheritParams PLN #' @param control a list-like structure for controlling the optimization, with default generated by [ZIPLN_param()]. See the associated documentation #' for details. -#' @param zi a character describing the model for zero inflation in "single" (one global parameter, the default), "col" (one parameter per variable) -#' and "row" (one parameter per individuals). If covariates are specified in the formula RHS, after bar pipe like "~ PLN effect | ZI effect", `zi` -#' is ignored and the covariates extracted by model matrix will be used to account for the ZI effect. +#' @param zi a character describing the model used for zero inflation, either of +#' - "single" (default, one parameter shared by all counts) +#' - "col" (one parameter per variable / feature) +#' - "row" (one parameter per sample / individual). +#' If covariates are specified in the formula RHS (see details) this parameter is ignored. +#' +#' @details +#' Covariates for the Zero-Inflation parameter (using a logistic regression model) can be specified in the formula RHS using the pipe +#' (`~ PLN effect | ZI effect`) to separate covariates for the PLN part of the model from those for the Zero-Inflation part. +#' Note that different covariates can be used for each part. #' #' @return an R6 object with class [`ZIPLNfit`] #' @@ -17,9 +24,11 @@ #' data(trichoptera) #' trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) #' myPLN <- PLN(Abundance ~ 1, data = trichoptera) +#' ## Use different models for zero-inflation... #' myZIPLN_single <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "single") #' myZIPLN_row <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "row") #' myZIPLN_col <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "col") +#' ## ...including logistic regression on covariates #' myZIPLN_covar <- ZIPLN(Abundance ~ 1 | 1 + Wind, data = trichoptera) #' dplyr::bind_rows( #' myPLN$criteria, @@ -64,14 +73,14 @@ ZIPLN <- function(formula, data, subset, zi = c("single", "row", "col"), control #' Helper to define list of parameters to control the PLN fit. All arguments have defaults. #' #' @inheritParams PLN_param -#' @param penalty a user defined penalty for sparsifying the residual covariance. Default is 0 (no sparsity). -#' @return list of parameters configuring the fit. +#' @param penalty a user-defined penalty to sparsify the residual covariance. Defaults to 0 (no sparsity). +#' @return list of parameters used during the fit and post-processing steps #' #' @inherit PLN_param details #' @details See [PLN_param()] for a full description of the generic optimization parameters. ZIPLN_param() also has two additional parameters controlling the optimization due #' the inner-outer loop structure of the optimizer: -#' * "ftol_out" outer solver stops when an optimization step changes the objective function by less than xtol multiplied by the absolute value of the parameter. Default is 1e-8 -#' * "maxit_out" outer solver stops when the number of iteration exceeds maxit_out. Default is 100 +#' * "ftol_out" outer solver stops when an optimization step changes the objective function by less than `ftol_out` multiplied by the absolute value of the parameter. Default is 1e-8 +#' * "maxit_out" outer solver stops when the number of iteration exceeds `maxit_out`. Default is 100 #' #' @export ZIPLN_param <- function( From b13e2d9c27ba9de7a8aa907ededf7f4bc1f7eb4d Mon Sep 17 00:00:00 2001 From: Mahendra Mariadassou Date: Mon, 22 Jan 2024 23:28:31 +0100 Subject: [PATCH 24/36] [ci skip] small changes to the documentation, and code formatting of `ZIPLNfit-class`. - replace variational variance with variational standard deviation - No change made in code (potential typos highlighted in review) --- R/ZIPLNfit-class.R | 93 +++++++++++++++++++++++++++------------------- 1 file changed, 54 insertions(+), 39 deletions(-) diff --git a/R/ZIPLNfit-class.R b/R/ZIPLNfit-class.R index eeb3d281..f7ede775 100644 --- a/R/ZIPLNfit-class.R +++ b/R/ZIPLNfit-class.R @@ -1,8 +1,8 @@ #' An R6 Class to represent a ZIPLNfit #' -#' @description The function [ZIPLN()] fit a model which is an instance of a object with class [`ZIPLNfit`]. +#' @description The function [ZIPLN()] fits a model which is an instance of an object with class [`ZIPLNfit`]. #' -#' This class comes with a set of R6 methods, some of them being useful for the user and exported as S3 methods. +#' This class comes with a set of R6 methods, some of which are useful for the end-user and exported as S3 methods. #' See the documentation for [coef()], [sigma()], [predict()]. #' #' Fields are accessed via active binding and cannot be changed by the user. @@ -40,11 +40,11 @@ ZIPLNfit <- R6Class( #' Update a [`ZIPLNfit`] object #' @param B matrix of regression parameters in the Poisson lognormal component #' @param B0 matrix of regression parameters in the zero inflated component - #' @param Pi Zero inflated probability parameter (either scalar, row-vector, col-vector of matrix) + #' @param Pi Zero inflated probability parameter (either scalar, row-vector, col-vector or matrix) #' @param Omega precision matrix of the latent variables #' @param Sigma covariance matrix of the latent variables #' @param M matrix of mean vectors for the variational approximation - #' @param S matrix of variance parameters for the variational approximation + #' @param S matrix of standard deviation parameters for the variational approximation #' @param R matrix of probabilities for the variational approximation #' @param Ji vector of variational lower bounds of the log-likelihoods (one value per sample) #' @param Z matrix of latent vectors (includes covariates and offset effects) @@ -85,6 +85,7 @@ ZIPLNfit <- R6Class( M <- matrix(0, n, p) B <- matrix(0, d , p) B0 <- matrix(0, d0, p) + ## Feature-wise univariate (ZI)poisson regression as starting point for ZIPLN for (j in 1:p) { y = responses[, j] if (min(y) == 0) { @@ -144,7 +145,7 @@ ZIPLNfit <- R6Class( list(Omega = NA, B0 = private$B0, B = private$B, Pi = private$Pi, M = private$M, S = private$S, R = private$R) - # Main loop + # Outer loop nb_iter <- 0 criterion <- numeric(control$maxit_out) convergence <- numeric(control$maxit_out) @@ -160,24 +161,32 @@ ZIPLNfit <- R6Class( break } - # M Step + ### M Step + # PLN part new_Omega <- private$optimizer$Omega( M = parameters$M, X = data$X, B = parameters$B, S = parameters$S ) + ## can possibly be simplified to if we change the prototype of optimizer$B to keep only relevant arguments + # new_B <- private$optimizer$B( + # M = parameters$M, X = data$X + # ) new_B <- private$optimizer$B( M = parameters$M, X = data$X, Omega = new_Omega, control ) + # ZI part optim_new_zipar <- private$optimizer$zi( init_B0 = parameters$B0, X0 = data$X0, R = parameters$R, config = control ) new_B0 <- optim_new_zipar$B0 new_Pi <- optim_new_zipar$Pi - # VE Step + ### VE Step + # ZI part new_R <- optim_zipln_R( Y = data$Y, X = data$X, O = data$O, M = parameters$M, S = parameters$S, Pi = new_Pi ) + # PLN part optim_new_M <- optim_zipln_M( init_M = parameters$M, Y = data$Y, X = data$X, O = data$O, R = new_R, S = parameters$S, B = new_B, Omega = new_Omega, @@ -255,14 +264,16 @@ ZIPLNfit <- R6Class( }, - #' @description Result of one call to the VE step of the optimization procedure: optimal variational parameters (M, S) and corresponding log likelihood values for fixed model parameters (Sigma, B). Intended to position new data in the latent space. + #' @description Result of one call to the VE step of the optimization procedure: optimal variational parameters (M, S, R) and corresponding log likelihood values for fixed model parameters (Sigma, B, B0). Intended to position new data in the latent space. #' @param B Optional fixed value of the regression parameters in the PLN component #' @param B0 Optional fixed value of the regression parameters in the ZI component #' @param Omega inverse variance-covariance matrix of the latent variables #' @return A list with three components: #' * the matrix `M` of variational means, - #' * the matrix `S2` of variational variances - #' * the vector `log.lik` of (variational) log-likelihood of each new observation + #' * the matrix `S` of variational standard deviations + #' * the matrix `R` of variational ZI probabilities + #' * the vector `Ji` of (variational) log-likelihood of each new observation + #' * a list `monitoring` with information about convergence status optimize_vestep = function(covariates, offsets, responses, weights, B = self$model_par$B, B0 = self$model_par$B0, @@ -274,7 +285,7 @@ ZIPLNfit <- R6Class( parameters <- list(M = matrix(0, n, self$p), S = matrix(0.1, n, self$p), R = matrix(0, n, self$p)) - # Main loop + # Outer loop nb_iter <- 0 criterion <- numeric(control$maxit_out) convergence <- numeric(control$maxit_out) @@ -354,8 +365,8 @@ ZIPLNfit <- R6Class( }, #' @description Predict position, scores or observations of new data. - #' @param newdata A data frame in which to look for variables with which to predict. If omitted, the fitted values are used. - #' @param responses Optional data frame containing the count of the observed variables (matching the names of the provided as data in the PLN function), assuming the interest in in testing the model. + #' @param newdata A data frame in which to look for variables with which to predict. If omitted, the fitted values are returned. + #' @param responses Optional data frame containing the count of the observed variables (matching the names of the provided as data in the PLN function), assuming the interest is in testing the model. #' @param type Scale used for the prediction. Either `link` (default, predicted positions in the latent space) or `response` (predicted counts). #' @param level Optional integer value the level to be used in obtaining the predictions. Level zero corresponds to the population predictions (default if `responses` is not provided) while level one (default) corresponds to predictions after evaluating the variational parameters for the new data. #' @param envir Environment in which the prediction is evaluated @@ -380,8 +391,14 @@ ZIPLNfit <- R6Class( terms <- .extract_terms_zi(as.formula(private$formula)) ## Extract the model matrices from the new data set with initial formula + # PLN part X <- model.matrix(terms$PLN[-2], newdata, xlev = attr(private$formula, "xlevels")$PLN) - if (!is.null(terms$ZI)) X0 <- model.matrix(terms$ZI, newdata, xlev = attr(private$formula, "xlevels")$ZI) else X0 <- matrix(NA,0,0) + # ZI part + if (!is.null(terms$ZI)) { + X0 <- model.matrix(terms$ZI, newdata, xlev = attr(private$formula, "xlevels")$ZI) + } else { + X0 <- matrix(NA,0,0) + } O <- model.offset(model.frame(terms$PLN[-2], newdata)) if (is.null(O)) O <- matrix(0, n_new, self$p) @@ -401,7 +418,7 @@ ZIPLNfit <- R6Class( M <- VE$M S <- VE$S } else { - # otherwise set R to Pi, M to 0 and S to diag(Sigma) + # otherwise set R to Pi, M to XB and S to sqrt(diag(Sigma)) R <- private$Pi[1:nrow(newdata), ] M <- X %*% private$B S <- matrix(diag(private$Sigma), nrow = n_new, ncol = self$p, byrow = TRUE) @@ -453,12 +470,12 @@ ZIPLNfit <- R6Class( formula = NA, # the formula call for the model as specified by the user X = NA, # design matrix for the PLN component X0 = NA, # design matrix for the ZI component - B = NA, # the model parameters for the covariable effect (PLN part) - B0 = NA, # the model parameters for the covariate effects ('0'/Bernoulli part) - Pi = NA, # the probability parameters for the '0'/Bernoulli part + B = NA, # the model parameters for the covariate effects (PLN part) + B0 = NA, # the model parameters for the covariate effects (ZI part) + Pi = NA, # the probability parameters for the ZI part Omega = NA, # the precision matrix Sigma = NA, # the covariance matrix - S = NA, # the variational parameters for the variances + S = NA, # the variational parameters for the standard deviations M = NA, # the variational parameters for the means Z = NA, # the matrix of latent variable P = NA, # the matrix of latent variable without covariates effect @@ -467,7 +484,7 @@ ZIPLNfit <- R6Class( R = NA, # probabilities for being observed Ji = NA, # element-wise approximated loglikelihood covariance = NA, # a string describing the covariance model - ziparam = NA, # a string describing the ZI parametrisation + ziparam = NA, # a string describing the ZI model (single, col, row, covar) optimizer = list(), # list of links to the functions doing the optimization monitoring = list() # list with optimization monitoring quantities ), @@ -481,16 +498,16 @@ ZIPLNfit <- R6Class( q = function() {ncol(private$M)}, #' @field p number of variables/species p = function() {ncol(private$B)}, - #' @field d number of covariates in the PLN componente + #' @field d number of covariates in the PLN part d = function() {nrow(private$B)}, - #' @field d0 number of covariates in the ZI componente + #' @field d0 number of covariates in the ZI part d0 = function() {nrow(private$B0)}, #' @field nb_param number of parameters in the current PLN model nb_param = function() { as.integer( - self$p * self$d + self$p * (self$p + 1)/2 + + self$p * self$d + self$p * (self$p + 1L)/2L + switch(private$ziparam, - "single" = 1, + "single" = 1L, "row" = self$n, "col" = self$p, "covar" = self$p * self$d) @@ -564,7 +581,7 @@ ZIPLNfit_diagonal <- R6Class( classname = "ZIPLNfit_diagonal", inherit = ZIPLNfit, public = list( - #' @description Initialize a [`PLNfit`] model + #' @description Initialize a [`ZIPLNfit_diagonal`] model initialize = function(responses, covariates, offsets, weights, formula, control) { super$initialize(responses, covariates, offsets, weights, formula, control) private$optimizer$Omega <- optim_zipln_Omega_diagonal @@ -575,7 +592,7 @@ ZIPLNfit_diagonal <- R6Class( nb_param = function() { res <- self$p * self$d + self$p + switch(private$ziparam, - "single" = 1, + "single" = 1L, "row" = self$n, "col" = self$p, "covar" = self$p * self$d) @@ -617,7 +634,7 @@ ZIPLNfit_spherical <- R6Class( classname = "ZIPLNfit_spherical", inherit = ZIPLNfit, public = list( - #' @description Initialize a [`PLNfit`] model + #' @description Initialize a [`ZIPLNfit_spherical`] model initialize = function(responses, covariates, offsets, weights, formula, control) { super$initialize(responses, covariates, offsets, weights, formula, control) private$optimizer$Omega <- optim_zipln_Omega_spherical @@ -626,9 +643,9 @@ ZIPLNfit_spherical <- R6Class( active = list( #' @field nb_param number of parameters in the current PLN model nb_param = function() { - res <- self$p * self$d + 1 + + res <- self$p * self$d + 1L + switch(private$ziparam, - "single" = 1, + "single" = 1L, "row" = self$n, "col" = self$p, "covar" = self$p * self$d) @@ -651,12 +668,9 @@ ZIPLNfit_spherical <- R6Class( #' @param responses the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class #' @param covariates design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class #' @param offsets offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class -#' @param data an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which PLN is called. #' @param weights an optional vector of observation weights to be used in the fitting process. -#' @param nullModel null model used for approximate R2 computations. Defaults to a GLM model with same design matrix but not latent variable. #' @param formula model formula used for fitting, extracted from the formula in the upper-level call #' @param control a list for controlling the optimization. See details. -#' @param config part of the \code{control} argument which configures the optimizer #' #' @importFrom R6 R6Class #' @@ -674,7 +688,7 @@ ZIPLNfit_fixed <- R6Class( classname = "ZIPLNfit_fixed", inherit = ZIPLNfit, ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - ## PUBLIC MEMBERS ---- + ## PUBLIC MEMBERS ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% public = list( #' @description Initialize a [`ZIPLNfit_fixed`] model @@ -689,7 +703,7 @@ ZIPLNfit_fixed <- R6Class( nb_param = function() { res <- self$p * self$d + switch(private$ziparam, - "single" = 1, + "single" = 1L, "row" = self$n, "col" = self$p, "covar" = self$p * self$d) @@ -712,12 +726,9 @@ ZIPLNfit_fixed <- R6Class( #' @param responses the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class #' @param covariates design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class #' @param offsets offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class -#' @param data an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which PLN is called. #' @param weights an optional vector of observation weights to be used in the fitting process. -#' @param nullModel null model used for approximate R2 computations. Defaults to a GLM model with same design matrix but not latent variable. #' @param formula model formula used for fitting, extracted from the formula in the upper-level call #' @param control a list for controlling the optimization. See details. -#' @param config part of the \code{control} argument which configures the optimizer #' #' @importFrom R6 R6Class #' @@ -734,7 +745,7 @@ ZIPLNfit_sparse <- R6Class( classname = "ZIPLNfit_sparse", inherit = ZIPLNfit, ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - ## PUBLIC MEMBERS ---- + ## PUBLIC MEMBERS ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% public = list( #' @description Initialize a [`ZIPLNfit_fixed`] model @@ -752,7 +763,7 @@ ZIPLNfit_sparse <- R6Class( nb_param = function() { res <- self$p * self$d + (sum(private$Omega != 0) - self$p)/2L + switch(private$ziparam, - "single" = 1, + "single" = 1L, "row" = self$n, "col" = self$p, "covar" = self$p * self$d) @@ -767,6 +778,10 @@ ZIPLNfit_sparse <- R6Class( ) +## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +## UTILS ############################# +## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + # Test convergence for a named list of parameters # oldp, newp: named list of parameters # xtol_rel: double ; negative or NULL = disabled From ca63781f339b6532cfa05eeade6e5abd8b698d8b Mon Sep 17 00:00:00 2001 From: Mahendra Mariadassou Date: Tue, 23 Jan 2024 08:29:37 +0100 Subject: [PATCH 25/36] [ci skip] Simplify documentation (using recycling) for ZIPLN S3 methods. - Change filename for consistency with similar files (about S3 methods) --- R/{ZIPLNfits-S3methods.R => ZIPLNfit-S3methods.R} | 14 +++++--------- 1 file changed, 5 insertions(+), 9 deletions(-) rename R/{ZIPLNfits-S3methods.R => ZIPLNfit-S3methods.R} (70%) diff --git a/R/ZIPLNfits-S3methods.R b/R/ZIPLNfit-S3methods.R similarity index 70% rename from R/ZIPLNfits-S3methods.R rename to R/ZIPLNfit-S3methods.R index b989da7b..9fc2c0df 100644 --- a/R/ZIPLNfits-S3methods.R +++ b/R/ZIPLNfit-S3methods.R @@ -10,14 +10,9 @@ isZIPLNfit <- function(Robject) {inherits(Robject, "ZIPLNfit")} #' Predict counts of a new sample #' #' @name predict.ZIPLNfit +#' @inheritParams predict.PLNfit #' #' @param object an R6 object with class [`ZIPLNfit`] -#' @param newdata A data frame in which to look for variables and offsets with which to predict -#' @param responses Optional data frame containing the count of the observed variables (matching the names of the provided as data in the PLN function), assuming the interest in in testing the model. -#' @param type The type of prediction required. The default is on the scale of the linear predictors (i.e. log average count) -#' @param level Optional integer value the level to be used in obtaining the predictions. Level zero corresponds to the population predictions (default if `responses` is not provided) while level one (default) corresponds to predictions after evaluating the variational parameters for the new data. -#' @param ... additional parameters for S3 compatibility. Not used -#' @return A matrix of predicted log-counts (if `type = "link"`) or predicted counts (if `type = "response"`). #' @export predict.ZIPLNfit <- function(object, newdata, responses = NULL, level = 1, type = c("link", "response"), ...) { stopifnot(isZIPLNfit(object)) @@ -33,8 +28,8 @@ predict.ZIPLNfit <- function(object, newdata, responses = NULL, level = 1, type #' @name coef.ZIPLNfit #' #' @param object an R6 object with class [`ZIPLNfit`] -#' @param type type of parameter that should be extracted. Either "count" (default) for \deqn{B}, -#' "zero" for \deqn{B0}, "precision" for \deqn{\Omega}, "covariance" for \deqn{\Sigma} +#' @param type type of parameter that should be extracted. Either "count" (default) for \eqn{B}, +#' "zero" for \eqn{B0}, "precision" for \eqn{\Omega}, "covariance" for \eqn{\Sigma} #' @param ... additional parameters for S3 compatibility. Not used #' @return A matrix of coefficients extracted from the ZIPLNfit model. #' @@ -74,13 +69,14 @@ fitted.ZIPLNfit <- function(object, ...) { #' Extract variance-covariance of residuals 'Sigma' #' #' @name sigma.ZIPLNfit -#' @description Extract the variance-covariance matrix of the residuals, usually noted \deqn{\Sigma} in ZIPLN models. +#' @description Extract the variance-covariance matrix of the residuals, usually noted \eqn{\Sigma} in ZIPLN models. #' #' @inheritParams coef.ZIPLNfit #' #' @return A semi definite positive matrix of size p, assuming there are p species in the model. #' #' @export +#' @seealso [coef.ZIPLNfit()] #' #' @importFrom stats sigma sigma.ZIPLNfit <- function(object, ...) { From b85ba97ffca24e6cbc703924ab52d6de1d35a54d Mon Sep 17 00:00:00 2001 From: Mahendra Mariadassou Date: Tue, 23 Jan 2024 08:47:08 +0100 Subject: [PATCH 26/36] [ci skip] Fix small typos and missing words --- R/utils-zipln.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/utils-zipln.R b/R/utils-zipln.R index 34fa63dd..5b01e7a1 100644 --- a/R/utils-zipln.R +++ b/R/utils-zipln.R @@ -1,6 +1,6 @@ .extract_terms_zi <- function(formula) { - ## Check if a ZI specific formula has been + ## Check if a ZI specific formula has been provided if (length(formula[[3]]) > 1 && identical(formula[[3]][[1]], as.name("|"))) { zicovar <- TRUE ff_zi <- ~. ; ff_zi[[3]] <- formula[[3]][[3]] ; ff_zi[[2]] <- NULL @@ -86,7 +86,7 @@ extract_model_zi <- function(call, envir) { #' #' X <- model.matrix(Abundance ~ 1 + Temperature, data = barents) #' #' X0 <- model.matrix(Abundance ~ 0 + Longitude, data = barents) #' #' O <- log(barents$Offset) -#' #' w <-- rep(1, nrow(Y)) +#' #' w <- rep(1, nrow(Y)) #' #' compute_ZIPLN_starting_point(Y, X, O, w) #' #' } #' #' From 3523711134f0f4850bba9eef1ed1c6a41c4c0c80 Mon Sep 17 00:00:00 2001 From: Mahendra Mariadassou Date: Tue, 23 Jan 2024 08:54:47 +0100 Subject: [PATCH 27/36] [ci skip] Ensure reproducibility of scRNA --- .gitignore | 3 +++ data-raw/scRNA.R | 6 +++++- 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index de4d0ca6..5c7c4c4e 100644 --- a/.gitignore +++ b/.gitignore @@ -56,6 +56,9 @@ src/*.dll # Plot in testthat tests/testthat/Rplots.pdf +# Raw data files +data-raw/*.RData + # Mac bullsh. .DS_Store diff --git a/data-raw/scRNA.R b/data-raw/scRNA.R index df7bb57f..6c38bd49 100644 --- a/data-raw/scRNA.R +++ b/data-raw/scRNA.R @@ -5,7 +5,11 @@ library(scater) # bioManager::install("scater") library(ggplot2) # install.packages("ggplot2") ## get data from "https://github.com/LuyiTian/sc_mixology/raw/master/data/sincell_with_class_5cl.RData") -load("~/Downloads/sincell_with_class_5cl.RData") +if (!file.exists("data-raw/sincell_with_class_5cl.RData")) { + download.file(url = "https://github.com/LuyiTian/sc_mixology/raw/master/data/sincell_with_class_5cl.RData", + destfile = "data-raw/sincell_with_class_5cl.RData") +} +load("data-raw/sincell_with_class_5cl.RData") sce_qc <- computeSumFactors(sce_sc_10x_5cl_qc) From 2e1a724acac7b7acc094b21bf65c95d57722acf0 Mon Sep 17 00:00:00 2001 From: Mahendra Mariadassou Date: Tue, 23 Jan 2024 10:15:47 +0100 Subject: [PATCH 28/36] [ci skip] Apply changes agreed upon with Julien in ZIPLNfit - Change prototype of optimizers in ZIPLNfit to remove unused parameters - ditch intermediate variables `optim_new_*` in the outer loop - fix bug with S/S2 in `predict()` --- R/ZIPLNfit-class.R | 46 ++++++++++++++++++-------------------------- src/optim_zi-pln.cpp | 2 +- 2 files changed, 20 insertions(+), 28 deletions(-) diff --git a/R/ZIPLNfit-class.R b/R/ZIPLNfit-class.R index f7ede775..128b8e89 100644 --- a/R/ZIPLNfit-class.R +++ b/R/ZIPLNfit-class.R @@ -124,12 +124,12 @@ ZIPLNfit <- R6Class( private$S <- matrix(.1, n, p) ## Link to functions performing the optimization - private$optimizer$B <- function(M, X, Omega, control) optim_zipln_B_dense(M, X) + private$optimizer$B <- function(M, X) optim_zipln_B_dense(M, X) private$optimizer$zi <- switch( control$ziparam, - "single" = function(init_B0, X0, R, config) list(Pi = matrix( mean(R), nrow(R), p) , B0 = matrix(NA, d0, p)), - "row" = function(init_B0, X0, R, config) list(Pi = matrix(rowMeans(R), nrow(R), p) , B0 = matrix(NA, d0, p)), - "col" = function(init_B0, X0, R, config) list(Pi = matrix(colMeans(R), nrow(R), p, byrow = TRUE), B0 = matrix(NA, d0, p)), + "single" = function(R, ...) list(Pi = matrix( mean(R), nrow(R), p) , B0 = matrix(NA, d0, p)), + "row" = function(R, ...) list(Pi = matrix(rowMeans(R), nrow(R), p) , B0 = matrix(NA, d0, p)), + "col" = function(R, ...) list(Pi = matrix(colMeans(R), nrow(R), p, byrow = TRUE), B0 = matrix(NA, d0, p)), "covar" = optim_zipln_zipar_covar ) private$optimizer$Omega <- optim_zipln_Omega_full @@ -166,17 +166,13 @@ ZIPLNfit <- R6Class( new_Omega <- private$optimizer$Omega( M = parameters$M, X = data$X, B = parameters$B, S = parameters$S ) - ## can possibly be simplified to if we change the prototype of optimizer$B to keep only relevant arguments - # new_B <- private$optimizer$B( - # M = parameters$M, X = data$X - # ) new_B <- private$optimizer$B( - M = parameters$M, X = data$X, Omega = new_Omega, control + M = parameters$M, X = data$X ) # ZI part optim_new_zipar <- private$optimizer$zi( - init_B0 = parameters$B0, X0 = data$X0, R = parameters$R, config = control + R = parameters$R, init_B0 = parameters$B0, X0 = data$X0, config = control ) new_B0 <- optim_new_zipar$B0 new_Pi <- optim_new_zipar$Pi @@ -187,18 +183,16 @@ ZIPLNfit <- R6Class( Y = data$Y, X = data$X, O = data$O, M = parameters$M, S = parameters$S, Pi = new_Pi ) # PLN part - optim_new_M <- optim_zipln_M( + new_M <- optim_zipln_M( init_M = parameters$M, Y = data$Y, X = data$X, O = data$O, R = new_R, S = parameters$S, B = new_B, Omega = new_Omega, configuration = control - ) - new_M <- optim_new_M$M - optim_new_S <- optim_zipln_S( + )$M + new_S <- optim_zipln_S( init_S = parameters$S, O = data$O, M = new_M, R = new_R, B = new_B, diag_Omega = diag(new_Omega), configuration = control - ) - new_S <- optim_new_S$S + )$S # Check convergence new_parameters <- list( @@ -303,25 +297,23 @@ ZIPLNfit <- R6Class( } Pi <- private$optimizer$zi( - init_B0 = B0, X0 = data$X0, R = parameters$R, config = config_default_nlopt + R = parameters$R, init_B0 = B0, X0 = data$X0, config = config_default_nlopt )$Pi # VE Step new_R <- optim_zipln_R( Y = data$Y, X = data$X, O = data$O, M = parameters$M, S = parameters$S, Pi = Pi ) - optim_new_M <- optim_zipln_M( + new_M <- optim_zipln_M( init_M = parameters$M, Y = data$Y, X = data$X, O = data$O, R = new_R, S = parameters$S, B = B, Omega = Omega, configuration = control - ) - new_M <- optim_new_M$M - optim_new_S <- optim_zipln_S( + )$M + new_S <- optim_zipln_S( init_S = parameters$S, O = data$O, M = new_M, R = new_R, B = B, diag_Omega = diag(Omega), configuration = control - ) - new_S <- optim_new_S$S + )$S # Check convergence new_parameters <- list(R = new_R, M = new_M, S = new_S) nb_iter <- nb_iter + 1 @@ -416,12 +408,12 @@ ZIPLNfit <- R6Class( ) R <- VE$R M <- VE$M - S <- VE$S + S2 <- VE$S^2 } else { - # otherwise set R to Pi, M to XB and S to sqrt(diag(Sigma)) + # otherwise set R to Pi, M to XB and S2 to diag(Sigma) R <- private$Pi[1:nrow(newdata), ] M <- X %*% private$B - S <- matrix(diag(private$Sigma), nrow = n_new, ncol = self$p, byrow = TRUE) + S2 <- matrix(diag(private$Sigma), nrow = n_new, ncol = self$p, byrow = TRUE) } ## mean latent positions in the parameter space (covariates/offset only) @@ -433,7 +425,7 @@ ZIPLNfit <- R6Class( results <- switch( type, link = EZ, - response = R + (1 - R) * exp(EZ + .5 * S^2), + response = R + (1 - R) * exp(EZ + .5 * S2), ) attr(results, "type") <- type diff --git a/src/optim_zi-pln.cpp b/src/optim_zi-pln.cpp index 24e371ab..5665ed47 100644 --- a/src/optim_zi-pln.cpp +++ b/src/optim_zi-pln.cpp @@ -82,9 +82,9 @@ arma::mat optim_zipln_B_dense( // [[Rcpp::export]] Rcpp::List optim_zipln_zipar_covar( + const arma::mat & R, // (n,p) const arma::mat & init_B0, // (d0,p) const arma::mat & X0, // covariates (n,d0) - const arma::mat & R, // (n,p) const Rcpp::List & configuration // List of config values ; xtol_abs is B0 only (double or mat) ) { const auto metadata = tuple_metadata(init_B0); From 86240ef989fd0b4e1217a9d63cb9b18745ffb9a0 Mon Sep 17 00:00:00 2001 From: Mahendra Mariadassou Date: Tue, 23 Jan 2024 10:24:36 +0100 Subject: [PATCH 29/36] [ci skip] move convergence test helpers to zipln-utils.R --- R/ZIPLNfit-class.R | 68 ---------------------------------------------- R/utils-zipln.R | 62 ++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 62 insertions(+), 68 deletions(-) diff --git a/R/ZIPLNfit-class.R b/R/ZIPLNfit-class.R index 128b8e89..54ffd2ad 100644 --- a/R/ZIPLNfit-class.R +++ b/R/ZIPLNfit-class.R @@ -768,71 +768,3 @@ ZIPLNfit_sparse <- R6Class( ## END OF THE CLASS ZIPLNfit_sparse ## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ) - - -## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -## UTILS ############################# -## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - -# Test convergence for a named list of parameters -# oldp, newp: named list of parameters -# xtol_rel: double ; negative or NULL = disabled -# xtol_abs: double ; negative or NULL = disabled -# Returns boolean -parameter_list_converged <- function(oldp, newp, xtol_abs = NULL, xtol_rel = NULL) { - # Strategy is to compare each pair of list elements with matching names. - stopifnot(is.list(oldp), is.list(newp)) - oldp <- oldp[order(names(oldp))] - newp <- newp[order(names(newp))] - stopifnot(all(names(oldp) == names(newp))) - - # Check convergence with xtol_rel if enabled - if(is.double(xtol_rel) && xtol_rel > 0) { - if(all(mapply(function(o, n) { all(abs(n - o) <= xtol_rel * abs(o)) }, oldp, newp))) { - return(TRUE) - } - } - - # Check convergence with xtol_abs (homogeneous) if enabled - if(is.double(xtol_abs) && xtol_abs > 0) { - if(all(mapply(function(o, n) { all(abs(n - o) <= xtol_abs) }, oldp, newp))) { - return(TRUE) - } - } - - # If no criteria has triggered, indicate no convergence - FALSE -} - -#' #' @importFrom glmnet glmnet -#' optim_zipln_B <- function(M, X, Omega, config) { -#' -#' if(config$lambda > 0) { -#' if (!is.null(config$ind_intercept)) { -#' m_bar <- colMeans(M) -#' x_bar <- colMeans(X[, -config$ind_intercept]) -#' X <- scale(X[, -config$ind_intercept], x_bar, FALSE) -#' M <- scale(M, m_bar, FALSE) -#' } -#' p <- ncol(M); d <- ncol(X) -#' if (d > 0) { -#' Omega12 <- chol(Omega) -#' y <- as.vector(M %*% t(Omega12)) -#' x <- kronecker(Omega12, X) -#' glmnet_out <- glmnet(x, y, lambda = config$lambda, intercept = FALSE, standardize = FALSE) -#' B <- matrix(as.numeric(glmnet_out$beta), nrow = d, ncol = p) -#' } else { -#' B <- matrix(0, nrow = d, ncol = p) -#' } -#' -#' if (!is.null(config$ind_intercept)) { -#' mu0 <- m_bar - as.vector(crossprod(B, x_bar)) -#' B <- rbind(mu0, B) -#' } -#' -#' } else { -#' B <- optim_zipln_B_dense(M, X) -#' } -#' B -#' } -#' diff --git a/R/utils-zipln.R b/R/utils-zipln.R index 5b01e7a1..1e2418d3 100644 --- a/R/utils-zipln.R +++ b/R/utils-zipln.R @@ -62,6 +62,68 @@ extract_model_zi <- function(call, envir) { list(Y = Y, X = X, X0 = X0, O = O, w = w, formula = call$formula, zicovar = terms$zicovar) } +# Test convergence for a named list of parameters +# oldp, newp: named list of parameters +# xtol_rel: double ; negative or NULL = disabled +# xtol_abs: double ; negative or NULL = disabled +# Returns boolean +parameter_list_converged <- function(oldp, newp, xtol_abs = NULL, xtol_rel = NULL) { + # Strategy is to compare each pair of list elements with matching names. + stopifnot(is.list(oldp), is.list(newp)) + oldp <- oldp[order(names(oldp))] + newp <- newp[order(names(newp))] + stopifnot(all(names(oldp) == names(newp))) + + # Check convergence with xtol_rel if enabled + if(is.double(xtol_rel) && xtol_rel > 0) { + if(all(mapply(function(o, n) { all(abs(n - o) <= xtol_rel * abs(o)) }, oldp, newp))) { + return(TRUE) + } + } + + # Check convergence with xtol_abs (homogeneous) if enabled + if(is.double(xtol_abs) && xtol_abs > 0) { + if(all(mapply(function(o, n) { all(abs(n - o) <= xtol_abs) }, oldp, newp))) { + return(TRUE) + } + } + + # If no criteria has triggered, indicate no convergence + FALSE +} + +#' #' @importFrom glmnet glmnet +#' optim_zipln_B <- function(M, X, Omega, config) { +#' +#' if(config$lambda > 0) { +#' if (!is.null(config$ind_intercept)) { +#' m_bar <- colMeans(M) +#' x_bar <- colMeans(X[, -config$ind_intercept]) +#' X <- scale(X[, -config$ind_intercept], x_bar, FALSE) +#' M <- scale(M, m_bar, FALSE) +#' } +#' p <- ncol(M); d <- ncol(X) +#' if (d > 0) { +#' Omega12 <- chol(Omega) +#' y <- as.vector(M %*% t(Omega12)) +#' x <- kronecker(Omega12, X) +#' glmnet_out <- glmnet(x, y, lambda = config$lambda, intercept = FALSE, standardize = FALSE) +#' B <- matrix(as.numeric(glmnet_out$beta), nrow = d, ncol = p) +#' } else { +#' B <- matrix(0, nrow = d, ncol = p) +#' } +#' +#' if (!is.null(config$ind_intercept)) { +#' mu0 <- m_bar - as.vector(crossprod(B, x_bar)) +#' B <- rbind(mu0, B) +#' } +#' +#' } else { +#' B <- optim_zipln_B_dense(M, X) +#' } +#' B +#' } +#' #' #' Helper function for ZIPLN initialization. #' #' From b94ce760880934e0a54f7bcb3b3505d1cc61dcd8 Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Tue, 23 Jan 2024 13:40:21 +0100 Subject: [PATCH 30/36] fixing bug in VE step of ZIPLN (was not saving approxiate parameters --- R/RcppExports.R | 4 ++-- R/ZIPLNfit-class.R | 3 +++ src/RcppExports.cpp | 8 ++++---- 3 files changed, 9 insertions(+), 6 deletions(-) diff --git a/R/RcppExports.R b/R/RcppExports.R index 6f186120..c2f80753 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -65,8 +65,8 @@ optim_zipln_B_dense <- function(M, X) { .Call('_PLNmodels_optim_zipln_B_dense', PACKAGE = 'PLNmodels', M, X) } -optim_zipln_zipar_covar <- function(init_B0, X0, R, configuration) { - .Call('_PLNmodels_optim_zipln_zipar_covar', PACKAGE = 'PLNmodels', init_B0, X0, R, configuration) +optim_zipln_zipar_covar <- function(R, init_B0, X0, configuration) { + .Call('_PLNmodels_optim_zipln_zipar_covar', PACKAGE = 'PLNmodels', R, init_B0, X0, configuration) } optim_zipln_R <- function(Y, X, O, M, S, Pi) { diff --git a/R/ZIPLNfit-class.R b/R/ZIPLNfit-class.R index 54ffd2ad..2d117699 100644 --- a/R/ZIPLNfit-class.R +++ b/R/ZIPLNfit-class.R @@ -341,6 +341,9 @@ ZIPLNfit <- R6Class( convergence <- convergence[1:nb_iter] break } + + parameters <- new_parameters + objective <- new_objective } list( diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp index c464d23f..62bb3121 100644 --- a/src/RcppExports.cpp +++ b/src/RcppExports.cpp @@ -237,16 +237,16 @@ BEGIN_RCPP END_RCPP } // optim_zipln_zipar_covar -Rcpp::List optim_zipln_zipar_covar(const arma::mat& init_B0, const arma::mat& X0, const arma::mat& R, const Rcpp::List& configuration); -RcppExport SEXP _PLNmodels_optim_zipln_zipar_covar(SEXP init_B0SEXP, SEXP X0SEXP, SEXP RSEXP, SEXP configurationSEXP) { +Rcpp::List optim_zipln_zipar_covar(const arma::mat& R, const arma::mat& init_B0, const arma::mat& X0, const Rcpp::List& configuration); +RcppExport SEXP _PLNmodels_optim_zipln_zipar_covar(SEXP RSEXP, SEXP init_B0SEXP, SEXP X0SEXP, SEXP configurationSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type R(RSEXP); Rcpp::traits::input_parameter< const arma::mat& >::type init_B0(init_B0SEXP); Rcpp::traits::input_parameter< const arma::mat& >::type X0(X0SEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type R(RSEXP); Rcpp::traits::input_parameter< const Rcpp::List& >::type configuration(configurationSEXP); - rcpp_result_gen = Rcpp::wrap(optim_zipln_zipar_covar(init_B0, X0, R, configuration)); + rcpp_result_gen = Rcpp::wrap(optim_zipln_zipar_covar(R, init_B0, X0, configuration)); return rcpp_result_gen; END_RCPP } From aa58da013092382c7aa738aaa7b89a2e2e38347c Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Tue, 23 Jan 2024 13:40:36 +0100 Subject: [PATCH 31/36] fixing name file in DESCRIPTION + doc generation --- DESCRIPTION | 2 +- man/ZIPLN.Rd | 17 ++++++++++++++--- man/ZIPLN_param.Rd | 8 ++++---- man/ZIPLNfit.Rd | 29 ++++++++++++++++++----------- man/ZIPLNfit_diagonal.Rd | 2 +- man/ZIPLNfit_spherical.Rd | 2 +- man/coef.ZIPLNfit.Rd | 6 +++--- man/fitted.ZIPLNfit.Rd | 2 +- man/predict.ZIPLNfit.Rd | 5 +---- man/sigma.ZIPLNfit.Rd | 7 +++++-- 10 files changed, 49 insertions(+), 31 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index f23718c1..41ebd98f 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -89,7 +89,7 @@ Collate: 'RcppExports.R' 'ZIPLNfit-class.R' 'ZIPLN.R' - 'ZIPLNfits-S3methods.R' + 'ZIPLNfit-S3methods.R' 'barents.R' 'import_utils.R' 'mollusk.R' diff --git a/man/ZIPLN.Rd b/man/ZIPLN.Rd index 8c0f58df..edfeb5f8 100644 --- a/man/ZIPLN.Rd +++ b/man/ZIPLN.Rd @@ -19,9 +19,13 @@ ZIPLN( \item{subset}{an optional vector specifying a subset of observations to be used in the fitting process.} -\item{zi}{a character describing the model for zero inflation in "single" (one global parameter, the default), "col" (one parameter per variable) -and "row" (one parameter per individuals). If covariates are specified in the formula RHS, after bar pipe like "~ PLN effect | ZI effect", \code{zi} -is ignored and the covariates extracted by model matrix will be used to account for the ZI effect.} +\item{zi}{a character describing the model used for zero inflation, either of +\itemize{ +\item "single" (default, one parameter shared by all counts) +\item "col" (one parameter per variable / feature) +\item "row" (one parameter per sample / individual). +If covariates are specified in the formula RHS (see details) this parameter is ignored. +}} \item{control}{a list-like structure for controlling the optimization, with default generated by \code{\link[=ZIPLN_param]{ZIPLN_param()}}. See the associated documentation for details.} @@ -32,13 +36,20 @@ an R6 object with class \code{\link{ZIPLNfit}} \description{ Fit the multivariate Zero Inflated Poisson lognormal model with a variational algorithm. Use the (g)lm syntax for model specification (covariates, offsets, subset). } +\details{ +Covariates for the Zero-Inflation parameter (using a logistic regression model) can be specified in the formula RHS using the pipe +(\verb{~ PLN effect | ZI effect}) to separate covariates for the PLN part of the model from those for the Zero-Inflation part. +Note that different covariates can be used for each part. +} \examples{ data(trichoptera) trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate) myPLN <- PLN(Abundance ~ 1, data = trichoptera) +## Use different models for zero-inflation... myZIPLN_single <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "single") myZIPLN_row <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "row") myZIPLN_col <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "col") +## ...including logistic regression on covariates myZIPLN_covar <- ZIPLN(Abundance ~ 1 | 1 + Wind, data = trichoptera) dplyr::bind_rows( myPLN$criteria, diff --git a/man/ZIPLN_param.Rd b/man/ZIPLN_param.Rd index 11aeb13b..798b7fad 100644 --- a/man/ZIPLN_param.Rd +++ b/man/ZIPLN_param.Rd @@ -24,7 +24,7 @@ ZIPLN_param( \item{Omega}{precision matrix of the latent variables. Inverse of Sigma. Must be specified if \code{covariance} is "fixed"} -\item{penalty}{a user defined penalty for sparsifying the residual covariance. Default is 0 (no sparsity).} +\item{penalty}{a user-defined penalty to sparsify the residual covariance. Defaults to 0 (no sparsity).} \item{config_post}{a list for controlling the post-treatments (optional bootstrap, jackknife, R2, etc.). See details} @@ -35,7 +35,7 @@ log-transformed data, and with the same formula as the one provided by the user. which sometimes speeds up the inference.} } \value{ -list of parameters configuring the fit. +list of parameters used during the fit and post-processing steps } \description{ Helper to define list of parameters to control the PLN fit. All arguments have defaults. @@ -44,7 +44,7 @@ Helper to define list of parameters to control the PLN fit. All arguments have d See \code{\link[=PLN_param]{PLN_param()}} for a full description of the generic optimization parameters. ZIPLN_param() also has two additional parameters controlling the optimization due the inner-outer loop structure of the optimizer: \itemize{ -\item "ftol_out" outer solver stops when an optimization step changes the objective function by less than xtol multiplied by the absolute value of the parameter. Default is 1e-8 -\item "maxit_out" outer solver stops when the number of iteration exceeds maxit_out. Default is 100 +\item "ftol_out" outer solver stops when an optimization step changes the objective function by less than \code{ftol_out} multiplied by the absolute value of the parameter. Default is 1e-8 +\item "maxit_out" outer solver stops when the number of iteration exceeds \code{maxit_out}. Default is 100 } } diff --git a/man/ZIPLNfit.Rd b/man/ZIPLNfit.Rd index 35691793..37542df4 100644 --- a/man/ZIPLNfit.Rd +++ b/man/ZIPLNfit.Rd @@ -4,13 +4,18 @@ \alias{ZIPLNfit} \title{An R6 Class to represent a ZIPLNfit} \description{ -The function \code{\link[=ZIPLN]{ZIPLN()}} fit a model which is an instance of a object with class \code{\link{ZIPLNfit}}. +The function \code{\link[=ZIPLN]{ZIPLN()}} fits a model which is an instance of an object with class \code{\link{ZIPLNfit}}. -This class comes with a set of R6 methods, some of them being useful for the user and exported as S3 methods. +This class comes with a set of R6 methods, some of which are useful for the end-user and exported as S3 methods. See the documentation for \code{\link[=coef]{coef()}}, \code{\link[=sigma]{sigma()}}, \code{\link[=predict]{predict()}}. Fields are accessed via active binding and cannot be changed by the user. } +\details{ +Covariates for the Zero-Inflation parameter (using a logistic regression model) can be specified in the formula RHS using the pipe +(\verb{~ PLN effect | ZI effect}) to separate covariates for the PLN part of the model from those for the Zero-Inflation part. +Note that different covariates can be used for each part. +} \examples{ \dontrun{ # See other examples in function ZIPLN @@ -31,9 +36,9 @@ print(myPLN) \item{\code{p}}{number of variables/species} -\item{\code{d}}{number of covariates in the PLN componente} +\item{\code{d}}{number of covariates in the PLN part} -\item{\code{d0}}{number of covariates in the ZI componente} +\item{\code{d0}}{number of covariates in the ZI part} \item{\code{nb_param}}{number of parameters in the current PLN model} @@ -113,7 +118,7 @@ Update a \code{\link{ZIPLNfit}} object \item{\code{B0}}{matrix of regression parameters in the zero inflated component} -\item{\code{Pi}}{Zero inflated probability parameter (either scalar, row-vector, col-vector of matrix)} +\item{\code{Pi}}{Zero inflated probability parameter (either scalar, row-vector, col-vector or matrix)} \item{\code{Omega}}{precision matrix of the latent variables} @@ -121,7 +126,7 @@ Update a \code{\link{ZIPLNfit}} object \item{\code{M}}{matrix of mean vectors for the variational approximation} -\item{\code{S}}{matrix of variance parameters for the variational approximation} +\item{\code{S}}{matrix of standard deviation parameters for the variational approximation} \item{\code{R}}{matrix of probabilities for the variational approximation} @@ -195,7 +200,7 @@ Call to the Cpp optimizer and update of the relevant fields \if{html}{\out{}} \if{latex}{\out{\hypertarget{method-ZIPLNfit-optimize_vestep}{}}} \subsection{Method \code{optimize_vestep()}}{ -Result of one call to the VE step of the optimization procedure: optimal variational parameters (M, S) and corresponding log likelihood values for fixed model parameters (Sigma, B). Intended to position new data in the latent space. +Result of one call to the VE step of the optimization procedure: optimal variational parameters (M, S, R) and corresponding log likelihood values for fixed model parameters (Sigma, B, B0). Intended to position new data in the latent space. \subsection{Usage}{ \if{html}{\out{
}}\preformatted{ZIPLNfit$optimize_vestep( covariates, @@ -234,8 +239,10 @@ Result of one call to the VE step of the optimization procedure: optimal variati A list with three components: \itemize{ \item the matrix \code{M} of variational means, -\item the matrix \code{S2} of variational variances -\item the vector \code{log.lik} of (variational) log-likelihood of each new observation +\item the matrix \code{S} of variational standard deviations +\item the matrix \code{R} of variational ZI probabilities +\item the vector \code{Ji} of (variational) log-likelihood of each new observation +\item a list \code{monitoring} with information about convergence status } } } @@ -257,9 +264,9 @@ Predict position, scores or observations of new data. \subsection{Arguments}{ \if{html}{\out{
}} \describe{ -\item{\code{newdata}}{A data frame in which to look for variables with which to predict. If omitted, the fitted values are used.} +\item{\code{newdata}}{A data frame in which to look for variables with which to predict. If omitted, the fitted values are returned.} -\item{\code{responses}}{Optional data frame containing the count of the observed variables (matching the names of the provided as data in the PLN function), assuming the interest in in testing the model.} +\item{\code{responses}}{Optional data frame containing the count of the observed variables (matching the names of the provided as data in the PLN function), assuming the interest is in testing the model.} \item{\code{type}}{Scale used for the prediction. Either \code{link} (default, predicted positions in the latent space) or \code{response} (predicted counts).} diff --git a/man/ZIPLNfit_diagonal.Rd b/man/ZIPLNfit_diagonal.Rd index 226bb7ca..acce0d88 100644 --- a/man/ZIPLNfit_diagonal.Rd +++ b/man/ZIPLNfit_diagonal.Rd @@ -53,7 +53,7 @@ print(myPLN) \if{html}{\out{}} \if{latex}{\out{\hypertarget{method-ZIPLNfit_diagonal-new}{}}} \subsection{Method \code{new()}}{ -Initialize a \code{\link{PLNfit}} model +Initialize a \code{\link{ZIPLNfit_diagonal}} model \subsection{Usage}{ \if{html}{\out{
}}\preformatted{ZIPLNfit_diagonal$new( responses, diff --git a/man/ZIPLNfit_spherical.Rd b/man/ZIPLNfit_spherical.Rd index b80571cb..df904d7b 100644 --- a/man/ZIPLNfit_spherical.Rd +++ b/man/ZIPLNfit_spherical.Rd @@ -53,7 +53,7 @@ print(myPLN) \if{html}{\out{}} \if{latex}{\out{\hypertarget{method-ZIPLNfit_spherical-new}{}}} \subsection{Method \code{new()}}{ -Initialize a \code{\link{PLNfit}} model +Initialize a \code{\link{ZIPLNfit_spherical}} model \subsection{Usage}{ \if{html}{\out{
}}\preformatted{ZIPLNfit_spherical$new( responses, diff --git a/man/coef.ZIPLNfit.Rd b/man/coef.ZIPLNfit.Rd index fec306ea..198134a2 100644 --- a/man/coef.ZIPLNfit.Rd +++ b/man/coef.ZIPLNfit.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/ZIPLNfits-S3methods.R +% Please edit documentation in R/ZIPLNfit-S3methods.R \name{coef.ZIPLNfit} \alias{coef.ZIPLNfit} \title{Extract model coefficients} @@ -9,8 +9,8 @@ \arguments{ \item{object}{an R6 object with class \code{\link{ZIPLNfit}}} -\item{type}{type of parameter that should be extracted. Either "count" (default) for \deqn{B}, -"zero" for \deqn{B0}, "precision" for \deqn{\Omega}, "covariance" for \deqn{\Sigma}} +\item{type}{type of parameter that should be extracted. Either "count" (default) for \eqn{B}, +"zero" for \eqn{B0}, "precision" for \eqn{\Omega}, "covariance" for \eqn{\Sigma}} \item{...}{additional parameters for S3 compatibility. Not used} } diff --git a/man/fitted.ZIPLNfit.Rd b/man/fitted.ZIPLNfit.Rd index 4ba1c11d..2379d89a 100644 --- a/man/fitted.ZIPLNfit.Rd +++ b/man/fitted.ZIPLNfit.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/ZIPLNfits-S3methods.R +% Please edit documentation in R/ZIPLNfit-S3methods.R \name{fitted.ZIPLNfit} \alias{fitted.ZIPLNfit} \title{Extracts model fitted values from objects returned by \code{\link[=ZIPLN]{ZIPLN()}} and its variants} diff --git a/man/predict.ZIPLNfit.Rd b/man/predict.ZIPLNfit.Rd index 39a02e03..a80f91c3 100644 --- a/man/predict.ZIPLNfit.Rd +++ b/man/predict.ZIPLNfit.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/ZIPLNfits-S3methods.R +% Please edit documentation in R/ZIPLNfit-S3methods.R \name{predict.ZIPLNfit} \alias{predict.ZIPLNfit} \title{Predict counts of a new sample} @@ -26,9 +26,6 @@ \item{...}{additional parameters for S3 compatibility. Not used} } -\value{ -A matrix of predicted log-counts (if \code{type = "link"}) or predicted counts (if \code{type = "response"}). -} \description{ Predict counts of a new sample } diff --git a/man/sigma.ZIPLNfit.Rd b/man/sigma.ZIPLNfit.Rd index 4e79c5b7..ebdcd833 100644 --- a/man/sigma.ZIPLNfit.Rd +++ b/man/sigma.ZIPLNfit.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/ZIPLNfits-S3methods.R +% Please edit documentation in R/ZIPLNfit-S3methods.R \name{sigma.ZIPLNfit} \alias{sigma.ZIPLNfit} \title{Extract variance-covariance of residuals 'Sigma'} @@ -15,5 +15,8 @@ A semi definite positive matrix of size p, assuming there are p species in the model. } \description{ -Extract the variance-covariance matrix of the residuals, usually noted \deqn{\Sigma} in ZIPLN models. +Extract the variance-covariance matrix of the residuals, usually noted \eqn{\Sigma} in ZIPLN models. +} +\seealso{ +\code{\link[=coef.ZIPLNfit]{coef.ZIPLNfit()}} } From 9e9017422a8a1e51d26f417acde7afb5b7ca4c4b Mon Sep 17 00:00:00 2001 From: Mahendra Mariadassou Date: Tue, 23 Jan 2024 14:03:56 +0100 Subject: [PATCH 32/36] [ci skip] Fix bug in optimizeVE step (parameters/objective not saved) during outer-loop --- R/ZIPLNfit-class.R | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/R/ZIPLNfit-class.R b/R/ZIPLNfit-class.R index 2d117699..a33d780b 100644 --- a/R/ZIPLNfit-class.R +++ b/R/ZIPLNfit-class.R @@ -273,7 +273,6 @@ ZIPLNfit <- R6Class( B0 = self$model_par$B0, Omega = self$model_par$Omega, control = ZIPLN_param(backend = "nlopt")$config_optim) { - n <- nrow(responses) data <- list(Y = responses, X = covariates$PLN, X0 = covariates$ZI, O = offsets) parameters <- @@ -334,8 +333,12 @@ ZIPLNfit <- R6Class( xtol_abs = control$xtol_abs, xtol_rel = control$xtol_rel ) + ## Update parameters + parameters <- new_parameters + objective <- new_objective + + ## End outer loop in case of convergence if (parameters_converged | objective_converged) { - parameters <- new_parameters stop_reason <- "converged" criterion <- criterion[1:nb_iter] convergence <- convergence[1:nb_iter] @@ -398,6 +401,7 @@ ZIPLNfit <- R6Class( O <- model.offset(model.frame(terms$PLN[-2], newdata)) if (is.null(O)) O <- matrix(0, n_new, self$p) + browser() ## Optimize M and S if responses are provided, if (level == 1) { VE <- self$optimize_vestep( From 7dac1cea98c715c74e125d2e9abab8989bb8fb31 Mon Sep 17 00:00:00 2001 From: Mahendra Mariadassou Date: Tue, 23 Jan 2024 14:07:33 +0100 Subject: [PATCH 33/36] [ci skip] Undo previous commit - bug already fixed by Julien - forgotten browser in optimizeVE_step --- R/ZIPLNfit-class.R | 3 --- 1 file changed, 3 deletions(-) diff --git a/R/ZIPLNfit-class.R b/R/ZIPLNfit-class.R index a33d780b..f6805869 100644 --- a/R/ZIPLNfit-class.R +++ b/R/ZIPLNfit-class.R @@ -345,8 +345,6 @@ ZIPLNfit <- R6Class( break } - parameters <- new_parameters - objective <- new_objective } list( @@ -401,7 +399,6 @@ ZIPLNfit <- R6Class( O <- model.offset(model.frame(terms$PLN[-2], newdata)) if (is.null(O)) O <- matrix(0, n_new, self$p) - browser() ## Optimize M and S if responses are provided, if (level == 1) { VE <- self$optimize_vestep( From 8603cd6e5f1f3cb8d978dbadcb4a49033708cdb9 Mon Sep 17 00:00:00 2001 From: Mahendra Mariadassou Date: Tue, 23 Jan 2024 14:57:39 +0100 Subject: [PATCH 34/36] [ci skip] Small changes to predict and fitted for ZIPLNfit - bug fix: no contribution of ZI part to average counts - add "deflated" response type to predict to get predicted counts without scaling from the ZI part. --- R/ZIPLNfit-S3methods.R | 13 ++++++++++++- R/ZIPLNfit-class.R | 21 +++++++++------------ 2 files changed, 21 insertions(+), 13 deletions(-) diff --git a/R/ZIPLNfit-S3methods.R b/R/ZIPLNfit-S3methods.R index 9fc2c0df..6fd24af8 100644 --- a/R/ZIPLNfit-S3methods.R +++ b/R/ZIPLNfit-S3methods.R @@ -11,10 +11,21 @@ isZIPLNfit <- function(Robject) {inherits(Robject, "ZIPLNfit")} #' #' @name predict.ZIPLNfit #' @inheritParams predict.PLNfit +#' @param type Scale used for the prediction. Either `"link"` (default, predicted positions in the latent space), `"response"` (predicted average counts, accounting for zero-inflation) or `"deflated"` (predicted average counts, not accounting for zero-inflation and using only the PLN part of the model). +#' +#' @details +#' Note that `level = 1` can only be used if responses are provided, +#' as the variational parameters can't be estimated otherwise. In the absence of responses, `level` is ignored and the fitted values are returned +#' +#' Note also that when `type = "response"` corresponds to predicting +#' values with \eqn{(1 - \pi)A}, where \eqn{A} is the average count in +#' the PLN part of the model and \eqn{\pi} the probability of zero-inflation, +#' whereas `type = "deflated"` corresponds to \eqn{A}. + #' #' @param object an R6 object with class [`ZIPLNfit`] #' @export -predict.ZIPLNfit <- function(object, newdata, responses = NULL, level = 1, type = c("link", "response"), ...) { +predict.ZIPLNfit <- function(object, newdata, responses = NULL, level = 1, type = c("link", "response", "deflated"), ...) { stopifnot(isZIPLNfit(object)) object$predict(newdata = newdata, type = type, envir = parent.frame(), level = level, responses = responses) } diff --git a/R/ZIPLNfit-class.R b/R/ZIPLNfit-class.R index f6805869..3ab9e286 100644 --- a/R/ZIPLNfit-class.R +++ b/R/ZIPLNfit-class.R @@ -360,18 +360,15 @@ ZIPLNfit <- R6Class( ) }, - #' @description Predict position, scores or observations of new data. - #' @param newdata A data frame in which to look for variables with which to predict. If omitted, the fitted values are returned. - #' @param responses Optional data frame containing the count of the observed variables (matching the names of the provided as data in the PLN function), assuming the interest is in testing the model. - #' @param type Scale used for the prediction. Either `link` (default, predicted positions in the latent space) or `response` (predicted counts). + #' @description Predict position, scores or observations of new data. See [predict.ZIPLNfit()] for the S3 method and additional details + #' @param newdata A data frame in which to look for variables with which to predict. If omitted, the fitted values are used. + #' @param responses Optional data frame containing the count of the observed variables (matching the names of the provided as data in the PLN function), assuming the interest in in testing the model. + #' @param type Scale used for the prediction. Either `"link"` (default, predicted positions in the latent space), `"response"` (predicted average counts, accounting for zero-inflation) or `"deflated"` (predicted average counts, not accounting for zero-inflation and using only the PLN part of the model). #' @param level Optional integer value the level to be used in obtaining the predictions. Level zero corresponds to the population predictions (default if `responses` is not provided) while level one (default) corresponds to predictions after evaluating the variational parameters for the new data. #' @param envir Environment in which the prediction is evaluated #' - #' @details - #' Note that `level = 1` can only be used if responses are provided, - #' as the variational parameters can't be estimated otherwise. In the absence of responses, `level` is ignored and the fitted values are returned #' @return A matrix with predictions scores or counts. - predict = function(newdata, responses = NULL, type = c("link", "response"), level = 1, envir = parent.frame()) { + predict = function(newdata, responses = NULL, type = c("link", "response", "deflated"), level = 1, envir = parent.frame()) { ## Ignore everything if newdata is not provided if (missing(newdata)) { @@ -428,9 +425,9 @@ ZIPLNfit <- R6Class( type <- match.arg(type) results <- switch( type, - link = EZ, - response = R + (1 - R) * exp(EZ + .5 * S2), - + link = EZ, + response = (1 - R) * exp(EZ + .5 * S2), + deflated = exp(EZ + .5 * S2), ) attr(results, "type") <- type results @@ -520,7 +517,7 @@ ZIPLNfit <- R6Class( #' @field latent_pos a matrix: values of the latent position vector (Z) without covariates effects or offset latent_pos = function() {private$M - private$X %*% private$B}, #' @field fitted a matrix: fitted values of the observations (A in the model) - fitted = function() {private$R * private$zeros + (1 - private$R) * private$A}, + fitted = function() {(1 - private$R) * private$A}, #' @field vcov_model character: the model used for the covariance (either "spherical", "diagonal", "full" or "sparse") vcov_model = function() {private$covariance}, #' @field zi_model character: the model used for the zero inflation (either "single", "row", "col" or "covar") From 474da5ace3b6f6ad2f5f90e2d11b64de0ba0de4b Mon Sep 17 00:00:00 2001 From: Mahendra Mariadassou Date: Tue, 23 Jan 2024 15:22:36 +0100 Subject: [PATCH 35/36] [ci skip] test new parameter deflated in `prediction.ZIPLNfit()` --- tests/testthat/test-ziplnfit.R | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tests/testthat/test-ziplnfit.R b/tests/testthat/test-ziplnfit.R index 5f91cc6e..9ba0ffc5 100644 --- a/tests/testthat/test-ziplnfit.R +++ b/tests/testthat/test-ziplnfit.R @@ -54,10 +54,9 @@ test_that("PLN fit: Check prediction", { pred1 <- predict(model1, newdata = newdata, type = "response") pred1_off <- predict(model1_off, newdata = newdata, type = "response") pred2 <- predict(model2, newdata = newdata, type = "response") + pred2_def <- predict(model2, newdata = newdata, type = "deflated") pred2_ve <- predict(model2, newdata = newdata, type = "response", responses = newdata$Abundance) - pred3_ve <- predict(model3, newdata = newdata, type = "response", - responses = newdata$Abundance) ## predict returns fitted values if no data is provided expect_equal(model2$predict(), model2$fitted) @@ -74,6 +73,9 @@ test_that("PLN fit: Check prediction", { mean((newdata$Abundance - pred2_ve)^2) ) + ## Removing zero-inflation leads to higher predicted values + expect_gt(min(pred2_ve, pred2_def), 0) + ## R6 methods ## without offsets, predictions should be the same for all samples expect_equal(unname(apply(pred1, 2, sd)), rep(0, ncol(pred1))) From ceb39af90757e845d2766648bcfcf4b1094e194c Mon Sep 17 00:00:00 2001 From: Mahendra Mariadassou Date: Tue, 23 Jan 2024 15:23:12 +0100 Subject: [PATCH 36/36] Update documentation, bump version number. --- DESCRIPTION | 2 +- NEWS.md | 4 ++++ man/ZIPLNfit.Rd | 15 +++++---------- man/predict.ZIPLNfit.Rd | 13 +++++++++++-- 4 files changed, 21 insertions(+), 13 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 41ebd98f..bfa54fe0 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: PLNmodels Title: Poisson Lognormal Models -Version: 1.1.0-9000 +Version: 1.2.0 Authors@R: c( person("Julien", "Chiquet", role = c("aut", "cre"), email = "julien.chiquet@inrae.fr", comment = c(ORCID = "0000-0002-3629-3429")), diff --git a/NEWS.md b/NEWS.md index e4f38366..01adb95d 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,7 @@ +# Current (2024-01-23) + +* Addition of ZIPLN() and ZIPLNfit-class to allow for zero-inflation in the (for now) standard PLN model (merge PR #116) + # PLNmodels 1.1.0 (2024-01-08) * Update documentation of PLN*_param() functions to include torch optimization parameters diff --git a/man/ZIPLNfit.Rd b/man/ZIPLNfit.Rd index 37542df4..1f3c43b3 100644 --- a/man/ZIPLNfit.Rd +++ b/man/ZIPLNfit.Rd @@ -250,12 +250,12 @@ A list with three components: \if{html}{\out{}} \if{latex}{\out{\hypertarget{method-ZIPLNfit-predict}{}}} \subsection{Method \code{predict()}}{ -Predict position, scores or observations of new data. +Predict position, scores or observations of new data. See \code{\link[=predict.ZIPLNfit]{predict.ZIPLNfit()}} for the S3 method and additional details \subsection{Usage}{ \if{html}{\out{
}}\preformatted{ZIPLNfit$predict( newdata, responses = NULL, - type = c("link", "response"), + type = c("link", "response", "deflated"), level = 1, envir = parent.frame() )}\if{html}{\out{
}} @@ -264,11 +264,11 @@ Predict position, scores or observations of new data. \subsection{Arguments}{ \if{html}{\out{
}} \describe{ -\item{\code{newdata}}{A data frame in which to look for variables with which to predict. If omitted, the fitted values are returned.} +\item{\code{newdata}}{A data frame in which to look for variables with which to predict. If omitted, the fitted values are used.} -\item{\code{responses}}{Optional data frame containing the count of the observed variables (matching the names of the provided as data in the PLN function), assuming the interest is in testing the model.} +\item{\code{responses}}{Optional data frame containing the count of the observed variables (matching the names of the provided as data in the PLN function), assuming the interest in in testing the model.} -\item{\code{type}}{Scale used for the prediction. Either \code{link} (default, predicted positions in the latent space) or \code{response} (predicted counts).} +\item{\code{type}}{Scale used for the prediction. Either \code{"link"} (default, predicted positions in the latent space), \code{"response"} (predicted average counts, accounting for zero-inflation) or \code{"deflated"} (predicted average counts, not accounting for zero-inflation and using only the PLN part of the model).} \item{\code{level}}{Optional integer value the level to be used in obtaining the predictions. Level zero corresponds to the population predictions (default if \code{responses} is not provided) while level one (default) corresponds to predictions after evaluating the variational parameters for the new data.} @@ -276,11 +276,6 @@ Predict position, scores or observations of new data. } \if{html}{\out{
}} } -\subsection{Details}{ -Note that \code{level = 1} can only be used if responses are provided, -as the variational parameters can't be estimated otherwise. In the absence of responses, \code{level} is ignored and the fitted values are returned -} - \subsection{Returns}{ A matrix with predictions scores or counts. } diff --git a/man/predict.ZIPLNfit.Rd b/man/predict.ZIPLNfit.Rd index a80f91c3..8052cbd1 100644 --- a/man/predict.ZIPLNfit.Rd +++ b/man/predict.ZIPLNfit.Rd @@ -9,7 +9,7 @@ newdata, responses = NULL, level = 1, - type = c("link", "response"), + type = c("link", "response", "deflated"), ... ) } @@ -22,10 +22,19 @@ \item{level}{Optional integer value the level to be used in obtaining the predictions. Level zero corresponds to the population predictions (default if \code{responses} is not provided) while level one (default) corresponds to predictions after evaluating the variational parameters for the new data.} -\item{type}{The type of prediction required. The default is on the scale of the linear predictors (i.e. log average count)} +\item{type}{Scale used for the prediction. Either \code{"link"} (default, predicted positions in the latent space), \code{"response"} (predicted average counts, accounting for zero-inflation) or \code{"deflated"} (predicted average counts, not accounting for zero-inflation and using only the PLN part of the model).} \item{...}{additional parameters for S3 compatibility. Not used} } \description{ Predict counts of a new sample } +\details{ +Note that \code{level = 1} can only be used if responses are provided, +as the variational parameters can't be estimated otherwise. In the absence of responses, \code{level} is ignored and the fitted values are returned + +Note also that when \code{type = "response"} corresponds to predicting +values with \eqn{(1 - \pi)A}, where \eqn{A} is the average count in +the PLN part of the model and \eqn{\pi} the probability of zero-inflation, +whereas \code{type = "deflated"} corresponds to \eqn{A}. +}

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