docs updating documentation

R/02_modeler.R

@@ -29,12 +29,12 @@
 #' \describe{
 #'   \item{\code{param}}{A data frame containing the optimized parameters and related information.}
 #'   \item{\code{dt}}{A data frame with data used and fitted values.}
-#'   \item{\code{fn}}{The call used to calculate the AUC.}
+#'   \item{\code{fn}}{The call used when fitting models.}
 #'   \item{\code{metrics}}{Metrics and summary of the models.}
 #'   \item{\code{execution}}{Execution time.}
 #'   \item{\code{response}}{Response variable.}
 #'   \item{\code{keep}}{Metadata to keep across.}
-#'   \item{\code{fun}}{Function being optimized}
+#'   \item{\code{fun}}{Name of the function.}
 #'   \item{\code{fit}}{List with the fitted models.}
 #' }
 #' @export

R/04_predict.R

@@ -5,6 +5,8 @@
 #' @param object An object inheriting from class \code{modeler} resulting of
 #' executing the function \code{modeler()}
 #' @param x A numeric time point to make the prediction. Can be more than one.
+#' If type = "auc", x needs to be of size 2, which specifies the interval for
+#' calculating the AUC.
 #' @param id A unique identifier to filter by. \code{NULL} by default.
 #' @param type A character string specifying the type of prediction. If "point"
 #' it will predict the value of y for a given x. If "auc" it will return the
@@ -16,12 +18,11 @@
 #' @param ... Further parameters. For future improvements.
 #' @author Johan Aparicio [aut]
 #' @method predict modeler
-#' @return A data.frame object with predicted values.
+#' @return A data.frame object with predicted values and standard errors.
 #' @export
 #' @examples
 #' library(flexFitR)
 #' data(dt_potato)
-#' explorer <- explorer(dt_potato, x = DAP, y = c(Canopy, GLI_2), id = Plot)
 #' mod_1 <- dt_potato |>
 #'   modeler(
 #'     x = DAP,

R/07_confint.R

@@ -44,7 +44,7 @@ confint.modeler <- function(x, parm = NULL, level = 0.95, id = NULL, ...) {
   } else {
     uid <- unique(dt$uid)
   }
-  ci_table <- coef.modeler(x, df = TRUE, id = id) |>
+  ci_table <- coef.modeler(x, df = TRUE, id = uid) |>
     mutate(
       t_value = qt(1 - (1 - level) / 2, df = rdf),
       ci_lower = solution - t_value * std.error,