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fit_inventory_COMP_reparametrisation.r
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fit_inventory_COMP_reparametrisation.r
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library(COMPoissonReg)
library(cmdstanr)
library(rstan)
source("aux.r")
####
epsilon <- 1E-16 # .Machine$double.eps
MaxIter <- 1E4
Ncutoff <- 100
inventory <- read.csv("data/Shmuelli_2005.csv")
nobs <- sum(inventory$frequency)
cY <- list(
k = inventory$count,
n = inventory$frequency,
K = nrow(inventory)
)
stan.data <- list(
K = cY$K,
N = nobs,
n = cY$n,
y = cY$k,
s_mu = .01,
r_mu = .01,
s_nu = 0.0625,
r_nu = 0.25,
eps = epsilon,
M = MaxIter,
batchSize = 10,
N_cap = Ncutoff
)
iterations <- 5000
############
## EB
fpath.eb <- "stan/MCMC_COMP_2_ErrorBounding.stan"
EB_impl <- cmdstanr::cmdstan_model(fpath.eb,
include_paths = "./stan/")
opt_EB <- EB_impl$optimize(data = stan.data)
opt_EB$mle()
EB.raw <-
EB_impl$sample(data = stan.data,
refresh = floor(iterations/5),
chains = 4, parallel_chains = 4,
iter_warmup = iterations, iter_sampling = iterations,
adapt_delta = .90, max_treedepth = 12,
show_messages = FALSE)
## Threshold
fpath.thresh <- "stan/MCMC_COMP_2_Threshold.stan"
threshold_impl <- cmdstanr::cmdstan_model(fpath.thresh, include_paths = "./stan/")
opt_threshold <- threshold_impl$optimize(data = stan.data)
opt_threshold$mle()
threshold.raw <-
threshold_impl$sample(data = stan.data,
refresh = floor(iterations/5),
chains = 4, parallel_chains = 4,
iter_warmup = iterations, iter_sampling = iterations,
adapt_delta = .90, max_treedepth = 12,
show_messages = FALSE)
## Fixed Cap
fpath.fixed <- "stan/MCMC_COMP_2_fixedCap.stan"
fixed_impl <- cmdstanr::cmdstan_model(fpath.fixed, include_paths = "./stan/")
opt_fixed <- fixed_impl$optimize(data = stan.data)
opt_fixed$mle()
fixed.raw <-
fixed_impl$sample(data = stan.data,
refresh = floor(iterations/5),
chains = 4, parallel_chains = 4,
iter_warmup = iterations, iter_sampling = iterations,
adapt_delta = .90, max_treedepth = 12,
show_messages = FALSE)
## Batches
fpath.batch <- "stan/MCMC_COMP_2_Batches.stan"
batches_impl <- cmdstanr::cmdstan_model(fpath.batch, include_paths = "./stan/")
opt_batches <- batches_impl$optimize(data = stan.data)
opt_batches$mle()
batches.raw <-
batches_impl$sample(data = stan.data,
refresh = floor(iterations/5),
chains = 4, parallel_chains = 4,
iter_warmup = iterations, iter_sampling = iterations,
adapt_delta = .90, max_treedepth = 12,
show_messages = FALSE)
####
all.raws <- list(
EB = EB.raw,
ST = threshold.raw,
BA = batches.raw,
FC = fixed.raw
)
all.mcmcs <- lapply(all.raws, stanfit)
lapply(all.mcmcs, function(x){
print(x, pars = c("mu", "nu", "n_iter"),
digits_summary = 3)
})
lapply(all.mcmcs, check_hmc_diagnostics)
lapply(all.raws, function(x) x$time())
efficiencies <- lapply(seq_along(all.raws), function(j){
ESS <- monitor(all.mcmcs[[j]], print = FALSE)$n_eff[c(1, 2)]
ttime <- all.raws[[j]]$time()$total
return(ESS/ttime)
})
names(efficiencies) <- names(all.mcmcs)
efficiencies
####
nits.df <- reshape2::melt(
lapply(all.mcmcs,
function(x) extract(x, 'n_iter')$n_iter)
)
library(ggplot2)
ggplot(nits.df,
aes(y = value, colour = L1, fill = L1)) +
geom_boxplot(alpha = 0.4) +
scale_y_continuous("Iterations to approximate logZ") +
theme_bw(base_size = 16)