Full model 2: burn-in and thinning

Load packages, read data and source custom scripts

rm(list = ls())
library(bamlss)

#> Loading required package: coda

#> Loading required package: colorspace

#> Loading required package: mgcv

#> Loading required package: nlme

#> This is mgcv 1.8-31. For overview type 'help("mgcv-package")'.

#> 
#> Attaching package: 'bamlss'

#> The following object is masked from 'package:mgcv':
#> 
#>     smooth.construct

library(gamlss.dist)

#> Loading required package: MASS

path_proj <- day2day::git_path()
path_data <- file.path(path_proj, "data")
path_processed <- file.path(path_data, "processed")
path_modelled <- file.path(path_data, "modelled")

bwdata_file <- file.path(path_processed, "bwdata_41_model.fst")
path_modelled_data <- file.path(path_modelled, "pre-11-full-re.rds")
path_modelled_burned <- gsub("(\\.rds)$", "-burned\\1", path_modelled_data)
path_form <- gsub("(\\.rds)$", "-form\\1", path_modelled_data)

bwdata_model <- fst::read_fst(bwdata_file)
model <- readRDS(path_modelled_data)
form <- readRDS(path_form)

Burn-in and thinning

model$samples <- window(model$samples, start = 3501, thin = 20)

Compute results

system.time(model$results <- results.bamlss.default(model))

#>    user  system elapsed 
#> 221.861   1.667 223.533

system.time(saveRDS(model, file = path_modelled_burned))

#>    user  system elapsed 
#>   5.386   0.008   5.432

Maximum auto-correlation function (ACF)

par(mar = c(4, 4, 0.5, 0), mfrow = c(1, 2))
plot(model, model = "pi", which = "max-acf", spar = FALSE)

$Maximum ACF of samples for $\pi$$

Figure 1: Maximum ACF of samples for $\pi$

MCMC convergence

par(mar = c(4, 4, 3, 1), mfrow = c(1, 2))
plot(model, which = "samples")

Time to execute the task

Only useful when executed with Rscript.

proc.time()

#>    user  system elapsed 
#> 285.319   2.100 287.652