Linear model: fitting
Birthweight model including covariates at municipality level with linear effects.
Load packages, read data and source custom scripts
Paths are defined relative to the git repository location.
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.constructlibrary(gamlss.dist)#> Loading required package: MASSpath_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")
path_modelled_data <- file.path(path_modelled, "bw-muni-04-covs-lm.rds")
path_modelled_sink <- gsub("\\.rds$", "\\.txt", path_modelled_data)
path_modelled_form <- gsub("(\\.rds)$", "-form\\1", path_modelled_data)
bwdata_model <- fst::read_fst(file.path(path_processed, "bwdata_41_model.fst"))Define formula for our model
Now we define the same models as in the previous study.
form_sigma <- sigma ~ 1
form_mu <- born_weight ~ remoteness + prop_tap_toilet
form <- list(form_mu, form_sigma)Run the model of interest and save results
{
sink(path_modelled_sink)
bamlss_model <- bamlss(
form, data = bwdata_model,
n.iter = 1000, burnin = 0, cores = 4, combine = FALSE, light = TRUE
)
sink()
}
readLines(path_modelled_sink)#> [1] "AICc 4458381. logPost -2229223 logLik -2229186 edf 4.0000 eps 0.0299 iteration 1"
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#> [96] "|********************| 100% 0.00sec 4.78min"system.time(saveRDS(bamlss_model, file = path_modelled_data))#> user system elapsed
#> 0.21 0.00 0.21saveRDS(form, file = path_modelled_form)Time to execute the task
Only useful when executed with Rscript.
proc.time()#> user system elapsed
#> 1183.748 1.649 303.356