This vignette provides the code used to generate the virtual populations for the ten subpopulations of interest, plus a non-obese adult subpopulation.
To use the code in this vignette, you’ll first need to load a few packages.
Because we’ll be writing some files (and then reading them in other vignettes), be aware of where your current working directory is – the files will be written there. # Set up subpopulation specs Here, we set the number of individuals in each virtual population (1000). We also specify a list of names for the virtual populations. Then we specify corresponding lists of gender, age limits, and BMI categories. HTTK-Pop default values will be used for other population specifications (e.g. race/ethnicity, kidney function categories).
nsamp<-1000
#List subpop names
ExpoCast.group<-list("Total",
"Age.6.11",
"Age.12.19",
"Age.20.65",
"Age.GT65",
"BMIgt30",
"BMIle30",
"Females",
"Males",
"ReproAgeFemale",
"Age.20.50.nonobese")
#List subpop gender specifications
gendernum <- c(rep(list(NULL),7),
list(list(Male=0, Female=1000)),
list(list(Male=1000, Female=0)),
list(list(Male=0, Female=1000)),
list(NULL))
#List subpop age limits in years
agelim<-c(list(c(0,79),
c(6,11),
c(12,19),
c(20,65),
c(66,79)),
rep(list(c(0,79)),4),
list(c(16,49)),
list(c(20,50)))
#List subpop weight categories
bmi_category <- c(rep(list(c('Underweight',
'Normal',
'Overweight',
'Obese')),
5),
list('Obese', c('Underweight','Normal', 'Overweight')),
rep(list(c('Underweight',
'Normal',
'Overweight',
'Obese')),
3),
list(c('Underweight', 'Normal', 'Overweight')))First, define the loop body as a function; then use parallel::clusterMap to parallelize it. Warning: This might take a couple of minutes to run.
tmpfun <- function(gendernum, agelim, bmi_category, ExpoCast_grp,
nsamp, method){
result <- tryCatch({
pops <- httk::httkpop_generate(
method=method,
nsamp = nsamp,
gendernum = gendernum,
agelim_years = agelim,
weight_category = bmi_category)
filepart <- switch(method,
'virtual individuals' = 'vi',
'direct resampling' = 'dr')
saveRDS(object=pops,
file=paste0(paste('data/httkpop',
filepart, ExpoCast_grp,
sep='_'),
'.Rdata'))
return(getwd())
}, error = function(err){
print(paste('Error occurred:', err))
return(1)
})
}
cluster <- parallel::makeCluster(2, # Reduced from 40 to 2 cores
outfile='subpopulations_parallel_out.txt')
evalout <- parallel::clusterEvalQ(cl=cluster,
{library(data.table)
library(httk)})
parallel::clusterExport(cl = cluster,
varlist = 'tmpfun')
#Set seeds on all workers for reproducibility
parallel::clusterSetRNGStream(cluster,
TeachingDemos::char2seed("Caroline Ring"))
out_vi <- parallel::clusterMap(cl=cluster,
fun = tmpfun,
gendernum=gendernum,
agelim=agelim,
bmi_category=bmi_category,
ExpoCast_grp = ExpoCast.group,
MoreArgs = list(nsamp = nsamp,
method = 'virtual individuals'))
out_dr <- parallel::clusterMap(cl=cluster,
fun = tmpfun,
gendernum=gendernum,
agelim=agelim,
bmi_category=bmi_category,
ExpoCast_grp = ExpoCast.group,
MoreArgs = list(nsamp = nsamp,
method = 'direct resampling'))
parallel::stopCluster(cluster)