this script runs a model on our scientific computing cluster
library(dplyr); library(brms)
setwd("/usr/users/rarslan/updated_data/")
args = commandArgs()
dataset = args[6]
uptobyear = args[7]
if (dataset == "swed") {
load("swed1.rdata")
} else {
load(paste0(dataset, ".rdata"))
}
model_data = get(paste0(dataset, ".1")) %>% tbl_df %>%
filter(byear < uptobyear) %>%
select(children, birth_cohort, male, maternalage.factor, paternalage.mean, paternalage, paternal_loss, maternal_loss, older_siblings, nr.siblings, last_born, idParents) %>%
na.omit()
model_formula = children ~ paternalage + birth_cohort + male + maternalage.factor + paternalage.mean + paternal_loss + maternal_loss + older_siblings + nr.siblings + last_born + (1 | idParents)
if (dataset == "swed_subset_children") {
model_family = poisson()
} else {
model_formula_hu = update(model_formula, hu ~ . )
model_formula = bf(model_formula, model_formula_hu)
model_family = hurdle_poisson()
}
model = brm( model_formula,
family = model_family, data = model_data,
chains = 6, iter = 800, warmup = 300, cores = 6, ranef = FALSE)
summary(model)
saveRDS(model,file = paste0("coefs/", dataset, "/r24_uniform_priors.rds"))