Run model on cluster

library(dplyr); library(brms)
setwd("/usr/users/rarslan/updated_data/")

args = commandArgs()
dataset = args[6]
uptobyear = args[7]

load(paste0(dataset, ".rdata"))

model_data = get(paste0(dataset, ".1")) %>% tbl_df %>% 
    filter(byear < uptobyear)

if (dataset == "rpqa") {
    model_data = model_data %>%     
        select(urban, codeLieuNaiss, 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 * urban + birth_cohort + male + maternalage.factor + paternalage.mean + paternal_loss + maternal_loss + older_siblings + nr.siblings + last_born + (1 | idParents) + (1 | codeLieuNaiss)
} else if(dataset == "ddb") {
    model_data = model_data %>%     
        select(region, parish_code, 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 * region + birth_cohort + male + maternalage.factor + paternalage.mean + paternal_loss + maternal_loss + older_siblings + nr.siblings + last_born + (1 | idParents) + (1 | parish_code)
} else if(dataset == "krmh") {
    model_data = model_data %>%     
        select(gebortk, 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) + (1 | gebortk)
}

model_prior = c(set_prior("normal(0,5)", class = "b"),
                                set_prior("normal(0,5)", class = "b", nlpar = "hu"),
                                set_prior("student_t(3, 0, 5)", class = "sd"))
model_formula_hu = update(model_formula,  hu ~ . )
model_formula = bf(model_formula, model_formula_hu)
model_family = hurdle_poisson()

model = brm(    model_formula,
                         prior = model_prior, 
                         family = model_family, data = model_data, 
                         chains = 6, iter = 800, warmup = 300, cores = 6, ranef = FALSE)

summary(model)

saveRDS(model,file = paste0("coefs/", dataset, "/r15_region_moderator_parish_ranef.rds"))