fcal-lcmm.qmd

#### LCMM

```{R LCMM code}
#| eval: false
#| echo: true
#| code-fold: false

## Modelling ##
library(lcmm)
library(splines)

args <- commandArgs(trailingOnly = TRUE)
fcal <- readRDS("fcal.RDS")
fcal$calpro_result <- log(fcal$calpro_result)
if(!dir.exists("cache")) dir.create("cache")

rep <- 50
# set the maximum number of iterations
maxiter <- 10

if(file.exists("m1.RDS")) {
  m1 <- readRDS("m1.RDS")
  ng <- as.numeric(args[1])
  # create a cluster
  cl <- parallel::makeForkCluster(parallel::detectCores())
  # export the number of groups to the cluster
  parallel::clusterExport(cl, "ng")
  # run gridsearch
  hlme.fit <- gridsearch(
    rep = rep,
    maxiter = maxiter,
    minit = m1,
    cl = cl,
    hlme(calpro_result ~ ns(calpro_time,
                            df = 4,
                            Boundary.knots = c(0, 7)),
         mixture = ~ ns(calpro_time,
                        df = 4,
                        Boundary.knots = c(0, 7)),
         random = ~  1,
         subject = "ids",
	 classmb = ~ 1 + diagnosis,
         ng = ng,
         data = fcal,
         maxiter = 24000,
         verbose = TRUE,
         partialH = FALSE)
  )
  # stop the cluster
  parallel::stopCluster(cl)

  if (hlme.fit$conv == 1) {
    message("Convergence achieved for ", ng, " subgroups ✅ \n")
  } else {
    stop("Convergence NOT achieved for ", ng, " subgroups ⚠️ \n")
  }
  saveRDS(hlme.fit, paste0("cache/fcal-", ng, ".RDS"))
} else {
  stop("m1 not found!")
}
```