multlcmm "object 'c_loglikmultlcmm' not found"'

[chrisraynerr]

Hi Viviane,

When I try to use multiple cores (nproc) to run multlcmm, I get the error:

Error in {: task1 failed - "object 'c_loglikmultlcmm' not found"'

I looked for documentation on c_loglikmultlcmm, but can't find anything. Do you have a solution?

Many thanks, Chris

[SPINFALLING]

I had the same problem when i run multiple cores, and i thinks it may related the List delivery problem, you can't deliver list objects like object[[1]]

[VivianePhilipps]

Hi,

do you get the error within a gridsearch, or with a single multlcmm model? The c_loglikmultlcmm function is the Fortran code that is called in the multlcmm function. I've never had such troubles in the parallel mode. In my experience, I only get such an error when the function in sourced (so used outside the package).

Viviane

[SPINFALLING]

Hi Viviane, I am running a multi-trajectory model using the mpjlcmm function with three indices: X1, X2, and X3, concerning their grouping from trajectory latent class 1 to 5. I have more than 50 thousands records, so it really takes me a lot of time. …

man_cubic_X1_3 <- hlme(X1~age60+I(age60^2)+I(age60^3)+hypoglycemia,
                   random=~age60,
                   subject="ParticipantID",
                   data=JinLin_man,ng=3,
                   mixture=~age60+I(age60^2)+I(age60^3),
                   B=random(man_cubic_FBG1),maxiter=0)

…

man_cubic_Three_3 <- mpjlcmm(longitudinal=list(man_cubic_X1_3,man_cubic_X2_3,man_cubic_X3_3),
                         subject="ParticipantID",
                         ng=3,
                         data=JinLin_man,
                         maxiter=199,
                         B=man_cubic_Three_1

) …

Here is my code, I’m trying to using multiple nodes to speed up the calculation

fit_trajectory_models <- function(data, ng, initial_models) {
models <- list()
if (ng == 1) {
models$X1 <- hlme(X1~ age60 + I(age60^2) + I(age60^3) + hypoglycemia,
                        random = ~age60,
                        subject = "ParticipantID",
                        data = data,
                        maxiter = 100)

     models$X2 <- hlme(X2~ age60 + I(age60^2) + I(age60^3),
                         random = ~age60,
                         subject = "ParticipantID",
                         data = data,
                         maxiter = 100)

     models$X3 <- hlme(X3 ~ age60 + I(age60^2) + I(age60^3) + hypotensor,
                        random = ~age60,
                        subject = "ParticipantID",
                        data = data,
                        maxiter = 100)
   } else {
     models$X1 <- hlme(X1 ~ age60 + I(age60^2) + I(age60^3) + hypoglycemia,
                        random = ~age60,
                        subject = "ParticipantID",
                        data = data,
                        ng = ng,
                        mixture = ~age60 + I(age60^2) + I(age60^3),
                        B = random(initial_models$FBG), maxiter = 0)

     models$X2 <- hlme(X2 ~ age60 + I(age60^2) + I(age60^3),
                         random = ~age60,
                         subject = "ParticipantID",
+                         data = data,
                         ng = ng,
                         mixture = ~age60 + I(age60^2) + I(age60^3),
                         B = random(initial_models$LDLc), maxiter = 0)

    models$X3 <- hlme(X3 ~ age60 + I(age60^2) + I(age60^3) + hypotensor,
                        random = ~age60,
                        subject = "ParticipantID",
                        data = data,
                        ng = ng,
                        mixture = ~age60 + I(age60^2) + I(age60^3),
                        B = random(initial_models$SBP), maxiter = 0)
   }

   return(models)
}
nitial_models <- fit_trajectory_models(JinLin_man, 1, NULL)
ng_list <- 2:5

trajectory_models <- foreach(ng = ng_list, .combine = 'c', .packages = 'lcmm', .export = 'initial_models') %dopar% {
fit_trajectory_models(JinLin_man, ng, initial_models)
}

I received an "object not found" error, even though I have my 'initial models' ready.

Error in { : task 1 failed - "object 'initial_models' not found" In addition: Warning message: In e$fun(obj, substitute(ex), parent.frame(), e$data) : already exporting variable(s): initial_models

I'm also wondering if there exists other way to speed up the calculation

Many thanks, Jerry

[chrisraynerr]

Hello,

I am running a single multlcmm model.

  Mod0 <-
    multlcmm(
      ng      = 1,
      fixed   =  SCL1 + SCL2 + SCL3 + SCL4 ~ poly(Age,3) + CurrPreg + Postnatal + factor(Parity) + Plurality,
      random  = ~ 1 + Age,
      data    = DfR,
      subject = "ID",
      link    = "thresholds",
      methInteg = "QMC",
      nMC       = 1000,
      returndata = T,
      nproc   = NCORES
    )

cat > MultLcmm_IrtModel.sh << EOT
#!/bin/bash
export OMP_NUM_THREADS=1
export OPENBLAS_NUM_THREADS=1
source /cluster/bin/jobsetup
cd $working_dir
module purge
module load R/4.2.0-foss-2021b

Rscript --vanilla MultLcmm_IrtModel.R
EOT

sbatch --time=5-00:00:00 --cpus-per-task=10 --mem-per-cpu=20G --job-name=MultLcmm_IrtModel --output=logs/%x.%j.out \
MultLcmm_IrtModel.sh

Any obvious errors here? Are all the functions required exported to the different nodes? (I saw this as a suggestion on stackoverflow).

Thanks in advance! Chris

[VivianePhilipps]

Hi Chris,

I don't see any error here. I do it in the same way and it works correctly. Sorry, I don't know how to help you.

Viviane

[VivianePhilipps]

For Jerry's question :

I'm aware of that problem but I don't know how to fix it. The only solution I have is to use a global variable :

fit_trajectory_models <- function(data, ng, initial_models) {
models <- list()
if (ng == 1) {
models$X1 <- hlme(X1~ age60 + I(age60^2) + I(age60^3) + hypoglycemia,
                        random = ~age60,
                        subject = "ParticipantID",
                        data = data,
                        maxiter = 100)

     models$X2 <- hlme(X2~ age60 + I(age60^2) + I(age60^3),
                         random = ~age60,
                         subject = "ParticipantID",
                         data = data,
                         maxiter = 100)

     models$X3 <- hlme(X3 ~ age60 + I(age60^2) + I(age60^3) + hypotensor,
                        random = ~age60,
                        subject = "ParticipantID",
                        data = data,
                        maxiter = 100)
   } else {
 m1 <<- initial_models$FBG
     models$X1 <- hlme(X1 ~ age60 + I(age60^2) + I(age60^3) + hypoglycemia,
                        random = ~age60,
                        subject = "ParticipantID",
                        data = data,
                        ng = ng,
                        mixture = ~age60 + I(age60^2) + I(age60^3),
                        B = random(m1), maxiter = 0)
 m2 <<- initial_models$LDLc
     models$X2 <- hlme(X2 ~ age60 + I(age60^2) + I(age60^3),
                         random = ~age60,
                         subject = "ParticipantID",
                         data = data,
                         ng = ng,
                         mixture = ~age60 + I(age60^2) + I(age60^3),
                         B = random(m2), maxiter = 0)
     m3 <<- initial_models$SBP
    models$X3 <- hlme(X3 ~ age60 + I(age60^2) + I(age60^3) + hypotensor,
                        random = ~age60,
                        subject = "ParticipantID",
                        data = data,
                        ng = ng,
                        mixture = ~age60 + I(age60^2) + I(age60^3),
                        B = random(m3), maxiter = 0)
   }

   return(models)
}

If someone has a better solution, feel free to share it!

Viviane