Data frame creation inside Parlapply in R - r

Question

I am trying something pretty simple, want to run a bunch of regressions parallelly. When I use the following data generator (PART 1), The parallel part does not work and give the error listed below
#PART 1
p <- 20; rho<-0.7;
cdc<- diag(p)
for( i in 1:(p-1) ){ for( j in (i+1):p ){
cdc[i,j] <- cdc[j,i] <- rho^abs(i-j)
}}
my.data <- mvrnorm(n=100, mu = rep(0, p), Sigma = cdc)
The following Parallel Part does work but if I generate the data as PART 2
# PART 2
my.data<-matrix(rnorm(1000,0,1),nrow=100,ncol=10)
I configured the function that I want to run parallelly... as
parallel_fun<-function(obj,my.data){
p1 <- nrow(cov(my.data));store.beta<-matrix(0,p1,length(obj))
count<-1
for (itration in obj) {
my_df<-data.frame(my.data)
colnames(my_df)[itration] <- "y"
my.model<-bas.lm(y ~ ., data= my_df, alpha=3,
prior="ZS-null", force.heredity = FALSE, pivot = TRUE)
cf<-coef(my.model, estimator="MPM")
betas<-cf$postmean[-1]
store.beta[ -itration, count]<- betas
count<-count+1
}
result<-list('Beta'=store.beta)
}
So I write the following way of running parlapply
{
no_cores <- detectCores(logical = TRUE)
myclusternumber<-(no_cores-1)
cl <- makeCluster(myclusternumber)
registerDoParallel(cl)
p1 <- ncol(my.data)
obj<-splitIndices(p1, myclusternumber)
clusterExport(cl,list('parallel_fun','my.data','obj'),envir=environment())
clusterEvalQ(cl, {
library(MASS)
library(Matrix)
library(BAS)
})
newresult<-parallel::parLapply(cl,obj,fun = parallel_fun,my.data)
stopCluster(cl)
}
But whenever am doing PART 1 I get the following error
Error in checkForRemoteErrors(val) :
7 nodes produced errors; first error: object 'my_df' not found
But this should not happen, the data frame should be created, I have no idea why this is happening. Any help is appreciated.

Answer 1

Posting this as one possible workaround, see if it works:
parallel_fun<-function(obj,my.data){
p1 <- nrow(cov(my.data));store.beta<-matrix(0,p1,length(obj))
count<-1
for (itration in obj) {
my_df<-data.frame(my.data)
colnames(my_df)[itration] <- "y"
my_df <<- my_df
my.model<-bas.lm(y ~ ., data= my_df, alpha=3,
prior="ZS-null", force.heredity = FALSE, pivot = TRUE)
cf<-BAS:::coef.bas(my.model, estimator="MPM")
betas<-cf$postmean[-1]
store.beta[ -itration, count]<- betas
count<-count+1
}
result<-list('Beta'=store.beta)
}
The issue seems to be with BAS:::coef.bas function, that calls eval in order to get my_df and fails to do that when called in parallel. The "hack" here is to force my_df out to the parent environment by calling my_df <<- my_df.
There should be a better way to do this, but <<- might be the fastest one. In general, <<- may cause unwanted behaviour, especially when used in loops. Assigning unique variable name before exporting (and don't forgetting to remove after use) is one way to tackle them.