Get multiple values out of parallel foreach loop - r

Question

I've tried searching but haven't located anything that's gotten me all the way.
I'm running an occupancy prediction model on a stack of three rasters. Due to the large amount of processing that needs to happen I'm using a parallel foreach loop.
I need to retrieve three variables out of the results from the loop: test, na, and pred. I need those three values to fill in the new raster values and maintain the same extent. Unless someone knows a way to fill in the gaps created by NA values during processing?
Below is the code I've been trying to use based on posts I've found.
I also tried nesting foreach loops, but I'm not sure I understand how those work, or if that would achieve my ends.
library(parallel)
library(doSNOW)
multiResultClass<- function(test = NULL, tmp = NULL, na = NULL, pred = NULL){
results<- list(
test = test,
tmp = tmp,
na = na,
pred = pred
)
class(results)<- append(class(results), "multiResultClass")
return(results)
}
nc<- detectCores()-1
cl<- makeCluster(nc)
registerDoSNOW(cl)
predicts<- foreach (i = 1:nrow(pm), .multicombine = T, .maxcombine = 1000,
.packages = c("unmarked", "raster"), .verbose = T)%dopar%{
results<- multiResultClass()
test<- cellFromRow(pm, i)
tmp<- data.frame(pm[test])
na<- any(is.na(tmp[i, ]))
if(length(which(na) != nrow(tmp))){
pred<- predict(fmBest, "state", tmp)
}
results$test<- test
results$tmp<- tmp
results$na<- na
results$pred<- pred
return(results)
}
foreach(i = 1:nrow(pm))%do%{
test<- predicts[[i]]$test
na<- predicts[[i]]$na
pred<- predicts[[i]]$pred
}
stopCluster(cl)
I have a working foreach loop that gets me the pred values, but without test and na I haven't found a way to properly fill in the raster template the data needs to go into. That foreach loop is below:
library(parallel)
library(doSNOW)
ns<- detectCores()-1
cl<- makeCluster(ns);cl
registerDoSNOW(cl)
predictions<-
foreach (i = 1:nrow(pm), .multicombine = T, .maxcombine = 5000,
.packages = c("unmarked", "raster"), .verbose = T)%dopar%{
test<- cellFromRow(pm, i)
tmp<- data.frame(pm[test])
na<- any(is.na(tmp[i, ]))
if(length(which(na) != nrow(tmp))){
predict(fmBest, "state", tmp)
}
}
stopCluster(cl)

Answer 1

I finally found a combine function I could make work. Below is the code I used that returns values for test, na, and pred all in a large list.
library(doSNOW)
nc<- detectCores()-1
cl<- makeCluster(nc);cl
registerDoSNOW(cl)
comb<- function(...){
mapply('rbind', ..., SIMPLIFY = F)
}
predictions<- foreach(i = 1:nrow(pm), .combine = 'comb', .multicombine = T,
.maxcombine = 200, .packages = c("unmarked", "raster"), .verbose = T,
.inorder = F)%dopar%{
#get cell number values from raster stack
test<- cellFromRow(pm, i)
# make into a data.frame for prediction
tmp<- data.frame(pm[test])
# test which are na
na<- any(is.na(tmp[i, ]))
# avoid NA values entering the predict function
if(length(which(na)) != nrow(tmp)){
# # Predict the new data
pred<- predict(fmBest, "state", tmp)
}
list(test, na, pred)
}
stopCluster(cl)