Vectorization using apply family of functions in R

Question

I am working with Linear Regression model, and I want to calculate some performance measures manually. I split my data using Leave One Out Cross-Validation (LOOCV).

The following R code give me the desired results but takes a long time since I am using for loop with LOOCV.

Is there a way to rewrite my code in a fast way using, for example, apply family of functions in R?

The dataset is uploaded from here

wdbc <- read_excel("Folds5x2_pp.xlsx") 
wdbc[] <- lapply(wdbc, scale)

   dim(wdbc)

9568    5

   head(wdbc)

1 -0.629 -0.987  1.82  -0.00952  0.521
2  0.742  0.681  1.14  -0.975   -0.586
3 -1.95  -1.17  -0.185  1.29     2.00 
4  0.162  0.237 -0.508  0.228   -0.462
5 -1.19  -1.32  -0.678  1.60     1.14 
6  0.888  0.404 -0.173 -0.996   -0.627

fitted_value <- rep(0,nrow(wdbc))

for(i in 1:nrow(wdbc)){
test<-wdbc[i,]
training<-wdbc[-i,]
m=lad(PE ~ ., data=training, method="BR")

co.data = coef(m)
x = cbind(1, as.matrix(test[, !(colnames(test) %in% "PE")]))
fitted_value[i] <- x %*% co.data
    }

R2<-(cor(wdbc$PE,fitted_value)^2) 
SAD<-sum(abs(wdbc$PE-fitted_value))

c(round(SAD,2) ,round(R2,2))

NOTE 1

The data used in the question is only for an explanation because in my project I have many datasets with high dimensions.

EDIT

Based on @Dominic van Essen answer, I used the following R code using parSapply function from parallel package but it takes time more than the for loop.

library(parallel)

mycluster=makeCluster(detectCores()-1) 
wdbc <- read_excel("Folds5x2_pp.xlsx") 
wdbc[] <- lapply(wdbc, scale)
clusterExport(mycluster,c("lad","wdbc")) 

fitted_value = parSapply(mycluster,seq_len(nrow(wdbc)),function(i) {
    for(i in 1:nrow(wdbc)){
    test<-wdbc[i,]
    training<-wdbc[-i,]
    m=lad(PE ~ ., data=training, method="BR")

    co.data = coef(m)
    x = cbind(1, as.matrix(test[, !(colnames(test) %in% "PE")]))
  }
    return (x %*% co.data)
})

NOTE 2

I have 8 cores, and "PE" is the dependent variable in my data set.

Answer 1

You can easily re-write your loop using sapply instead of for... , although, as bzki commented, this alone will not speed-up your code:

# sapply version:
fitted_value = sapply(seq_len(nrow(wdbc)),function(i) {
    # put all the gubbins in here
    # ...
    return (x %*% co.data)
})

However, if you have multiple cores available on your computer, or - even better - access to a server with many processors, then an sapply loop can easily be parallelized using parSapply from the 'parallel' package, as shown in this example:

# slow sapply loop (takes 12s):
data=123
answer = sapply(1:12,function(i) {
    Sys.sleep(1)
    return(data+i)
})
# faster parallel version (takes 4s on my laptop with 4 cores):
library(parallel)
mycluster=makeCluster(detectCores()-1) # leave 1 core available for system 
data=123
clusterExport(mycluster,"data") # specify variable(s) that should be available to parallel function
answer = parSapply(mycluster,1:12,function(i) {
    Sys.sleep(1)
    return(data+i)
})
stopCluster(mycluster)