I have implemented a quite long program in R and I have noticed that one block, concearning a backward model selection, is too slow and I would like to receive some suggestions in order to speed up all the program. Here is the code:library(MASS)df.Cars <- na.omit(Cars93[-57, -c(1,2,4,6,27)])rownames(df.Cars) <- 1:82for(j in c(1, 5, 6, 12, 22)) { df.Cars[, j] <- as.numeric(df.Cars[, j]) - 1}df.Cars$Cylinders <- as.numeric(as.character(df.Cars$Cylinders))df.Cars <- data.frame(df.Cars, RPM2 = df.Cars$RPM^2)pos <- sample(1:82, 82)list_folds <- c(list(pos[1:9]), list(pos[10:18]), lapply(0:7, function(x) pos[19:26 + x*8]))names(list_folds) <- paste0(fold_, 1:10)best_models <- list()for(j in 1:10) {     best_models[[j]] <- as.list(rep(NA,22))}The slow part begins now:for(j in 1:length(list_folds)) {   mydf <- df.Cars[unlist(list_folds[-j]),]   best_models[[j]][[22]] <- glm(Origin ~., binomial, mydf)   for(i in 21:1) {      matr <- colnames(model.matrix(best_models[[j]][[i+1]]))[-1]      mod0 <- lapply(matr, function(x) {          formul <- eval(parse(text=paste(.~. -, x)))          update(best_models[[j]][[i+1]], formul)          })  DEVmin <- which.min(sapply(mod0, deviance))best_models[[j]][[i]] <- mod0[[DEVmin]]       }}
Speed up my backward model selection
performance;r