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The lapply loop and the for loop have very similar speed characteristics. Differences seen are almost always due to how you use memory in the body of the loop. This fact is not new. You may be under the incorrect assumption that using lapply is somehow equivalent to "vectorization", which it is not. -- Sent from my phone. Please excuse my brevity. On August 7, 2017 7:29:58 AM PDT,

Hi! I am doing a lapply and for comparaison and I get that for is faster than lapply. What I have done: n<-100000 set.seed(123) x<-rnorm(n) y<-x+rnorm(n) rand.data<-data.frame(x,y) k<-100 samples<-split(sample(1:n),rep(1:k,length=n)) res<-list() t<-Sys.time() for(i in 1:100){ modelo<-lm(y~x,rand.data[-samples[[i]]])

Dear Jesus, The difference is marginal when each code chunk does the same things. Your for loop does not yields the same output as the lapply. Here is the cleaned version of your code. n<-10000 set.seed(123) x<-rnorm(n) y<-x+rnorm(n) rand.data<-data.frame(x,y) k<-100 samples <- split(sample(n), rep(seq_len(k),length=n)) library(microbenchmark) microbenchmark( "for"