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]]]) prediccion<-predict(modelo,rand.data[samples[[i]],]) res[[i]] <- (prediccion - rand.data$y[samples[[i]]]) } print(Sys.time()-t) Which takes 8.042 seconds and using Lapply cv.fold.fun <- function(index){ fit <- lm(y~x, data = rand.data[-samples[[index]],]) pred <- predict(fit, newdata = rand.data[samples[[index]],]) return((pred - rand.data$y[samples[[index]]])^2) } t<-Sys.time() nuevo<-lapply(seq(along = samples),cv.fold.fun) print(Sys.time()-t) Which takes 9.56 seconds. So... has been improved the FOR loop on R??? Thanks! [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.