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!
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