If you run it under the profiler in RStudio, you will see that the 'lm'
call is taking about 2 seconds longer in the function which might have to
do with resolving the reference. So it is probably the function call in
'lapply' vs. the in-line statement in the 'for' loop that account for the
differ
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<-1
set.seed(123)
x<-rnorm(n)
y<-x+rnorm(n)
rand.data<-data.frame(x,y)
k<-100
samples <- split(sample(n), r
mailto:r-help-boun...@r-project.org] On Behalf Of Jesús Para
Fernández
Sent: Monday, August 7, 2017 9:30 AM
To: r-help@r-project.org
Subject: [R] Has For bucle be impooved in R
Hi!
I am doing a lapply and for comparaison and I get that for is faster than
lapply.
What I have done:
n<-1000
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
Hi!
I am doing a lapply and for comparaison and I get that for is faster than
lapply.
What I have done:
n<-10
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
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