Is there any way to make the following algorithm faster, such as using an apply function?
set.seed(1)
n=1000
y=rnorm(n)
x1=rnorm(n)
x2=rnorm(n)
lm.ft=function(y,x1,x2)
return(lm(y~x1+x2)$coef)
res=list();
for(i in 1:n){
x1.bar=x1-x1[i]
x2.bar=x2-x2[i]
res[[i]]=lm.ft(y,x1.bar,x2.bar)
}
Use lm.fit instead of lm:
fun1 <- function() {
res=list();
for(i in 1:n){
x1.bar=x1-x1[i]
x2.bar=x2-x2[i]
res[[i]]=lm.ft(y,x1.bar,x2.bar)
}
res
}
lm.ft2 <- function(y,x1,x2) lm.fit(cbind(1,x1,x2), y)$coef
fun2 <- function() {
res2 <- sapply(seq_along(y), function(i, x1, x2, y) {
x1.bar=x1-x1[i]
x2.bar=x2-x2[i]
lm.ft2(y,x1.bar,x2.bar)
}, x1=x1, x2=x2, y=y)
res2
}
library(microbenchmark)
microbenchmark(res <- fun1(), res2 <- fun2(), times=10)
#Unit: milliseconds
# expr min lq median uq max neval
#res <- fun1() 147.776069 149.580443 152.64378 159.53053 166.06834 10
#res <- fun2() 8.760102 9.004934 10.34582 10.58757 13.86649 10
all.equal(
unname(t(res2)),
unname(do.call(rbind,res))
)
#[1] TRUE
for loop. However, for loops are actually pretty fast in R (and lapply and friends are not faster). What you do inside the loop usually is the time consuming part. The best speed-up you can achieve by using vectorized functions.
