Hi:
Avoiding loops in R by using vectorization is usually one of the best ways
to improve performance. Since we can't replicate your data (e.g., tslength
is not given), instead I'll generate a couple of functions to extract the
estimated beta from randomly generated data. The replicate() function comes
in handy for this type of simulation.
(1) Create a function to construct the sample and run the model:
wf <- function() {
x <- sample(1:20, 10)
y <- 2 + 1.5 * x + rnorm(20)
summary(lm(y ~ x))$coef[2]
}
system.time(replicate(1000, wf()))
user system elapsed
3.17 0.00 3.17
(2) Use a loop instead of replicate():
b <- numeric(1000)
system.time(for(i in seq_along(b)) b[i] <- wf())
user system elapsed
3.17 0.00 3.18
(3) Use lsfit() instead (only works for simple linear regression):
wf2 <- function() {
x <- sample(1:20, 10)
y <- 2 + 1.5 * x + rnorm(10)
lsfit(x, y)$coef[2]
}
system.time(replicate(1000, wf2()))
user system elapsed
0.45 0.00 0.46
Hopefully you can adapt one or more of these functions to your needs.
Dennis
On Fri, Jul 30, 2010 at 1:18 AM, Natasa Tzortzaki <tz_nat...@hotmail.com>wrote:
>
> hello,
>
> i am new to R and trying to calculate the beta coefficient for standard
> linear regression for a series of randomly generated numbers. I have created
> this loop, but it runs really slow, is there a way to improve it?
>
> #number of simulations
> n.k<-999
> #create the matrix for regression coefficients generated from #random data
>
> beta<-matrix(0,1,n.k+1)
> e<-matrix(0,tslength,n.k+1)
>
>
> for(k in 1:n.k+1)
> {
> for(i in 1:tslength)
> {
> beta[1,1]<-beta1
> e[i,k]<-c(rnorm(1,0,var.all))
> beta[1,k]<-summary(lm(e[1:tslength,k]~t))$coefficient[2]
> }
> }
> thanks
> Anastasia Tzortzaki
>
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>
> ______________________________________________
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