> -----Original Message-----
> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org]
> On Behalf Of Natasa Tzortzaki
> Sent: Friday, July 30, 2010 1:19 AM
> To: r-help@r-project.org
> Subject: [R] (no subject)
>
>
> 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
>
Anastasia,
You haven't provided a reproducible example, so I am going to guess at some of
what you are doing. You also have used some function names for your variable
names which I like to avoid, so I have used 'b' as the name of the vector for
collecting beta coefficients and 'ts' for the predictor variable. I have set
var.all equal to 1.
To speed up your process, you want to eliminate looping as much as possible.
In addition, you should pre-allocate space for vectors that you are going to
fill up in a loop. Your e values can all be generated at once outside of your
loop. I also used the coef() extractor function directly on the lm() object.
So, you could do something like the following:
# number of simulations
n.k <- 999
# predictor variable
ts <- 1:100
tslength <- length(ts)
var.all <- 1
# pre-allocate vector to collect beta coefficients
b <- numeric(n.k)
# generate your e values all at once
# columns contain values for each simuation trial
e <- matrix(rnorm(n.k*tslength,0,var.all), ncol = n.k)
# run your analyses
for(k in 1:n.k)
{
b[k] <- coef(lm(e[,k] ~ ts))[2]
}
Hope this is helpful,
Dan
Daniel Nordlund
Bothell, WA USA
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