[R] very fast OLS regression?
ivo welch
ivowel at gmail.com
Wed Mar 25 21:28:28 CET 2009
Dear R experts:
I just tried some simple test that told me that hand computing the OLS
coefficients is about 3-10 times as fast as using the built-in lm()
function. (code included below.) Most of the time, I do not care,
because I like the convenience, and I presume some of the time goes
into saving a lot of stuff that I may or may not need. But when I do
want to learn the properties of an estimator whose input contains a
regression, I do care about speed.
What is the recommended fastest way to get regression coefficients in
R? (Is Gentlemen's weighted-least-squares algorithm implemented in a
low-level C form somewhere? that one was always lightning fast for
me.)
regards,
/ivo
bybuiltin = function( y, x ) coef(lm( y ~ x -1 ));
byhand = function( y, x ) {
xy<-t(x)%*%y;
xxi<- solve(t(x)%*%x)
b<-as.vector(xxi%*%xy)
## I will need these later, too:
## res<-y-as.vector(x%*%b)
## soa[i]<-b[2]
## sigmas[i]<-sd(res)
b;
}
MC=500;
N=10000;
set.seed(0);
x= matrix( rnorm(N*MC), nrow=N, ncol=MC );
y= matrix( rnorm(N*MC), nrow=N, ncol=MC );
ptm = proc.time()
for (mc in 1:MC) byhand(y[,mc],x[,mc]);
cat("By hand took ", proc.time()-ptm, "\n");
ptm = proc.time()
for (mc in 1:MC) bybuiltin(y[,mc],x[,mc]);
cat("By built-in took ", proc.time()-ptm, "\n");
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