[R] regression on a matrix

[Eduardo Leoni]

Hi - 

I am doing a monte carlo experiment that requires to do a linear
regression of a matrix of vectors of dependent variables on  a fixed
set of covariates (one regression per vector). I am wondering if
anyone has any idea of how to speed up the computations in R. The code
follows:

#regression function
#Linear regression code
qreg <- function(y,x) {
  X=cbind(1,x)
  m<-lm.fit(y=y,x=X)
  p<-m$rank

  r <- m$residuals
  n <- length(r)
  rss <- sum(r^2)
  resvar <- rss/(n - p)		
  
  Qr <- m$qr
  p1 <- 1:p
  R <- chol2inv(Qr$qr[p1, p1, drop = FALSE])
  se <- sqrt(diag(R) * resvar)
  b <- m$coefficients
  return(c(b[2],se[2]))
}


#simulate
a <- c(1,.63,.63,1)
a <- matrix(a,2,2)
c <- chol(a)
C <- 0.7
theta <- 0.8
sims <- 1000
n<-20

u <- rnorm(n,0,sqrt(1-C))
w <- rgamma(n,C/theta,1/theta)
e <- rnorm(n,0,sqrt(w))
  
x1 <- rnorm(n)
x <- x1*c[2,2]+c[1,2]*w
v <- e+u
y <- 1+x+v
w <- rgamma(n,C/theta,1/theta)

#create matrix of dep variable
newdep <- matrix(rnorm(length(y)*sims,y,sqrt(w)),c(length(y),sims))


monte <- apply(newdep,2,qreg,x=x)