[R] regression on a matrix
Eduardo Leoni
e.leoni at gmail.com
Thu Mar 3 23:15:39 CET 2005
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)
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