[R] lme speedup question

[Dick Beyer]

I am hoping someone will be kind enough to have a look at the following piece of code and tell me if there is a way to run lme() so it is a lot faster. The inner loop, j in 1:15000, takes about 2 hrs on my 2.8GHz dual Xeon 4GB RAM machine.  The timings I have done show the dominant execution time is in lme.

options(contrasts=c("contr.sum", "contr.sum"))
getOption("contrasts")
vg  <- c(1,2,1,2,2,1,2,1,1,2,1,2,2,1,2,1,1,2,1,2,2,1,2,1)
ag  <- c(1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,6,6,6,6)
dy  <- c(1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2)
rp  <- c(1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3)

for (perm in 1:216){
 vg <- vgAll[perm,]
 for(j in 1:15000){
  ge <- c(philmaanova.rloess$adjdata[j,1:24])
  dat <- data.frame(ge,vg=factor(vg),ag=factor(ag),dy=factor(dy),rp=factor(rp))
  dat$vgrp <- getGroups(dat, form = ~ 1|vg/rp, level = 2)
  ge.lme   <- lme(fixed=ge~vg+ag+dy, data=dat, random=~1|vgrp)
  cp1[j,1] <- philmaanova.rloess$cloneid[j]
  tmpInt   <- intervals(ge.lme,level=0.95,which="fixed")
  cp1[j,2] <- tmpInt$fixed[2,2]*2
  cp1[j,3] <- tmpInt$fixed[2,1]
  cp1[j,4] <- tmpInt$fixed[2,3]
}
}

An Aside: I am using lme for a mixed model with treating rp as random and nested in the fixed effect vg.  I am collecting the coefficients and se's so I can later calculate a modified t-test (like SAM does, Significance Analysis of Microarrays).

Thanks very much for any help,
Dick
*******************************************************************************
Richard P. Beyer, Ph.D.	University of Washington
Tel.:(206) 616 7378	Env. & Occ. Health Sci. , Box 354695
Fax: (206) 685 4696	4225 Roosevelt Way NE, # 100
			Seattle, WA 98105-6099