bigmemory and biglm packages may be of your interest.
b
On Oct 28, 2009, at 8:50 AM, Georg Ehret wrote:
Dear R community,
I have a fairly large file with variables in rows. Every variable
(thousands) needs to be regressed on a reference variable. The file
is too
big to load into R (or R gets too slow having done it) and I do now
read in
line by line with "scan" (see below) and write the results to out.
Although
improved, this is still very slow... Can someone please help me and
suggest
how I can make this faster?
Thank you and best regards, Georg.
*******************************************
Georg Ehret, Johns Hopkins U, Baltimore MD, USA
for (i in 16:nmax){
line<-
scan(file=paste(file),nlines=1,skip=(i-1),what="integer",sep=",")
d<-as.numeric(line[-1])
name<-line[1]
modela <- lm(s1~a+a2+b+s+M+W)
modelb <- lm(s2~a+a2+b+s+M+W+d)
modelc <- lm(s3~a+2+b+s+M+W+d+d*s)
p_main <- anova(modela,modelb)$P[2]
p_main_i <- anova(modela,modelc)$P[2]
p_i <- anova(modelb,modelc)$P[2]
cat
(c(name,p_main,p_main_i,p_i),file=paste("out",".txt",sep=""),append=T)
cat("\n",file=paste("out",".txt",sep=""),append=T)
}
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and provide commented, minimal, self-contained, reproducible code.
