One more note. In case it is helpful, I am including the code for my loop:
# data is read in
numSNPs <- ncol(myData);
pvalues <- rep(-1,numSNPs);
names(pvalues) <- colnames(myData);
for (SNPnum in 1:numSNPs)
{
is.na(pvalues[SNPnum]) <- TRUE;
try({
fit.yags <- yags(log(myPhenos$PHENO
I monitored the usage of memory on a script that I ran. It ran 30K
regressions and it stores p-values for one of the
coefficients. It read in a file that has 3000 rows and about 30K
columns. The size of the file is about 170 MB.
My understanding is that memory usage started out at 2.2G and went up
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