have you tried:
fits <- lm(a~b)
fstat <- sapply(summary(fits), function(x) x[["fstatistic"]][["value"]])
it takes 3secs for 100K columns on my machine (running on batt)
b
On Aug 23, 2009, at 9:55 PM, big permie wrote:
Dear R users,
I have a matrix a and a classification vector b such that
str(a)
num [1:50, 1:800000]
and
str(b)
Factor w/ 3 levels "cond1","cond2","cond3"
I'd like to do an anova on all 800000 columns and record the F
statistic for
each test; I currently do this using
f.stat.vec <- numeric(length(a[1,])
for (i in 1:length(a[1,]) {
f.test.frame <- data.frame(nums = a[,i], cond = b)
aov.vox <- aov(nums ~ cond, data = f.test.frame)
f.stat <- summary(aov.vox)[[1]][1,4]
f.stat.vec[i] <- f.stat
}
The problem is that this code takes about 70 minutes to run.
Is there a faster way to do an anova & record the F stat for each
column?
Any help would be appreciated.
Thanks
Heath
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