Matthew -
Others will probably tell you about the folly of performing
1733 t-tests on groups with 4 observations each, but an alternative
to your approach would be to use R to solve your problem. (I'm
using var.equal=TRUE because that's what you're calculating, but
you might consider using the default behaviour assuming unequal
variances.)
onerow = function(i){
thetest = t.test(noncancer[i,],cancer[i,],var.equal=TRUE)
c(thetest$statistic,thetest$p.value,as.numeric(thetest$p.value < 0.05))
}
answer = t(sapply(1:nrow(cancer),onerow))
Then answer should have the information that you want.
- Phil Spector
Statistical Computing Facility
Department of Statistics
UC Berkeley
spec...@stat.berkeley.edu
On Tue, 25 Jan 2011, Leitch, Matthew C. wrote:
From: Leitch, Matthew C.
Sent: Monday, January 24, 2011 6:53 PM
To: 'i...@network-theory.co.uk'
Subject: question about the pt() calculation
Hello
Thank you for your time. I am a graduate student at the University of Texas Medical
Branch, and I was wondering if you could help me with a R program I am writing. I have
some data that is stored a file that has 1733 rows and 4 columns. Each row is
independent, so I have a loop system so I don't have to manually perform 1733 t-tests.
However I got most of it to work, thanks to the "An Introduction to R" book and
a helpful colleague. But I am having some difficulty with getting my t-test scores
converted to p-values. The pt() test won't take the t object, but I am unsure how to make
it create a p-value for each row. If you have any suggestions they would be greatly
appreciated.
Best Wishes
Matt
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