Hi Ed,
It's little hard to work out exactly what you want, but here's a guess:
esdf<-data.frame(GENO=rep(c("control","A","B","AB"),each=20),
age=rep(c(10,20),40),OBS=runif(80,1,21))
for(age in c(10,20)) {
for(geno in c("A","B","AB"))
print(t.test(OBS~GENO,esdf[esdf$age==age &
esdf$GENO %in% c("control",geno),]))
}
Note that this is not a good way to use t.test, nor a good way to
analyze data like this. Look at defining sensible contrasts and using
ANOVA or a similar approach.
Jim
On Fri, Jan 20, 2017 at 5:20 AM, Ed Siefker <ebs15...@gmail.com> wrote:
> I have a data set with observations on groups with multiple variables.
> Let's call them GENO and AGE. I have control and test genotypes
> and two different ages. It is only meaningful to compare control and
> test within the same age.
>
> I'd like to get the p value for each group compared back to control
> of the appropriate age. T-test requires that the grouping factor has
> exactly two levels. How can I do this efficiently?
>
> I was hoping something like ttest(OBS ~ GENO * AGE, mydata) would work.
> Is there something I can do with tapply() or aggregate() to do this?
> I'd like to end up with a table that looks like this:
>
> GENO Age OBS p.val
> control 10 1.1 1
> control 10 0.9 1
> control 20 2.1 1
> control 20 1.9 1
> A 10 11 0.01224066
> A 10 9 0.01224066
> A 20 21 0.003102783
> A 20 19 0.003102783
> B 10 4 0.057714305
> B 10 6 0.057714305
> B 20 14 0.005923285
> B 20 16 0.005923285
> AB 10 1 0.698488655
> AB 10 1.1 0.698488655
> AB 20 2 0.552786405
> AB 20 2.2 0.552786405
>
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