Hi Georg,
Instead of stratifying by sex, use all your data together to fit one model that
controls for sex. This gives you more power because of the increased sample
size. Also, this way you can add an interaction term, which is what you are
looking for. The gender variable should be defined as a factor. Here's an
example:
dataframe$sex <- factor(dataframe$sex)
awesomemodel <- lm(outcomevariable ~ sex + x + sex*x, data = dataframe)
Hope this helps,
JoAnn
________________________________________
From: r-help-boun...@r-project.org [r-help-boun...@r-project.org] On Behalf Of
Georg Ehret [georgeh...@gmail.com]
Sent: Sunday, April 04, 2010 5:01 PM
To: r-help
Subject: [R] calculating an interaction statistic from stratified data
Dear R community,
I have data on beta&standard error (for the main effect of variable x),
stratified by sex for my dataset. I wish to calculate the sex-interaction
effect (as beta&se) from these two stratified datasets. Is there a package
to do this? If not, any advice how to do it manually?
Thank you very much and best regards, Georg.
************************
Georg Ehret, JHU, Baltimore
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