Much thanks to Chuck Cleland for the following solution:
t(sapply(split(mydf, mydf$TIME), function(x){coefficients(summary(glm(X ~ A
+ B, data = x, family=binomial)))}))
To see what's in the 12 columns of the matrix returned by those lines, look
at the results this way:
lapply(split(mydf, mydf$TIME), function(x){coefficients(summary(glm(X ~ A +
B, data = x, family=binomial)))})
-----Original Message-----
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On
Behalf Of Mike H. Ryu
Sent: Tuesday, April 29, 2008 7:27 AM
To: r-help@r-project.org
Subject: [R] Running regression (lm, lrm) 100+ times and saving the results
as matrix
An undergraduate here, so do not hesitate to let me know if you feel that
I'm heading in a wrong direction.
I have a data frame containing panel data across 10 years (hence 120
months). I want to be able to run regression separately for each month (or
year). The below shows how I ran the regression for each month, but I need
to know how I would combine the regression results together into a matrix
possibly.
Thank you!
Mike
-------------------------------
m = max(data$TIME)
# define regmatrix
for(i in 1:m){
g=runreg(data, i)
# attach g to regmatrix
}
runreg = function(data, index){
datainterim = subset(data, TIME==index)
g = lrm(X ~ A + B, datainterim)
return(g)
}
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
