try something like this (untested):
dataCountry <- split(data, data$COUNTRY)
model.per.country <- lapply(dataCountry, function (x) {
glm(dependent.var ~ FEMALE + AGE + EDUCLIN, family = binomial,
data = x)
})
I hope it helps.
Best,
Dimitris
----
Dimitris Rizopoulos
Ph.D. Student
Biostatistical Centre
School of Public Health
Catholic University of Leuven
Address: Kapucijnenvoer 35, Leuven, Belgium
Tel: +32/(0)16/336899
Fax: +32/(0)16/337015
Web: http://med.kuleuven.be/biostat/
http://www.student.kuleuven.be/~m0390867/dimitris.htm
----- Original Message -----
From: "Rense Nieuwenhuis" <[EMAIL PROTECTED]>
To: <[EMAIL PROTECTED]>
Sent: Friday, December 07, 2007 9:32 AM
Subject: [R] Same regression per sub-group: apply?
> Dear helpers,
>
> I've come up with what is probably a simple problem, but I cannot
> find the solution. I have a data-set containing survey-data from
> several countries. What I want to do is to perform some regression
> analyses, for each country separately. The question is, how to do
> this nicely (thus without repeating the same syntax with another
> `subset' argument).
>
> I thought of the following:
>
> model.per.country <- tapply(data, data$COUNTRY, function(x) glm
> (dependent.var ~ FEMALE + AGE + EDUCLIN + (), family=binomial,
> data=capital))
>
> But this does not work. What goes wrong, I think, is that the
> dependent variable is clustered according to `Country', but not so
> for the predictors. The error message I received:
>
> Error in tapply(dat, dat$COUNTRY, function(x) glm(participate ~
> FEMALE + :
> arguments must have same length
> >
>
>
> Could you please help me solving this little problem?
>
> Thanks in advance:
>
> Rense Nieuwenhuis
>
>
>
> [[alternative HTML version deleted]]
>
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>
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