Panel data with binary dependent variable in R

I开发者_如何学Gos it possible to do regressions in R using a panel data set with a binary dependent variable? I am familiar with using glm for logit and probit and plm for panel data, but am not sure how to combine the two. Are there any existing code examples?

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It would also be helpful if I could figure out how to extract the matrix that plm() is using when it does a regression. For instance, you could use plm to do fixed effects, or you could create a matrix with the appropriate dummy variables and then run that through glm(). In a case like this, however, it is annoying to generate the dummies yourself and it would be easier to have plm do it for you.

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The package "pglm" might be what you need.

http://cran.r-project.org/web/packages/pglm/pglm.pdf

This package offers some functions of glm-like models for panel data.

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Maybe the package lme4 is what you are looking for. It seems to be possible to run generalized regressions with fixed effects using the comand glme. But you should be aware that panel data with binary dependent variable is different than the usual linear models.

This site may be helpful.

Best regards, Manoel

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model.frame(plmmodel) 

will give you the data frame that is actually used by plm for fitting the model (i.e. after list-wise deletion if you have NAs, etc.)

I don't think that plm has implemented functions to estimate models with binary outcomes, but I may be wrong. Check out the reference manual at: http://cran.r-project.org/web/packages/plm/index.html

If I'm right, this would suggest that you can't "combine the two" without considerable work in extending the functions provided by plm.