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Hi there, I'm trying to fit GEE models on binomial data. My problem is how to "select" a model if ever it is possible and how I decide a factor has to be drop out or not. More specifically: 1) Are QIC (Pan 2001) reliable and can be used in a model selection framework (Burhnam and Anderson 1998)? If yes, how can the QIC be estimated from available information in a gee object? 2) If not, can R compute an ANOVA table like for GLM (seems that SAS does it)? The problem is to know whether a factor with more than 2 levels is significant or not. Can I consider such a factor as...

Hi there, I'm trying to fit GEE models on binomial data. My problem is how to "select" a model if ever it is possible and how I decide a factor has to be drop out or not. More specifically: 1) Are QIC (Pan 2001) reliable and can be used in a model selection framework (Burhnam and Anderson 1998)? If yes, how can the QIC be estimated from available information in a gee object? 2) If not, can R compute an ANOVA table like for GLM (seems that SAS does it)? The problem is to know whether a factor with more than 2 levels is significant or not. Can I consider such a factor as...

Hi there, I'm trying to fit GEE models on binomial data. My problem is how to "select" a model if ever it is possible and how I decide a factor has to be drop out or not. More specifically: 1) Are QIC (Pan 2001) reliable and can be used in a model selection framework (Burhnam and Anderson 1998)? If yes, how can the QIC be estimated from available information in a gee object? 2) If not, can R compute an ANOVA table like for GLM (seems that SAS does it)? The problem is to know whether a factor with more than 2 levels is significant or not. Can I consider such a factor as...