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Simple illustration, > df3 <- data.frame(id=c(3,2,1,4), age=c(40,50,60,50), dose1=c(1,2,1,2), dose2=c(2,1,2,1), dose4=c(3,3,3,3))> df3 id age dose1 dose2 dose41 3 40 1 2 32 2 50 2 1 33 1 60 1 2 34 4 50 2 1 3> melt.data.frame(df3, id.var=1:2, na.rm=T) id age variable value1 3 40 dose1 12 2 50 dose1 23 1 60 dose1 14 4 5...

A multinomial logit model can be specified as a conditional logit model after restructuring the data. Doing so gives flexibility in imposing restrictions on the dependent variable. One application is to specify a loglinear model for square tables, e.g. quasi-symmetry or quasi-independence, as a multinomial logit model with covariates. Further details on this technique and examples with several