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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...

...** log(dose) 1.9644 0.1750 11.22 <2e-16 *** Null deviance: 408.353 on 4 degrees of freedom Residual deviance: 10.828 on 3 degrees of freedom AIC: 32.287 Another way to do the same analysis is to reformulate the data, and use GLM with weights: >y1=c(rep(0,5),rep(1,5)) >dose1=rep(dose,2) >number = c(batch-dead,dead) >data1=as.data.frame(cbind (y1,dose,number)) >model2=glm(y1~log(dose1),binomial,weights=number,data=data1) >summary(model2) Which returns: Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -4.5318 0.4381 -...

Hi Everyone, I have data in a long format e.g. there is one row per patient but each follow-up appointment is included in the row. So, a snippet of the data looks like this: TrialNo Drug Sex Rand Adate1 Date1 Dose1 Time1 Adate2 Date2 Dose2 Time2 B1001029 LTG M 15719 30/04/2003 15825 150 106 29/08/2003 15946 200 227 B1117003 LTG M 15734 30/04/2003 15825 200 91 03/09/2003 15951 250 217 B138015 LTG M 14923 06/02/2001 15012 225 89 08/05/2001 15103 300 180 B112003 TPM F 14914 15/01/2001 14990 60 76 05/03/2001 15...

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