Hi!
Does anyone know how to do the test for goodness of fit of a logistic model (in rms package) after running fit.mult.impute?

I am using the rms and Hmisc packages to do a multiple imputation followed by a logistic regression model using lrm.
Everything works fine until I try to run the test for goodness of fit: residuals(type=c("gof"))
One needs to specify y=T and x=T in the fit. But I get a warning message when I do that with fit.multiple.impute.

a<-aregImpute(~med.hist.err+ med.discr+newLiving+No.drugs+Days.categ+Los+Age+Ward+Sex, n.impute=20, nk=0,data=med.err)
ddist<-datadist(Age,No.drugs,Days.categ, Sex, Living, Ward)
options(datadist="ddist")

fmi<-fit.mult.impute(med.hist.err~Age+No.drugs+Days.categ+Sex+Living+Ward, fitter=lrm, x=T, y=T,a,data=med.err)
Error in 1:n.impute : NA/NaN argument
In addition: Warning message:
In 1:n.impute : numerical expression has 18 elements: only the first used

It works to do the fit.mult.impute without x and y=T but then I get the following warning message when running residuals
gof<-residuals(fmi, type=c("gof"))
Error in residuals.lrm(fmi, type = c("gof")) :
  you did not specify y=T in the fit

It was no problem to do the goodness of fit test when I ran the lrm on my complete data set without multiple imputation and fit.mult.impute.
model.lrm<-lrm(med.hist.err~Age+No.drugs+Days +Sex+Living+Ward, x=TRUE, y=TRUE)
gof<-residuals(model.lrm, type=c("gof"))

Thanks
Lina
_________________
PhD student
Linnaeus University
Sweden




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