Dear all,

I think I have a rather strange question, but I'd like to give it a try:

I want to perform a simulation numerous times, thats why I can't do it by
hand. I sample a small dataset from a very large one, and use backward
selection to select significant predictors for some arbitrary outcome
variable Y. These predictors are to be placed in a model, and regression
coefficients estimated in a new small dataset.

This is what I got:

#First I sample a small dataset from the large one, lrm is logistic
regression from the Design package (as is the command fastbw)

fsubset=lrm(Ysub~X1sub+X2sub+X3sub+X4sub, data=dsubset1)
variables[i]=as.vector(fastbw(fsubset, rule="p", type="individual",
sls=0.5)[2])
variables=unlist(variables[i])

#So my "variables" are the significant ones.
#Below is the sampling of the testset, in which I want to estimate a model,
but the only predictors in the model should be the ones I found to be
significant.

dderiveset=sample(patnr, 50, replace=FALSE)
dderiveset=d[dderiveset,]
colnames(dderiveset)=c("pat", "X1d", "X2d", "X3d", "X4d", "Yd")
attach(dderiveset)
#Now in this new set of data, I want to build a model, but only using the
coefficients that were significant #in the fsubset model, thus from
"variables" I've tried everything and anything, even building logical
expression within the model, but this was not accepted.

Best regards,

Sander van Kuijk

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