Hi,
I have a database of 2211 rows with 31 entries each and I manually split my
data into 10 folds for cross validation. I build logistic regression model
as:
>model <- glm(qual ~ AgGr + FaHx + PrHx + PrSr + PaLp + SvD + IndExam +
Rad +BrDn + BRDS + PrinFin+ SkRtr + NpRtr + SkThck +TrThkc +
SkLes + AxAdnp + ArcDst + MaDen + CaDt + MaMG +
MaMrp + MaSh + SCTub + SCFoc + MaSz,
family=binomial(link=logit));
Where the variables are taken from the trainSet of size 1989x31. The test
set is sized 222x31. Now my question is when I try to predict on the test
set it gives me the error:
> predict.glm(model, testSet, type ="response")
"Error in drop(X[, piv, drop = FALSE] %*% beta[piv]) :
subscript out of bounds"
It does fine on trainSet. so it is something about the testSet. On the other
hand, I realized that some independent variables say "MaSz" takes 3
different values in the trainset vs. 4 different ones in the testSet. I am
not sure if this is the cause.If so, what would be the remedy?
Since I can retrieve the coefficients of the logistic regression, I could
manually calculate response for each entry in the testSet. This could solve
my problem although burdensome. But, I don't know an easy way of doing it as
my logistic regression have 80+ coefficients.
Could somebody advise?
Thanks,
M
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