I'm working with a logistic regression model in r.
model <- glm(response~., family="binomial", data)
and I'm using
DescTools::PseudoR2(model, which="Nagelkerke") to get an estimate of model fit.
My dataset has missing values, so if I want to do stepwise selection I have to remove these missing values using
na.omit(), but doing so drops 37 out of 187 rows of data.
data2 <- na.omit(data) model2 <- glm(response~., family="binomial", data2)
Comparing the two models before using stepwise, I noticed a significant drop in my pseudo $R^2$ value:
DescTools::PseudoR2(model, which="Nagelkerke") # 0.6496515 DescTools::PseudoR2(model2, which="Nagelkerke") # 0.4652934
After using model 2 to run a backward step based on AIC, my pseudo $R^2$ drops to around .35, which makes sense with fewer variables.
I'm wondering which $R^2$ result I should trust when presenting the model?
Could I present the $R^2$ from model 1 and build a model with the variables kept from the step model, but use the non-NA removed data or is this an inflated and disingenuous estimate?