Dear all,
I have a question regarding the use of stepAIC and polynomial (quadratic to be
specific) terms in a binary logistic regression model. I read in McCullagh and
Nelder, (1989, p 89) and as far as I remember from my statistics cources,
higher-degree polynomial effects should not be included without the main
effects. If I understand this correctly, following a stepwise model selection
based on AIC should not lead to a model where the main effect of some
continuous covariate is removed, but the quadratic term is kept.
The question is, should I keep the quadratic term (note, there are other main
effects that were retained following the stepwise algorithm) in the final model
or should I delete it as well and move on? Or should I retain the main effect
as well?
To picture it, the initial model to which I called stepAIC is:
Call: glm(formula = S ~ FR + Date * age + I(age^2), family =
logexposure(ExposureDays = DATA$int), data = DATA)
and the final one:
Call: glm(formula = S ~ FR + Date + I(age^2), family =
logexposure(ExposureDays = DATA$int), data = DATA)
Thanks very much in advance for your thoughts and suggestions,
Caspar
Caspar Hallmann
MSc Student WUR
The Netherlands
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