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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