R help - Jun 2009 - Insignificant variable improves AIC (multinom)?

Hi,

I am trying to specify a multinomial logit model using the multinom function
from the nnet package. Now I add another independent variable and it halves the
AIC as given by summary(multinom()). But when I call Anova(multinom()) from the
car package, it tells me that this added variable is insignificant
(Pr(>Chisq)=0.39). Thus, the improved AIC suggests to keep the variable but
the Anova suggests to drop it.

I am sure this is due to my lack of understanding of these models but could
someone help me out with a pointer what my mistake is?

Thanks so much,
  Werner

Werner Wernersen <pensterfuzzer at yahoo.de> wrote
>Hi,
>
>I am trying to specify a multinomial logit model using the multinom function
from the nnet package. Now I add another independent variable and it halves the
AIC as given by summary(multinom()). But when I call Anova(multinom()) from the
car package, it tells me that this added variable is insignificant
(Pr(>Chisq)=0.39). Thus, the improved AIC suggests to keep the variable but
the Anova suggests to drop it.
>
>I am sure this is due to my lack of understanding of these models but could
someone help me out with a pointer what my mistake is?

I am not sure why you  would expect the same answer from AIC and p-value.  They
are different questions.  AIC attempts to answer a question about overall model
fit.  p-value for a particular variable attempts to answer whether that
particular coefficient could be due to chance if the population value of the
parameter was 0.

One way these could give different answers is if the new variable affected the
parameter estimates for the other parameters.

It's yet another exemplar of the problems with using p-values for model
selection

HTH

Peter

Peter L. Flom, PhD
Statistical Consultant
www DOT peterflomconsulting DOT com