Hello
I am not really a statistic person, so it's possible i did something completely
wrong... if this is the case: sorry...
I try to get the best GLM model (with the lowest AIC) for my dataset.
Therefore I run a stepAIC (in the "MASS" package) for my GLM allowing only
two-variable-interactions.
For the output (summary) I got a model with 7 (of 8) variabels and 5
interactions and AIC=40.008
BUT: When I take this model and reduce stepwise further variables manually
(starting with the one with the highest p-values and first reducing all
interactions of a variable before i reduce the variable itself) until i can't
reduce more variables since all (or its interaction) have a p-value < 0.1, I
get a model with 4 variables and 2 interactions and an AIC of 33.879
So my questions: Why didn't the stepAIC give me the model with AIC=33.879?
And which model should I think of as the best?
For my calculations I used these formulae:
gm1<-glm(cpi~time+tank+...,data=d1)
gm2<-stepAIC(gm1)
summary(gm2)
#to get the "best" model -> AIC=40.008
#afterwards I reduced manually using the formula:
summary(glm(cpi~time+tank+...,data=d1))
giving me a model with AIC=33.879
Hope you understand what I did, and that you can help me.
Thanks
Florian
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