Hi Bruno,

probably not exactly what you are looking for, but maybe "all subset
regression" as in library "leaps" might be an alternative for variable
selection? But I am definitely not sure if this is faster than drop1()
(calculates more models), nor have I ever tested it with a hierarchical
logistic regression model.

Another option might be stepAIC() in MASS library, which is capable of
forward, backward and stepwise model selection. So it is add() and
drop() in one function.

Hope this might help a little.
Greetings from Munich, Felix


Am 02.11.12 10:49, schrieb brunosm:
> Hi,
>
> I'm trying to build a hierarchical logistic regression model with lme4
> package, but I have a problem on selecting the variables to include in this
> model.
>
> In a simple logistic regression, using Forward selection, i use a likelihood
> ratio test to check which variables i should include in the model, using the
> function add1().
>
> The problem is that this function doesn't work with the hierarchical model
> that i'm trying to achieve.
>
> Example:
>
> model<- glmer(y ~ (1 | group)+x+sex+age, family = binomial("logit"), data =
> db)
> add1(model, db, test="Chisq")
> Error: $ operator not defined for this S4 class
>
> I know that the drop1() function works fine to do a backward selection, but
> the problem is that i have 40 variables, and which time i use drop1() i have
> to wait a loooooong time to get a result...
>
> Is there any alternative to add1()? I mean, i don't want to use
> anova(model1,model2) because i would have to do all the models by hand...
>
>
> Thanks a lot guys,
>
> Bruno
>
>
>
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