Also, how about:

library(languageR)
pvals.fnc(model2)

Cheers,
Steve


On Mon, Mar 25, 2013 at 10:44 AM, lborger <lborger@cebc.cnrs.fr> wrote:

> Hello,
>
> would you be happy with the value provided by:
>
> anova(model1, model2)
>
> maybe see also http://markmail.org/thread/ufjpzawdsrnyjcm3
>
>
> Cheers,
> Luca
> -----Original Message-----
> From: Brad Buran <bburan@galenea.com>
> To: "r-sig-mixed-models@r-project.org" <r-sig-mixed-models@r-project.org>
> Date: Mon, 25 Mar 2013 14:11:19 +0000
> Subject: [R-sig-ME] determining whether factor is significant in linear
> mixed models
>
>
> Hello all:
>
> I'm having some difficulty interpreting the statistical significance of a
> factor in my mixed model and am hoping for some guidance.  In our
> experiments, we collect a certain measure (e.g. the distance the subject
> travels) over multiple sessions.  The subjects can be grouped by genotype
> (e.g. whether or not they contain a certain gene).   Here, we are
> interested
> in determining whether subjects with a certain gene typically travel a
> shorter or longer distance than those without a copy of that gene.
>
> When determining the appropriate model to fit to the data, typically one
> creates multiple models (e.g. one with genotype as a factor and one without
> genotype as a factor) then determines with model is, statistically, a
> better
> fit to the data.  For example:
>
> model1 = lmer(measure~session+(1|subject_id), data=data, REML=FALSE)
> model2 = lmer(measure~session+genotype+(1|subject_id), data=data,
> REML=FALSE)
> model3 = lmer(measure~session+genotype+(session|subject_id), data=data,
> REML=FALSE)
> print(anova(model1, model2, model3))
>
> Let's assume that the anova reveals that model2 (i.e. incorporating
> genotype
> as a factor) is the best fit.  However, if one runs the Anova function,
> post-hoc, on model2, it reveals that the p-value for the genotype factor is
> ~0.15:
>
> library('car')
> print(Anova(model2))
>
> This seems somewhat counter-intuitive.  I have read Douglas Bates' "lmer,
> p-values and all that" post.  Is the correct take-home message that I
> should
> ignore the result of "Anova(model2)" because it uses assumptions that are
> unreliable?  If I wish to publish the results of my analysis, would it be
> sufficient to report that genotype is a significant factor based on the
> fact
> that model2 (which incorporates genotype) is a better fit than model1?
>
> Thanks,
> Brad
>
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