Hi Ben,

You were spot on with your suggestion.  Thank you very much for your help.
I had mentioned before that I probably should keep random slopes within my
models since that is a primary interest of my study, regardless of whether
a LRT suggests they are needed.  Would you still recommend keeping them
within the model?

Jacob


On Mon, Sep 16, 2013 at 5:14 PM, Ben Bolker <bbolker@gmail.com> wrote:

> AvianResearchDivision <segerfan83@...> writes:
>
> >
> > Hi all,
> >
> > I have a very perplexing issue that I can't solve.  I am running a model
> > that resembles the following:
> >
> > model.lmer<-lmer(A~B*C+(B+0|Male)+(1|Male)
> >
> > A--continuous response variable
> > B--continuous, grand mean centered predictor variable
> > C--Factor with 11 levels
> > Male--Factor with 33 levels
> >
> > When I check coef(model.lmer), the results under column B (i.e. slope)
> are
> > the same across all 33 males.  This does not make any sense to me.  When
> I
> > change the response variable to anything else other than A, I see
> variation
> > in the slopes.  This goes for my 2012 data set only.  When I run my 2011
> > data using the same exact model from above, but referencing the 2011 data
> > frame, I see variation in slopes.  Finally, when I swap the data for C
> from
> > 2011 to C for 2012, I also see variation in slopes.
> >
> >  For whatever reason, I can't figure out what is going on with the 2012
> > data set.  I have checked and rechecked my data in excel.  I have also
> > exported the data as a .txt file and reimported it back into excel and
> that
> > hasn't fixed anything.  There is absolutely no reason that I can think of
> > for this to occur.  Any ideas?
>
>   I strongly suspect that the problem is that the estimated random
> effects variation in the slope of A with respect to B across Males is
> zero.  This happens with some regularity, especially for relatively
> small data sets, or complex models.  You're estimating a lot of
> variation in B in the fixed effects (interaction with the 11-level factor
> C), so it doesn't shock me that under some circumstances you get
> a zero estimate for sigma^2_(B|Male).
>
> You can easily test this explanation by looking at the summary(), or
> VarCorr(),
> to see if that variance component is zero (or very small).
>
>  Ben Bolker
>
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