I'm really grateful, =D

On 29 November 2011 11:15, Ben Bolker <bbolker@gmail.com> wrote:

> Ricardo Solar <rrsolar@...> writes:
>
> >
> > Hi Ben;
> >
> > It's exactly what I needed, the only (very simple indeed) question I have
> > is. In what case in lmer do I need to use:
> >
> > either: lmer(y~x*t+(1|f),data=D)
> >
> > or: lmer(y~x*t+(*x*|f),data=D)
> >
> > I mean, what is the effect of include another variable with the random
> > factor? I'd read it before, though I couldn't realise well what it
> means. I
> > saw that the significance drops down in the second case ...
> >
> > If you can clarify this issue all my problems are solved at least for
> now!
> > :p
>
>   You use (x|f) if you want a random-slope model; i.e., if your
> experimental
> design makes it reasonable to examine the variation in the effect of
> the covariate across fragments.  You can only do this if you have
> samples with varying x within a single fragment (e.g. a randomized
> block design).  (You may have low power to detect such variation
> even when it is theoretically possible to do so.)
>   For example, suppose that x was temperature, which varied among
> fragments and across years.
>
> x+t+(1|f): temperature has the same effect every year and in every fragment
> x*t+(1|f): temperature has different effects in every year, but the same
>           in every fragment
> x*t+(x|f): temperature has different effects in every year, and the
>           temperature effect varies across fragments
>
>
>
> >
> > Thanks a lot!
> >
> > On 29 November 2011 10:54, Ben Bolker <bbolker@...> wrote:
> >
> > > Ricardo Solar <rrsolar@...> writes:
> > >
> > > >
> > > > Hi everyone;
> > > >
> > > > I'm trying to analyse a dataset coming from samples of the same
> forest
> > > > fragments along 3 consecutive years. However, I'm trying to find a
> way to
> > > > consider in the analyses the temporal pseudo-replication in the
> data. Do
> > > > anyone have some idea about how can I solve this issue?
> > >
> > >   You haven't given us very much detail nor told us what you've tried
> > > so far, but I will suppose that you have a data frame D
> > > containing a normally distributed response
> > > variable y, a covariate x, a fragment factor f, and a year variable
> > > (factor) t (I will assume there is a single value of y measured
> > > per fragment per year.  Then the standard way to deal with this would
> be
> > >
> > >  lme(y~x*t,random=~1|f,data=D)
> > >
> > > or
> > >
> > >  lmer(y~x*t+(1|f),data=D)
> > >
> > >  You should treat year (t) as a fixed rather than a random effect
> > > because it won't be practical to estimate the variance of a random
> > > effect with only three levels.
> > >
> > >  Ben Bolker
> > >
> > > _______________________________________________
> > > R-sig-mixed-models@... mailing list
> > > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> > >
> >
>
> _______________________________________________
> R-sig-mixed-models@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>



-- 
==========================================
Ricardo Ribeiro de Castro Solar (Curriculo
Lattes<http://lattes.cnpq.br/9924177207371692>
)
MSc. em Entomologia - Doutorando - PPG Entomologia
Laboratório de Ecologia de Comunidades/Formigas <http://www.labecol.ufv.br>
(31) 3899-4018 - Contato Skype: rrsolar
Universidade Federal de Viçosa - MG
==========================================

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