[R-sig-ME] Modelling random effects with SITE, YEAR and SPECIES

[ONKELINX, Thierry]

Dear Kate,

The error you get with A*SPECIES indicates that you have to few data.
Probabily because not all the combination of A and SPECIES exist in your
dataset. Which was what I feared would happen.

As I meantioned before, adding A as a random slope to the random effect
will give you opnly info on the variability of A between the different
species, but not estimates per species. That's the difference between
random effects and mixed effects.

If the info per species is that important, then I would suggest to build
a model for each species.


HTH,

Thierry

------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium 
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be 
www.inbo.be 

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey

-----Oorspronkelijk bericht-----
Van: CL Pressland [mailto:Kate.Pressland at bristol.ac.uk] 
Verzonden: maandag 11 mei 2009 17:53
Aan: ONKELINX, Thierry; R Mixed Models
Onderwerp: RE: [R-sig-ME] Modelling random effects with SITE, YEAR and
SPECIES

Theirry,

thank you for you informative reply. I have had a go at your suggestions

but have been stumped:

--On 07 May 2009 10:16 +0200 "ONKELINX, Thierry"
<Thierry.ONKELINX at inbo.be> 
wrote:

> Dear Kate,
>
> Adding SPECIES as a random effect indicates that you want to take the
> effect of SPECIES into account but not need to know the effect of the
> individual SPECIES. If you do want to know that effect then you have
to
> add species to fixed effects. Examining the effect of A, B and C on
> species (as a fixed effect) requires interactions between them. The
> model then looks like (A + B + C) * SPECIES + Year + (1|SITE) +
(1|YEAR)
> This will only work if you have sufficiend data.

I tried this approach with data I have that is SPECIES recorded as SITES

over YEARS but when I tried A*SPECIES as a fixed factor I received this 
error message:

"Error in mer_finalize(ans) : Downdated X'X is not positive definite,
88."

I've searched for what this error means but I cannot understand it.

This was written by Douglas Bates in response to [Re: [R] lme4, error in

mer_finalize(ans)] posted 05 Dec 2008:
"That, admittedly obscure, error message relates to the fixed-effects 
specification rt ~ length + length:pos being rank deficient. If you look
at 
the summary of the linear model fit you will see that there are 3 
coefficients that are not determined because of singularities. The lm 
function detects the singularities and fits a lower-rank model.  The
lmer 
function is not as sophisticated. It just detects the singularities and 
quits."

I am unsure what this means or how it translates to my data. In my
example, 
I have 78 "SPECIES" (factor, coded as numbers) and "A" is ordered data
0, 
1, 2. The y variable is number/m. You wrote that this would only work is

you had sufficient data - each species is not recorded each time, so is 
this reduced data the cause i.e. not enough observations for n?

> Another option is to keep species as a random effect and add random
> slopes according to A, B and C. This will allow a different effect of
A,
> B anc C for each species. The model would look like A + B + C + Year +
> (1|SITE) + (1|YEAR) + (A + B + C|SPECIES)

I have tried this way also but I am unsure of the output - it does not
give 
species specific information and therefore I cannot work out which
species 
is more affected by A, only if SPECIES as a whole are affected or not by

each category of A. This is not useful to me as I would like to
determine, 
given the random effects, if A 0, 1, or 2 affect which species in the
data 
set.

Any thoughts?

Kate
>
> HTH,
>
> Thierry
>
>
------------------------------------------------------------------------
> ----
> ir. Thierry Onkelinx
> Instituut voor natuur- en bosonderzoek / Research Institute for Nature
> and Forest
> Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
> methodology and quality assurance
> Gaverstraat 4
> 9500 Geraardsbergen
> Belgium
> tel. + 32 54/436 185
> Thierry.Onkelinx at inbo.be
> www.inbo.be
>
> To call in the statistician after the experiment is done may be no
more
> than asking him to perform a post-mortem examination: he may be able
to
> say what the experiment died of.
> ~ Sir Ronald Aylmer Fisher
>
> The plural of anecdote is not data.
> ~ Roger Brinner
>
> The combination of some data and an aching desire for an answer does
not
> ensure that a reasonable answer can be extracted from a given body of
> data.
> ~ John Tukey
>

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