[R-sig-ME] LMER problem when all observations in one level are zero?

[ONKELINX, Thierry]

Dear Sarah,

The problem might be due to the perfect, negative correlation between
some of the fixed effects. Refitting your model without intercept might
reduce that effect.

Also note that the standard errors of the fixed effects are huge.

Best regards,

Thierry

------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek
team Biometrie & Kwaliteitszorg
Gaverstraat 4
9500 Geraardsbergen
Belgium

Research Institute for Nature and Forest
team Biometrics & 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: r-sig-mixed-models-bounces at r-project.org 
> [mailto:r-sig-mixed-models-bounces at r-project.org] Namens Sarah Reilly
> Verzonden: maandag 29 november 2010 8:04
> Aan: r-sig-mixed-models at r-project.org
> Onderwerp: [R-sig-ME] LMER problem when all observations in 
> one level are zero?
> 
> Hello,
> 
> We are analyzing data on the number of insects of certain 
> species on individual plants.  There are 3 species of plants 
> (fixed effect) nested within 8 different sites (random 
> effect).  We have structured the model as follows, after 
> tentatively dropping the intercept of site:
> 
> >lmer(insects ~ plantsp + (0+ plantsp|site) , family = 
> poisson, REML = 
> >TRUE
> )
> 
> We then went on to do contrasts between levels of the fixed effect:
> 
> >glht(bestmod, linfct = mcp(plantsp = "Tukey"))
> 
> This seems to work ok for some contrasts, but not others.  In 
> particular, sometimes we have the situation where an insect 
> was not found at all on one particular plant species (hence 
> all observations are 0 for that level of the
> fixed effect).   This returns the unsatisfactory result that 
> the level with
> the highest mean is different from the level with the 
> intermediate mean, but not at all different from the level 
> with the low (i.e. 0) mean.  Furthermore, adding a single 
> insect to a random plant within the all 0s group makes all 
> the contrasts highly significant.  The data are overdispersed 
> and zero-inflated but that doesn't seem to be responsible for 
> this particular problem.  We may end up using MCMCglmm but we 
> still would like to understand why this is happening in LMER.
> 
> Thanks for your assistance,
> 
> Sarah
> 
> Here are the results from a test where this happens:
> 
> Generalized linear mixed model fit by the Laplace approximation
> 
> Formula: insects ~ plantsp + (0 + plantsp | site)
> 
>   AIC  BIC logLik deviance
> 
>  1002 1042 -491.9    983.9
> 
> Random effects:
> 
>  Groups Name      Variance Std.Dev. Corr
> 
>  site   plantspTA 0.13088  0.36177
> 
>         plantspTG 1.81646  1.34776  0.000
> 
>         plantspTL 1.62193  1.27355  0.000 0.325
> 
> Number of obs: 626, groups: site, 8
> 
> Fixed effects:
> 
>             Estimate Std. Error z value Pr(>|z|)
> 
> (Intercept)   -18.71     907.10  -0.021    0.984
> 
> plantspTG      16.97     907.10   0.019    0.985
> 
> plantspTL      19.10     907.10   0.021    0.983
> 
> Correlation of Fixed Effects:
> 
>           (Intr) plntTG
> 
> plantspTG -1.000
> 
> plantspTL -1.000  1.000
> 
> 
> 
> Simultaneous Tests for General Linear Hypotheses
> 
> Multiple Comparisons of Means: Tukey Contrasts
> 
> Fit: glmer(formula = insects ~ plantsp + (0 + plantsp | 
> site), family = poisson,
> 
>     REML = TRUE)
> 
> Linear Hypotheses:
> 
>              Estimate Std. Error z value Pr(>|z|)
> 
> TG - TA == 0  16.9673   907.1004   0.019 0.999777
> 
> TL - TA == 0  19.1032   907.1003   0.021 0.999718
> 
> TL - TG == 0   2.1358     0.6068   3.520 0.000864 ***
> 
> ---
> 
> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> 
> (Adjusted p values reported -- single-step method)
> 
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> 
>