[R-sig-ME] zero variance query
Emmanuel Charpentier
charpent at bacbuc.dyndns.org
Tue Jun 2 01:07:37 CEST 2009
Le lundi 01 juin 2009 à 18:00 +0100, Christine Griffiths a écrit :
> Dear R users,
>
> I am having a problem with getting zero variance in my lmer models which
> specify two random effects. Having scoured the help lists, I have read that
> this could be because my variables are strongly correlated. However, when I
> simplify my model I still encounter the same problem.
>
> My response variable is abundance which ranges from 0-0.14.
>
> Below is an example of my model:
> > m1<-lmer(Abundance~Treatment+(1|Month)+(1|Block),family=quasipoisson)
> > summary(m1)
> Generalized linear mixed model fit by the Laplace approximation
> Formula: Abundance ~ Treatment + (1 | Month) + (1 | Block)
> AIC BIC logLik deviance
> 17.55 36.00 -2.777 5.554
> Random effects:
> Groups Name Variance Std.Dev.
> Month (Intercept) 5.1704e-17 7.1906e-09
> Block (Intercept) 0.0000e+00 0.0000e+00
> Residual 1.0695e-03 3.2704e-02
> Number of obs: 160, groups: Month, 10; Block, 6
>
> Fixed effects:
> Estimate Std. Error t value
> (Intercept) -3.73144 0.02728 -136.80
> Treatment2.Radiata 0.58779 0.03521 16.69
> Treatment3.Aldabra 0.47269 0.03606 13.11
>
> Correlation of Fixed Effects:
> (Intr) Trt2.R
> Trtmnt2.Rdt -0.775
> Trtmnt3.Ald -0.756 0.586
>
> 1. Is it wrong to treat this as count data?
Hmmm... IST vaguely R that, when the world was young and I was (already)
silly, Poisson distribution used to be a *discrete* distribution. Of
course, this may or may not stand for "quasi"Poisson (for some value of
"quasi").
May I inquire if you tried to analyze log(Abundance) (or log(Count),
maybe including log(area) in the model) ?
HTH,
Emmanuel Charpentier
> 2. I would like to retain these as random factors given that I designed my
> experiment as a randomised block design and repeated measures, albeit
> non-orthogonal and unbalanced. Is it acceptable to retain these random
> factors, is all else is correct?
> 3. The above response variable was calculated per m2 by dividing the Count
> by the sample area. When I used the Count (range 0-9) as my response
> variable, I get a small but reasonable variation of random effects. Could
> anyone explain why this occurs and whether one response variable is better
> than another?
>
> > m2<-lmer(Count~Treatment+(1|Month)+(1|Block),family=quasipoisson)
> > summary(m2)
> Generalized linear mixed model fit by the Laplace approximation
> Formula: Count ~ Treatment + (1 | Month) + (1 | Block)
> AIC BIC logLik deviance
> 312.5 331 -150.3 300.5
> Random effects:
> Groups Name Variance Std.Dev.
> Month (Intercept) 0.14591 0.38198
> Block (Intercept) 0.58690 0.76609
> Residual 2.79816 1.67277
> Number of obs: 160, groups: Month, 10; Block, 6
>
> Fixed effects:
> Estimate Std. Error t value
> (Intercept) 0.3098 0.3799 0.8155
> Treatment2.Radiata 0.5879 0.2299 2.5575
> Treatment3.Aldabra 0.5745 0.2382 2.4117
>
> Correlation of Fixed Effects:
> (Intr) Trt2.R
> Trtmnt2.Rdt -0.347
> Trtmnt3.Ald -0.348 0.536
>
> Many thanks,
> Christine
>
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