[R-sig-ME] zero variance query

[Emmanuel Charpentier]

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
>