[R-sig-ME] zero variance query
Christine Griffiths
Christine.Griffiths at bristol.ac.uk
Mon Jun 1 19:00:20 CEST 2009
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?
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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