AFAIK the COVTEST option just computes a Wald test! Since you want 
test for variance components (which is on the boundary of the 
parameter space), I'd suggest to use a LRT (i.e., anova.lme(model.1, 
model.2)) and moreover consider the simulate.lme() function of the 
"nlme" package.


Best,
Dimitris

----
Dimitris Rizopoulos
Ph.D. Student
Biostatistical Centre
School of Public Health
Catholic University of Leuven

Address: Kapucijnenvoer 35, Leuven, Belgium
Tel: +32/16/336899
Fax: +32/16/337015
Web: http://www.med.kuleuven.be/biostat/
     http://www.student.kuleuven.ac.be/~m0390867/dimitris.htm


----- Original Message ----- 
From: "Alfonso M Sanchez-Lafuente" <[EMAIL PROTECTED]>
To: <[email protected]>
Sent: Tuesday, June 21, 2005 9:57 AM
Subject: [R] Another Mix Model Question


> Hi again,
>
> thank you for your previous answers. Just another question, though 
> ...
>
> I get the following variance components after fitting a mixed model.
>
>
> Groups   Name              Variance Std.Dev. Corr
> PlantID  TreatmCtrl        0.51784  0.71961
>          TreatmNoAccess    4.77469  2.18511  -0.063
>          TreatmNoKeel      4.22726  2.05603   0.513  0.751
>          TreatmNoSpur      0.45918  0.67763   0.158  0.303  0.319
>          TreatmNoStand     3.45357  1.85838  -0.736 -0.070 -0.435 
> 0.495
> PlantID  PollClassApis     1.12364  1.06002
>          PollClassBombAnth 0.42769  0.65398  -0.759
> Residual                   3.09669  1.75974
>
> My question is: if n random effects are included in a model, how can 
> I
> test the hypothesis that the variance of such effects is 0 ?
>
> Some sort of COVTEST option in Proc MIXED in SAS (sorry, SAS is 
> still
> more familar to me than R).
>
> -- 
>
> ----------------------------------------------
> Alfonso M. Sanchez-Lafuente
> Departamento de Biologia Vegetal y Ecologia
> Facultad de Biologia
> Universidad de Sevilla
> Avd. Reina Mercedes 9
> E-41012, Sevilla, Spain
> email: [EMAIL PROTECTED] / [EMAIL PROTECTED]
>
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