Re: [R] testing the significance of the variance components using lme

From: Marco Geraci <>
Date: Fri 17 Feb 2006 - 07:01:32 EST

you should say a little more about the data you have. I guess you refer to longitudinal data. I say so because if you deal with spatial smoothing splines in form of mixed models, the answer to your question would be different. Anyway, a good starting point is given by the commands

fit.lme <- lme(...) # fitted model
fit.lme$apVar # approx. covariace matrix for the variance-covariance coefficients (see ?lmeObject) intervals(fit.lme) # confidence intervals for the parameter(s)

I believe that Pinhiero and Bates (2000) Mixed-Effects Models in S and S-Plus (Springer) includes some answers to your questions. I don't really know what happens when you use 'intervals' and the caveats of this command. When it comes to making inference about the variance components I tend to be suspicious. I hope some R users can give you a more complete answer than mine.

Testing whether or not a variance component is zero is a delicate issue. Check:
- Self and Liang (1987), Asymptotic properties of maximum likelihood estimators and likelihood ratio tests under nonstandard conditions. Journal of the American Statistical Association, 82, 605-610 - Zhang and Lin (2003), Hypothesis testing in semiparametric additive mixed models. Biometrika, 4, 57-74
- Bottai (2003), Confidence regions when the Fisher information is zero. Biometrika, 90, 73-84.

hope this helps a little


> Hi R-users,
> I am using lme to fit a linear mixed model with the
> nlme package,
> does anyone know if it is possible to obtain
> standard error estimates of the variance components
> estimators and an adequate method to test the
> significance of the variance component?
> Thanks,
> Berta.
> [[alternative HTML version deleted]]
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> mailing list PLEASE do read the posting guide! Received on Fri Feb 17 07:47:02 2006

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