[R-sig-ME] heteroscedascity in fixed factor

ONKELINX, Thierry Thierry.ONKELINX at inbo.be
Wed Apr 8 11:14:21 CEST 2009

Have a look at the varClas options. In your case adding weights =
varIdent(~treatment) models the heteroscedasticity along the levels of

lme(crop.height~treatment, random= ~1|field, height, weights =



ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be 

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-----Oorspronkelijk bericht-----
Van: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] Namens Jude Phillips
Verzonden: dinsdag 7 april 2009 21:24
Aan: r-sig-mixed-models at r-project.org
Onderwerp: [R-sig-ME] heteroscedascity in fixed factor


I'm trying to fit a simple mixed effects model to data on a crop

height.lme1<-lme(crop.height~treatment, random= ~1|field, height)

treatment is a categorical variable.  field is included as a random
because the management history of each field might affect the outcome of
trials.  Also the observations are unbalanced; different numbers of
measurements were recorded in each field.  treatment has a significant
effect on crop.height

The problem I'm having is this; crop.height is not homoscedastic with
respect to treatment.  Therefore, is it possible that the significant
of treatment is due to differences in variances rather than differences
means?  Is there any way around this?

Thanks for your help Megan Douglas

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