[R-sig-ME] heteroscedascity in fixed factor

[ONKELINX, Thierry]

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

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

HTH,

Thierry

------------------------------------------------------------------------
----
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
Belgium 
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be 
www.inbo.be 

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey

-----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

Hi,



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




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



treatment is a categorical variable.  field is included as a random
variable
because the management history of each field might affect the outcome of
the
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
effect
of treatment is due to differences in variances rather than differences
in
means?  Is there any way around this?



Thanks for your help Megan Douglas

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