[R-sig-ME] Dealing with heteroscedasticity in repeated measure models

[Andrew Dolman]

Hi Manuel,

First of all a quick correction to your basic model specifications.

First model is correct:
model1 = lme(ipa ~ tempo, data=ipa, random = ~1 | localidad) # random intercept

Second model is wrong
model2 = lme(ipa ~ tempo, random = ~1 | tempo/localidad, data=ipa) #
random intercept and slope

should be
model2 = lme(ipa ~ tempo, random = ~tempo|localidad, data=ipa) #
random intercept and slope

Your 3rd model fits an autoregressive model for temporally correlated
data. Whether this deals with heteroscedasticity depends on the type
of heteroscedasticity .

model3 = lme(ipa ~ tempo, random = ~1 | localidad, data=ipa,
correlation=corAR1(form=~ tempo))



andydolman at gmail.com



On 26 September 2010 13:24, Manuel Spínola <mspinola10 at gmail.com> wrote:
>  Dear list members,
>
> I am fitting a repeated measure model using lme.
> I have 4 measurements of a rate (variable called ipa) measured each year
> (variable called tempo, which was centered) on 48 counties (all the counties
> from a province, variable called localidad).
> I am considering county like a random factor.
> My models are:
>
> model1 = lme(ipa ~ tempo, data=ipa, random = ~1 | localidad) # random
> intercept
>
> model2 = lme(ipa ~ tempo, random = ~1 | tempo/localidad, data=ipa) # random
> intercept and slope
>
> model3 = lme(ipa ~ tempo, random = ~1 | localidad, data=ipa,
> correlation=corAR1(form=~ tempo))
>
> I have heteroscedasticity.
> Is my last model dealing with heteroscedasticity?
> Thank you very much in advance.
> Best,
>
> Manuel
>
> --
> Manuel Spínola, Ph.D.
> Instituto Internacional en Conservación y Manejo de Vida Silvestre
> Universidad Nacional
> Apartado 1350-3000
> Heredia
> COSTA RICA
> mspinola at una.ac.cr
> mspinola10 at gmail.com
> Teléfono: (506) 2277-3598
> Fax: (506) 2237-7036
>
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