[R-sig-ME] [R] Residuals of mixed effects model

[ONKELINX, Thierry]

Dear Will,

residuals() should take both the fixed and random effects into account.
Can you give us a reproducible example if you get something different?

Use residuals(model, type = "normalized") if you also want to account
for the correlation structure.

What do you want to do with the residuals? Model them? In that case I
would suggest that you model the response variable directly. Note that
the parameter estimates of the random effects and the correlation
structure can (will) change if you add variables to the model.

Best regards,

Thierry

PS Use the mixed models list for this kind of questions about mixed
models.

------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek
team Biometrie & Kwaliteitszorg
Gaverstraat 4
9500 Geraardsbergen
Belgium

Research Institute for Nature and Forest
team Biometrics & 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-help-bounces at r-project.org 
> [mailto:r-help-bounces at r-project.org] Namens will.eagle at gmx.net
> Verzonden: donderdag 29 juli 2010 16:07
> Aan: r-help at r-project.org
> Onderwerp: [R] Residuals of mixed effects model
> 
> Dear all,
> 
> how do I get the residuals from a lme() output objects which 
> are adjusted for fixed AND (!) random effects?
> 
> I tried residuals(), but it seems they just give me the 
> residuals adjusted for the fixed effects of the regression model.
> 
> The model I use is:
> lme.out <- lme(data=MyDataInLongFormat,fixed= outcome~1, 
> random= ~ 1|individual, correlation=corSymm(form = ~time|individual))
> 
> Actually, I use only the intercept in the fixed part of the 
> predictor, and I want to get residuals which are adjusted for 
> the fixed part (intercept) and the random effect, ie to get 
> rid of the correlatedness of individual measures across time. 
> This way I want to get data where I can treat the measures 
> per time point as independent groups. Makes sense?
> 
> Thanks in advance,
> 
> Will
> 
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> 

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