Cc to r-sig-mixed-models

Make some boxplots of the residuals per group and/or timepoint. One group with a higher variance can distort the qq-plot. Have a look at the example in http://www.r-bloggers.com/example-2014-3-allow-different-variances-by-group/

Adding a constant before log-transforming is not a good idea. The results will depend on the constant. If you must use a log-transformation: try to use ratios.

Best regards,

Thierry


ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium
+ 32 2 525 02 51
+ 32 54 43 61 85
Thierry.Onkelinx@inbo.be<mailto:Thierry.Onkelinx@inbo.be>
www.inbo.be<http://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

Van: Caroline Lustenberger [mailto:caroline.lustenberger@hotmail.com]
Verzonden: maandag 28 april 2014 13:42
Aan: ONKELINX, Thierry
Onderwerp: RE: [R] linear mixed model for non-normal negative and continous data

Dear Thierry

Thanks for your help. I now also posted this issue on R-Sig-mixed-models.

When plotting

plot(fitted(mod1), resid(mod1),
     ylim=(c(-abs(max(resid(mod1))), abs(max(resid(mod1))))))
abline(h=0)
lines(lowess(fitted(mod1), resid(mod1)), col="red")

the variance seems to be homogenous. Anyway, how could I add a variance function to deal with it? May you have a good example?
Furthermore, I also got the response that I could transform by adding 2 (because my lowest value is -1.91) and log-transform them. What do you think about that?

Thanks and all the best
Caroline




> From: Thierry.ONKELINX@inbo.be<mailto:Thierry.ONKELINX@inbo.be>
> To: caroline.lustenberger@hotmail.com<mailto:caroline.lustenberger@hotmail.com>; r-help@r-project.org<mailto:r-help@r-project.org>
> Subject: RE: [R] linear mixed model for non-normal negative and continous data
> Date: Mon, 28 Apr 2014 11:19:01 +0000
>
> Dear Caroline,
>
> Check the homogeneity of the variances. If they are inhomogeneous, you can add a variance function to deal with it. However, you will need to switch to the lme() from the nlme package.
>
> Best regards,
>
> Thierry
>
> PS R-Sig-mixed-models is a better list for this kind of questions.
>
> ir. Thierry Onkelinx
> Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest
> team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
> Kliniekstraat 25
> 1070 Anderlecht
> Belgium
> + 32 2 525 02 51
> + 32 54 43 61 85
> Thierry.Onkelinx@inbo.be<mailto:Thierry.Onkelinx@inbo.be>
> www.inbo.be<http://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@r-project.org<mailto:r-help-bounces@r-project.org> [mailto:r-help-bounces@r-project.org] Namens Caroline Lustenberger
> Verzonden: maandag 28 april 2014 12:04
> Aan: r-help@r-project.org<mailto:r-help@r-project.org>
> Onderwerp: [R] linear mixed model for non-normal negative and continous data
>
> Dear all
>
>
>
> I try to fit a linear mixed model to my data. In short, my dependent variable reflects changes of the bone level (Knmn, in mm), thus this variable is continous and provides negative values. I have two different groups (factor Group) that were measured 3 times each (thus repeated measures, factor Timepoint). I used the following model:
>
>
>
> mod_Knmn<-lmer(Knmn~Group*Timepoint+(1|VPnr),data=data)
>
>
>
> When performing a qq-plot my residuals are clearly deviant from the norm (long-tailed). Due to negative values I cannot perform classical transformation methods (e.g. log transformation). How could I proccede with this data. Is there a possibility to use a generalized linear model?
>
>
>
> Thanks and all the best
>
> Caroline
>
> [[alternative HTML version deleted]]
>
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The views expressed in this message and any annex are purely those of the writer and may not be regarded as stating an official position of INBO, as long as the message is not confirmed by a duly signed document.

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