[R] boxcox alternative

[ONKELINX, Thierry]

Dear Michael.

The normality of _covariates_ is seldom relevant. The relevance of normality of the _response variable_ depends on the model assumptions. In case of linear models the only the **residuals** (and not the responses) are assumed to be normally
distributed.

Transformation of response or covariates can improve the fit of a model. In that case, knowledge about the link between covariates and response variables will help to pick relevant transformations.

Best regards,

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 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 Michael Haenlein
Verzonden: maandag 24 februari 2014 11:51
Aan: r-help at r-project.org
CC: Michael Haenlein
Onderwerp: [R] boxcox alternative

Dear all,

I am working with a set of variables that are very non-normally distributed. To improve the performance of my model, I'm currently applying a boxcox transformation to them. While this improves things, the performance is still not great.

So my question: Are there any alternatives to boxcox in R? I would need a model that estimates the "best" transformation automatically without input from the user since my approach should be flexible enough to deal with any kind of distribution. boxcox allows me to do this by picking the lambda that leads to the "best fit" but I wonder whether there are other options out there.

Thanks,

Michael


Michael Haenlein
Professor of Marketing
ESCP Europe
Paris, France

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