Re: [R] lmer model with continuos non normal response variable, transformation needed?

From: Bert Gunter <gunter.berton_at_gene.com>
Date: Thu, 26 Jun 2008 11:12:58 -0700

If I understand you correctly, then to paraphrase what Brian Ripley has stated in recent posts, it is not the (possibly transformed) response that you want to be normal, but rather the error distributions. Your response presumably contains systematic variation due to your covariates (your model). So using the K-S test as I think you describe is nonsense.

I suggest you forget about testing for normality, transform your data "sensibly" (which is quite often not at all, even for proportions or counts), fit your model, and see what you get. If you're still hung up on distributional assumptions, check residual plots. Distributional assumptions are often most critical for inference, which for glmm's is problematic anyway, due to the crudeness of the asymptotic approximations (paraphrasing Doug Bates, now). They may or may not have a large impact on estimation, which is generally the greatest concern. Sensitivity analyses are a way to examine this.

Cheers,
Bert Gunter
Genentech Nonclinical Statistics

-----Original Message-----
From: r-help-bounces_at_r-project.org [mailto:r-help-bounces_at_r-project.org] On Behalf Of arams
Sent: Thursday, June 26, 2008 9:39 AM
To: r-help_at_r-project.org
Subject: [R] lmer model with continuos non normal response variable, transformation needed?

Hi.

I want to do an lmer model but have doubts of what family I should use. My response variable was originally a proportion, however I standarized it for each year of data collection (20 in total). After standarizing it I checked for normality with the Kolmogorov-Smirnov test, and it turns out it is not normal. It ranges from -3 to 4. Since it is no longer a proportion I can't use a binomial distribution nor a normal distribution. I'm guessing I have to transform it, but this is a variable
that has already been standarized. Anny suggestions? Thank you.

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Received on Thu 26 Jun 2008 - 18:16:16 GMT

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