Dear Arams,
I would suggest to use lme() instead of lmer(), and then to use a variance function to model the
heteroscedasticity in the within-group errors, such as:
model.new=update(model,weights=varPower(form=~primary.covariate))
where model and model.new are lme fits, and primary.covariate is usually a numerical explanatory
variable.
Using varPower() as specified above when dealing with response variables that are proportions or
counts often helps in removing non-constant and/or non-normal errors.
See for example Fig. 5.2 on page 217 in Pinheiro and Bates.
Best wishes
Christoph
Bert Gunter schrieb:
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: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On
Behalf Of arams
Sent: Thursday, June 26, 2008 9:39 AM
To: r-help@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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