data distribution for lme
See inline below.
On 12/11/13 11:28, Bert Gunter wrote:
This is not really an R question -- it is statistics. In any case, you should do better posting this on the R-Sig-Mixed-Models list, which concerns itself with matters like this. However, I'll hazard a guess at an answer: maybe. (Vague questions elicit vague answers).
No! Nay! Never! Well, hardly ever. The ***y*** values will rarely be
Gaussian.
(Think about a simple one-way anova, with 3 levels, and N(0,sigma^2) errors.
The y values will have a distribution which is a mixture of 3
independent Gaussian
distributions.)
You *may* wish to worry about whether the ***errors*** have a Gaussian
distribution. Some inferential results depend on this, but in many cases
these results are quite robust to non-Gaussianity.
There. I have exhausted my knowledge of the subject.
cheers,
Rolf
Cheers, Bert On Tue, Dec 10, 2013 at 6:55 AM, peyman <zirak.p at gmail.com> wrote:
Hi folks, I am using the lme package of R, and am wondering if it is assumed that the dependent factor (what we fit for; y in many relevant texts) has to have a normal Gaussian distribution? Is there any margins where some skewness in the data is accepted and how within R itself one could check distribution of the data?