On Tue, Dec 10, 2013 at 7:33 PM, Rolf Turner r.tur...@auckland.ac.nzwrote:
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
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
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).
Cheers,
Bert
On Tue, Dec
No - it is assumed to be conditionally normal, that is, normal conditional
on the model. So you should be looking at the distributions of the
residuals rather than of the response variable, as an indicator for whether
or not the model assumptions are satisfied. Skewness in the residuals may
or
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
Thanks Rolf and Andrew. I was entirely too careless and should take a
trip to the woodshed (google David Stockman woodshed for the
reference).
The correct answer therefore is: maybe for the residuals, for the
right model, of course.
But I still think the crowd on r-sig-mixed-models is the right
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