[R] data distribution for lme

[Bert Gunter]

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 place
to hash it out, if anything meaningful can indeed be made of it.

Cheers,
Bert

On Tue, Dec 10, 2013 at 5:33 PM, Rolf Turner <r.turner at auckland.ac.nz> wrote:
>
> 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?



-- 

Bert Gunter
Genentech Nonclinical Biostatistics

(650) 467-7374