[R-sig-ME] lme() vs aov()

[Reinhold Kliegl]

On Fri, May 23, 2008 at 12:09 PM, Federico Calboli
<f.calboli at imperial.ac.uk> wrote:
> Peter Dalgaard wrote:
>>
>> The gut reaction is that you shouldn't trust lme() in low-df cases, but in
>> this particular case the issue is different:
>>
>>  > summary(mod.aov)
>>
>> Error: source
>>         Df Sum Sq Mean Sq F value   Pr(>F)  drug       2 61.167  30.583
>>  61.167 0.003703 **
>> Residuals  3  1.500   0.500                   ---
>> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>>
>> Error: Within
>>         Df Sum Sq Mean Sq F value Pr(>F)
>> Residuals  6    9.0     1.5             Notice that the Residuals Mean Sq
>> is larger in the Within stratum than in the source stratum. In terms of a
>> mixed-effects model, this implies a negative estimate for the variance of
>> the source effect. lme() will have nothing of that and sets it to zero
>> instead. If you drop the Error(source) you get the same F as in lme()
>> although the df differ.
>>
>> (The  "negative variance" can be interpreted as negative within-source
>> correlation, but that only works properly for balanced designs. Long
>> story...)
>
> Thank you Peter for the explanation. I'm perfectly happy about this
> particular model, but I'd like to ask you (and everyone else who'd like to
> chime in), what do you mean with "you shouldn't trust lme() in low-df
> cases"? Why?
>
> (I ask because I often have low-df analyses to do).
>
> Regards,
>
> Federico
>

mcmcsamp(model) is one option.

Reinhold Kliegl