[R] unequal variance assumption for lme (mixed effect model)

[Spencer Graves]

<comments in line>

shirley zhang wrote:
> Hi Simon,
>
> Thanks for your reply. Your reply reminds me that book. I've read it
> long time ago, but haven't  try the weights option in my projects
> yet:)
>
> Is the heteroscedastic test always less powerful because we have to
> estimate the within group variance from the given data?
>   
SG:  In general, I suspect we generally lose power when we estimate more 
parameters. 

SG:  You can check this using the 'simulate.lme' function, whose use is 
illustrated in the seminal work reported in sect. 2.4 of Pinheiro and 
Bates (2000) Mixed-Effects Models in S and S-Plus (Springer). 
> Should we check whether each group has equal variance before using
> weights=varIdent()? If we should, what is the function for linear
> mixed model?
>   
SG:  The general advice I've seen is to avoid excessive 
overparameterization of heterscedasticity and correlations.  However, 
parsimonious correlation had heterscedasticity models would likely be 
wise.  Years ago, George Box expressed concern about people worrying too 
much about outliers, which are often fairly obvious and relatively easy 
to detect, while they worried too little, he thought, about dependence, 
especially serial dependence, which is generally more difficult to 
detect and creates bigger problems in inference than outliers.  He 
wrote, "Why worry about mice when there are tigers about?" 

SG:  Issues of this type can be fairly easily evaluated using 
'simulate.lme'. 

      Hope this helps. 
      Spencer Graves
> Thanks,
> Shirley
>
> On 6/27/07, Simon Blomberg <s.blomberg1 at uq.edu.au> wrote:
>   
>> The default settings for lme do assume equal variances within groups.
>> You can change that by using the various varClasses. see ?varClasses. A
>> simple example would be to allow unequal variances across groups. So if
>> your call to lme was:
>>
>> lme(...,random=~1|group,...)
>>
>> then to allow each group to have its own variance, use:
>>
>> lme(...,random=~1|group, weights=varIdent(form=~1|group),...)
>>
>> You really really should read Pinheiro & Bates (2000). It's all there.
>>
>> HTH,
>>
>> Simon.
>>
>> , On Wed, 2007-06-27 at 21:55 -0400, shirley zhang wrote:
>>     
>>> Dear Douglas and R-help,
>>>
>>> Does lme assume normal distribution AND equal variance among groups
>>> like anova() does? If it does, is there any method like unequal
>>> variance T-test (Welch T) in lme when each group has unequal variance
>>> in my data?
>>>
>>> Thanks,
>>> Shirley
>>>
>>> ______________________________________________
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>>>       
>> --
>> Simon Blomberg, BSc (Hons), PhD, MAppStat.
>> Lecturer and Consultant Statistician
>> Faculty of Biological and Chemical Sciences
>> The University of Queensland
>> St. Lucia Queensland 4072
>> Australia
>>
>> Room 320, Goddard Building (8)
>> T: +61 7 3365 2506
>> email: S.Blomberg1_at_uq.edu.au
>>
>> The combination of some data and an aching desire for
>> an answer does not ensure that a reasonable answer can
>> be extracted from a given body of data. - John Tukey.
>>
>>
>>     
>
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