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

[Spencer Graves]

      The 'weights' argument on 'lm' is assumed to identify a vector of 
the same length as the response, giving numbers that are inversely 
proportional to the variance for each observation. 

      However, 'lm' provides no capability to estimate weights.  If you 
want to do that, the varFunc capabilities in the 'nlme' package is the 
best tool I know for that purpose. 

      If someone thinks there are better tools available for estimating 
heterscedasticity, I hope s/he will enlighten us both. 

      Hope this helps.
      Spencer Graves   

shirley zhang wrote:
> Thanks for Spencer and Simon's help.  I've got very interesting
> results based on your suggestions.
>
> One more question,  how to handle unequal variance problme in lm()?
> Isn't the weights option also, which means weighted least squares,
> right?  Can you give me an example of setting this parameter in lm()
> to account for  different variance assumption in each group?
>
> Thanks again,
> Shirley
>
>
> On 6/29/07, Spencer Graves <spencer.graves at pdf.com> wrote:
>> <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
>> >>>
>> >>> ______________________________________________
>> >>> R-help at stat.math.ethz.ch mailing list
>> >>> https://stat.ethz.ch/mailman/listinfo/r-help
>> >>> PLEASE do read the posting guide 
>> http://www.R-project.org/posting-guide.html
>> >>> and provide commented, minimal, self-contained, reproducible code.
>> >>>
>> >> --
>> >> 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.
>> >>
>> >>
>> >>
>> >
>> > ______________________________________________
>> > R-help at stat.math.ethz.ch mailing list
>> > https://stat.ethz.ch/mailman/listinfo/r-help
>> > PLEASE do read the posting guide 
>> http://www.R-project.org/posting-guide.html
>> > and provide commented, minimal, self-contained, reproducible code.
>> >
>>