[R-sig-ME] comparing treatment with different skewness

[Maarten de Groot]

Hi Nicholas and list,

Sorry to be so short in explanation about my problem. I am working on 
the effect of five different acoustic signals (=treatments) on the 
response of one insect species. The animals I used I presented with the 
signals on different days. When I plotted the response variable y with 
the treatments I realized that within the treatments y was not normally 
distributed, and even worse y was not distributed equally for every 
treatment. With other words, for treatment 1 the response variable was 
more skewed to the lower region of the boxplot and in treatment 2 to the 
higher region, etc.
I tried to incorporate the variance into the model as suggested by Nicholas:

mod1<-lme(y~treatment, 
weights=varPower(form=~1|treatment),random=~1|animal,data=mydata)

However this did not work. There is one question I have. Nicholas, you 
are talking about "group" and I do not really understand what you mean 
with this.

Hopefully this gives you a better insight and will help me to solve the 
problem.

Kind regards,

Maarten

On 3/3/2011 10:40 PM, Nicholas Lewin-Koh wrote:
> Hi Maarten,
> Without the random effects, you are basically
> asking can you fit a model where the variance is a function of the
> group.
> So I would think you could do this using nlme,
> ie somehthing like
> library(nlme)
> lme(y~T + X1, data=mydat,
> weights=varPower(form=~1|group),random=~1|whatever)
>
> However, without more information it is hard to say more than that.
>
> Nicholas
>
>
>> Message: 2
>> Date: Thu, 3 Mar 2011 11:11:51 +0100
>> From: Maarten de Groot<Maarten.deGroot at nib.si>
>> To:<r-sig-mixed-models at r-project.org>
>> Subject: [R-sig-ME] comparing treatment with different skewness
>> Message-ID:<4D6F6967.8010303 at nib.si>
>> Content-Type: text/plain; charset="ISO-8859-1"; format=flowed
>>
>> Dear list,
>>
>> I have a problem regarding choosing the distribution family for a mixed
>> model. I want to compare my response variable y with treatment A,B,C and
>> D. In each of the treatments the distribution of y is differently. Is
>> there a way to implement this in a mixed model?
>>
>> Kind regards,
>>
>> Maarten
>>
>>
>>
> .
>