[R-sig-ME] problem bootstraping a mixed-model (lme)

[David Villegas Ríos]

Thanks Ken.
I tried to follow Faraday's book (Extending the linear model with R)
guidelines for parametric bootstraping but simulate() does not allow models
with a correlation structure, which is my case.
Is there any way to circumvent this issue?

David

2015-04-02 0:03 GMT+02:00 Ken Beath <ken.beath at mq.edu.au>:

> For parametric bootstrapping what is required is a model for the data, and
> using that you generate the bootstrap samples, there is no resampling
> involved.
>
> For non-parametric bootstrapping with multilevel data, resampling
> individual observations is not sufficient, what is required is to resample
> whole clusters/subjects. This is possible in R by converting teh data to a
> wide format, then in thefitting function taking the samples and expanding
> them back to long and fitting the model. You will also need to create
> unique id for each cluster before converting to long.
>
> I recommend reading something like Manly's book on resampling and
> bootstrapping, although I don't think it talks about multilevel models.
>
>
> On 1 April 2015 at 23:30, David Villegas Ríos <chirleu at gmail.com> wrote:
>
>> Dear all,
>> I'm trying to boostrap repeatability estimated from a lme output.
>> The model includes one fixed factor (month), one random factor (ID) and
>> one
>> correlation term to account for temporal autocorrelation of the
>> replicates.
>> I prefer parametric bootstraping since it is the recommended option
>> according to Nakagawa and Schielzeth, 2010 (Biological reviews)
>>
>> These have been my attepmts so far:
>>
>> *Option 1: parametric bootstraping of the full model (what I really need)*
>>
>> bootcoef<-function(data, index){
>>   dat<-data[index,]
>>
>>
>> mod<-lme(dvm~factor(month),random=~1|ID,data=dat,correlation=corAR1(form=~month))
>>
>>
>> return(as.numeric(VarCorr(mod))[1]/(as.numeric(VarCorr(mod))[1]+as.numeric(VarCorr(mod))[2]))
>> # this is the repeatability estimate
>> }
>> output=boot(depm3,bootcoef,100,sim=parametric)
>>
>> *Error*: output$t yields 100 identical values.
>>
>> *Option 2: non-parametric bootstraping of the full model*
>>
>> bootcoef<-function(data, index){
>>   dat<-data[index,]
>>
>>
>> mod<-lme(dvm~factor(month),random=~1|ID,data=dat,correlation=corAR1(form=~month))
>>
>>
>> return(as.numeric(VarCorr(mod))[1]/(as.numeric(VarCorr(mod))[1]+as.numeric(VarCorr(mod))[2]))
>> # this is the repeatability estimate
>> }
>> output=boot(depm3,bootcoef,100)
>>
>> *Error*: Error in Initialize.corAR1(X[[2L]], ...) : covariate must have
>> unique values within groups for "corAR1" objects
>>
>> *Option 3: parametric bootstraping of the model without the
>> autocorrelation
>> term*
>>
>> bootcoef<-function(data, index){
>>   dat<-data[index,]
>>   mod<-lme(dvm~factor(month),random=~1|ID,data=dat)
>>
>>
>> return(as.numeric(VarCorr(mod))[1]/(as.numeric(VarCorr(mod))[1]+as.numeric(VarCorr(mod))[2]))
>> # this is the repeatability estimate
>> }
>> output=boot(depm3,bootcoef,100,sim=parametric)
>>
>> *Erro*r: output$t yields 100 identical values which in addition I don't
>> like because the autocorrelation term is not int he model
>>
>> *Option 4: non-parametric bootstraping of the model without the
>> autocorrelation term*
>>
>> bootcoef<-function(data, index){
>>   dat<-data[index,]
>>   mod<-lme(dvm~factor(month),random=~1|ID,data=dat)
>>
>>
>> return(as.numeric(VarCorr(mod))[1]/(as.numeric(VarCorr(mod))[1]+as.numeric(VarCorr(mod))[2]))
>> # this is the repeatability estimate
>> }
>> output=boot(depm3,bootcoef,100)
>>
>> *Result*: I got 100 different values (this is ok), but I really need the
>> autocorrelation term to be in.
>>
>> Is this something that you can comment about without reproducible data?
>> Any
>> suggestion would be greatly appreciated.
>>
>> David
>>
>>         [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>
>
>
> --
>
> *Ken Beath*
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> Statistics Department
> MACQUARIE UNIVERSITY NSW 2109, Australia
>
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>
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