[R] bootstrapping nlme fits (was boot function)

Fri Aug 22 18:36:28 CEST 2003

On 22 Aug 2003 at 10:18, Frank E Harrell Jr wrote:

The following danish web page 
http://www.dina.dk/phd/s/s2/s2pr1.htm

gives an example of bootstrapping nlme models. If what they are doing 
is vali, I don't know, I abstained from it since I do not understand 
it.

Kjetil Halvorsen

> On Fri, 22 Aug 2003 14:39:28 +0100 (BST)
> Prof Brian Ripley <ripley at stats.ox.ac.uk> wrote:
> 
> > First, this has very little to do with boot: PLEASE use an infromative
> > subject line.  You need to work out how to resample in this situation: are
> > you resampling subjects or observations?  If you are resampling subjects,
> > you need to create a data frame containing just the resampled subjects and
> > pass that to nlme.
> > 
> > However, you also need to think if this is valid.  If you resample 
> > subjects, you will be fitting subjects twice or more as if they are 
> > independent.  I know of no theoretical studies on resampling mixed-effects 
> > models, and urge you to look for such results.
> > 
> > On Fri, 22 Aug 2003, Brunschwig, Hadassa {PDMM~Basel} wrote:
> 
> Hadassa - You may want to look at the slightly simpler generalized least squares with correlated observations case.  For that I have a bootstrap option in the Design packages's glsD function (which uses the nlme package).  There is an option to treat multiply-sampled subjects as if they were 
different subjects, or to pool them into one larger subject (I think the former is more correct but I haven't gotten very far in this thinking).  You can do simulations with glsD to check the performance of the cluster-sampling bootstrap in this situation.  I have done limited simulations and 
bootstrap variance estimates seem to be close to actual values, although not as close as information-matrix-based estimates when the model is true.  glsD attempts to implement the cluster bootstrap fairly efficiently, although it does not yet work for the case where an across-time covariance 
pattern is not assumed.
> 
> Frank Harrell
> 
> > 
> > > I skimmed through the archives and couldnt really find an answer to my
> > > question. 
> > 
> > It's not an R question.
> > 
> > > One thing i dont understand of the description of the function
> > > boot() is the second variable for statistics. I have a sample of say 19
> > > subjects out of these, using boot(), i would like to generate say 1000
> > > samples. For these 1000 samples ill calculate an nlme() and ill use
> > > these 1000 estimators of a variable to make further calculation. 
> > 
> > Whether this is valid most likely depends on what those calculations are.
> > 
> > > Now
> > > what i dont understand is where the index should be set. the nlme()
> > > looks like this:
> > > 
> > > nlme(Concentr~a*(1-exp(Day*(log(0.1,base=exp(1))/exp(logt09))))
> > >                               ,data=data
> > >                               ,fixed=a+logt09~1
> > >                               ,random=a+logt09~1|Subject[ind]
> > >                               ,start=list(fixed=c(a=30,logt09=1)))
> > > 
> > > My idea was to put the index ( second variable of the statistcs
> > > function) 
> > 
> > What that variable means depends on the other arguments to boot, and you 
> > haven't told us what those are.
> > 
> > > in the subject as i want to generate different samples of
> > > subjects. I get the error that the vector ind was not found. I would be
> > > happy for any help concerning this problem.
> > 
> > 
> > 
> > -- 
> > Brian D. Ripley,                  ripley at stats.ox.ac.uk
> > Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
> > University of Oxford,             Tel:  +44 1865 272861 (self)
> > 1 South Parks Road,                     +44 1865 272866 (PA)
> > Oxford OX1 3TG, UK                Fax:  +44 1865 272595
> > 
> > ______________________________________________
> > R-help at stat.math.ethz.ch mailing list
> > https://www.stat.math.ethz.ch/mailman/listinfo/r-help
> 
> 
> ---
> Frank E Harrell Jr              Prof. of Biostatistics & Statistics
> Div. of Biostatistics & Epidem. Dept. of Health Evaluation Sciences
> U. Virginia School of Medicine  http://hesweb1.med.virginia.edu/biostat
> 
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