[R] bootstrapping nlme fits (was boot function)
Frank E Harrell Jr
fharrell at virginia.edu
Fri Aug 22 21:14:33 CEST 2003
On Fri, 22 Aug 2003 12:36:28 -0400
"kjetil brinchmann halvorsen" <kjetil at entelnet.bo> wrote:
> 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
Thanks. I use the unconditional bootstrap which does not assume a correlation structure and does not use residuals.
Frank
>
>
> > 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
> >
> > ______________________________________________
> > R-help at stat.math.ethz.ch mailing list
> > https://www.stat.math.ethz.ch/mailman/listinfo/r-help
>
> ______________________________________________
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---
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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