On Fri, 22 Aug 2003 12:36:28 -0400 "kjetil brinchmann halvorsen" <[EMAIL PROTECTED]> 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 <[EMAIL PROTECTED]> 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, [EMAIL PROTECTED] > > > 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 > > > > > > ______________________________________________ > > > [EMAIL PROTECTED] 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 > > > > ______________________________________________ > > [EMAIL PROTECTED] mailing list > > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > > ______________________________________________ > [EMAIL PROTECTED] 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 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help