Grant, Have you considered a gls model instead of a lm model? In a gls model one can model the correlation between the measures. So you won't need to select a subset of your data. You can kind gls in the nlme package.
HTH, Thierry ------------------------------------------------------------------------ ---- ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest Cel biometrie, methodologie en kwaliteitszorg / Section biometrics, methodology and quality assurance Gaverstraat 4 9500 Geraardsbergen Belgium tel. + 32 54/436 185 [EMAIL PROTECTED] www.inbo.be To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey -----Oorspronkelijk bericht----- Van: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Namens Grant Gillis Verzonden: donderdag 4 september 2008 14:57 Aan: r-help@r-project.org Onderwerp: Re: [R] restricted bootstrap Hello Professor Ripely, Sorry for not being clear. I posted after a long day of struggling. Also my toy distance matrix should have been symmetrical. Simply put I have spatially autocorrelated data collected from many points. I would like to do a linear regression on these data. To deal with the autocrrelation I want to resample a subset of my data with replacement but I need to restrict subsets such that no two locations where data was collected are closer than Xm apart (further apart than the autocrrelation in the data). Thanks for having a look at this for me. I will look up the hard-core spatial point process. Grant 2008/9/4 Prof Brian Ripley <[EMAIL PROTECTED]> > I see nothing here to do with the 'bootstrap', which is sampling with > replacement. > > Do you know what you mean exactly by 'randomly sample'? In general the way > to so this is to sample randomly (uniformly, whatever) and reject samples > that do not meet your restriction. For some restrictions there are more > efficient algorithms, but I don't understand yours. (What are the 'rows'? > Do you want to sample rows in space or xy locations? How come 'dist' is > not symmetric?) For some restrictions, an MCMC sampling scheme is needed, > the hard-core spatial point process being a related example. > > > On Wed, 3 Sep 2008, Grant Gillis wrote: > > Hello List, >> >> I am not sure that I have the correct terminology here (restricted >> bootstrap) which may be hampering my archive searches. I have quite a >> large >> spatially autocorrelated data set. I have xy coordinates and the >> corresponding pairwise distance matrix (metres) for each row. I would >> like >> to randomly sample some number of rows but restricting samples such that >> the >> distance between them is larger than the threshold of autocorrelation. I >> have been been unsuccessfully trying to link the 'sample' function to >> values >> in the distance matrix. >> >> My end goal is to randomly sample M thousand rows of data N thousand times >> calculating linear regression coefficients for each sample but am stuck on >> taking the initial sample. I believe I can figure out the rest. >> >> >> Example Question >> >> I would like to radomly sample 3 rows further but withe the restriction >> that >> they are greater than 100m apart >> >> example data: >> main data: >> >> y<- c(1, 2, 9, 5, 6) >> x<-c( 1, 3, 5, 7, 9) >> z<-c(2, 4, 6, 8, 10) >> a<-c(3, 9, 6, 4 ,4) >> >> maindata<-cbind(y, x, z, a) >> >> y x x a >> [1,] 1 1 1 3 >> [2,] 2 3 3 9 >> [3,] 9 5 5 6 >> [4,] 5 7 7 4 >> [5,] 6 9 9 4 >> >> distance matrix: >> row1<-c(0, 123, 567, 89) >> row2<-c(98, 0, 345, 543) >> row3<-c(765, 90, 0, 987) >> row4<-c(654, 8, 99, 0) >> >> dist<-rbind(row1, row2, row3, row4) >> >> [,1] [,2] [,3] [,4] >> row1 0 123 567 89 >> row2 98 0 345 543 >> row3 765 90 0 987 >> row4 654 8 99 0 >> >> Thanks for all of the help in the past and now >> >> Cheers >> Grant >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> R-help@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide >> http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> >> > -- > Brian D. Ripley, [EMAIL PROTECTED] > Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/<http://www.stats.ox.ac.uk/%7Eripley/> > University of Oxford, Tel: +44 1865 272861 (self) > 1 South Parks Road, +44 1865 272866 (PA) > Oxford OX1 3TG, UK Fax: +44 1865 272595 > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Dit bericht en eventuele bijlagen geven enkel de visie van de schrijver weer en binden het INBO onder geen enkel beding, zolang dit bericht niet bevestigd is door een geldig ondertekend document.%CRLF%The views expressed in this message and any annex are purely those of the writer and may not be regarded as stating an official position of INBO, as long as the message is not confirmed by a duly signed document%CRLF% ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
