Martin, Pardon the delayed reply.
Bootstrap methods have been around for some time (late seventies?), but their popularity seems to have exploded in correspondence with computing technology. You should be able to find more information in most modern books on statistical inference, but here is a brief: The bootstrap is a method often used to establish an empirical null distribution for a test statistic when traditional (analytical) methods fail. The bootstrap works by imposing a null hypothesis on the observed data, followed by re-sampling with replacement. The test statistic is computed at each re-sample and used to build up an empirical null distribution. The idea is to impose the null hypothesis while preserving variability in the observed data, and thus the test statistic. For example, suppose we observe some continuous scalar data and hypothesize that the sample was observed from a population with mean zero. We can impose this hypothesis by subtracting the sample mean from each observation. Re-samples from these transformed data are treated as having been observed under the null hypothesis. In the case of classification and partitioning, the difficulty is formulating a meaningful null hypothesis about the collection of classifications, and imposing the null hypothesis in a bootstrap sampling scheme. -Matt On Wed, 2010-11-17 at 10:01 -0500, Martin Tomko wrote: > Thanks Mat, > I have in the meantime identified the Rand index, but not the others. I > will also have a look at profdpm, that did not pop-up in my searches. > Indeed, the interpretation is going to be critical... Could you please > elaborate on what you mean by the bootstrap process? > > Thanks a lot for your helps, > Martin > > On 11/17/2010 3:50 PM, Matt Shotwell wrote: > > There are several statistics used to compare nominal classifications, or > > _partitions_ of a data set. A partition isn't quite the same in this > > context because partitioned data are not restricted to a fixed number of > > classes. However, the statistics used to compare partitions should also > > work for these 'restricted' partitions. See the Rand index, Fowlkes and > > Mallows index, Wallace indices, and the Jaccard index. The profdpm > > package implements a function (?profdpm::pci) that computes these > > indices for two factors representing partitions of the same data. > > > > The difficult part is drawing statistical inference about these indices. > > It's difficult to formulate a null hypothesis, and even more difficult > > to determine a null distribution for a partition comparison index. A > > bootstrap test might work, but you will probably have to implement this > > yourself. > > > > -Matt > > > > On Wed, 2010-11-17 at 08:33 -0500, Martin Tomko wrote: > > > >> Dear all, > >> I am having a hard time to figure out a suitable test for the match > >> between two nominal classifications of the same set of data. > >> I have used hierarchical clustering with multiple methods (ward, > >> k-means,...) to classify my dat into a set number of classesa, and I > >> would like to compare the resulting automated classification with the > >> actual - objective benchmark one. > >> So in principle I have a data frame with n columns of nominal > >> classifications, and I want to do a mutual comparison and test for > >> significance in difference in classification between pairs of columns. > >> > >> I just need to identify a suitable test, but I fail. I am currently > >> exploring the possibility of using Cohen's Kappa, but I am open to other > >> suggestions. Especially the fact that kappa seems to be moslty used on > >> failible, human annotators seems to bring in limitations taht do not > >> apply to my automatic classification. > >> Any help will be appreciated, especially if also followed by a pointer > >> to an R package that implements it. > >> > >> Thanks > >> Martin > >> > >> ______________________________________________ > >> [email protected] 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. > >> > > > > -- Matthew S. Shotwell Graduate Student Division of Biostatistics and Epidemiology Medical University of South Carolina ______________________________________________ [email protected] 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.
