Hi, exist a special cluster-analysis algorithms which can work with NA's. a further "problem" is that i want cluster variables not cases to identify special variable-set's. Is it a common way turn the data.frame and use kmeans,because this works with NA's, or have anybody another method for finding "variable-sets" , with exception of factor analysis. thanks for advance Christian -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
Hi, On Wed, 7 Aug 2002 chr.schulz at email.de wrote:> Hi, > > exist a special cluster-analysis algorithms > which can work with NA's. > > a further "problem" is that i want cluster > variables not cases to identify special variable-set's. > > Is it a common way turn the data.frame and use > kmeans,because this works with NA's, or have anybody another > method for finding "variable-sets" , with exception of factor analysis. > > thanks for advance > ChristianThe choice of a clustering method depends very much on the aims of your analysis and especially on your concept of "similarity" between the entities to cluster. One of the most often used methods for clustering variables is to compute a dissimilarity matrix between the variables, where dissimilarity is defined as 1-correlation or 1-|correlation| (depending on if you want variables pointing in almost exactly the opposite direction to be interpreted as similar or as extremely dissimilar). Then you can apply one of the algorithms of hclust or agnes of library cluster (the latter is not a bad choice as a default). NAs are not a big problem because correlations can be properly computed in presence of NAs. Christian> > > > -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- > r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html > Send "info", "help", or "[un]subscribe" > (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch > _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._ >-- *********************************************************************** Christian Hennig Seminar fuer Statistik, ETH-Zentrum (LEO), CH-8092 Zuerich (current) and Fachbereich Mathematik-SPST/ZMS, Universitaet Hamburg hennig at stat.math.ethz.ch, http://stat.ethz.ch/~hennig/ hennig at math.uni-hamburg.de, http://www.math.uni-hamburg.de/home/hennig/ ####################################################################### ich empfehle www.boag.de -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
Hi, thanks for the different replies. Varclus in the Hmisc-Package is really nice and is exactly what i need for my intention (... and works with correlations what you describe below ,too) ;-)) regards,Christian Christian Hennig <hennig at stat.math.ethz.ch> schrieb am 08.08.02 10:38:00:> Hi, > > On Wed, 7 Aug 2002 chr.schulz at email.de wrote: > > > Hi, > > > > exist a special cluster-analysis algorithms > > which can work with NA's. > > > > a further "problem" is that i want cluster > > variables not cases to identify special variable-set's. > > > > Is it a common way turn the data.frame and use > > kmeans,because this works with NA's, or have anybody another > > method for finding "variable-sets" , with exception of factor analysis. > > > > thanks for advance > > Christian > > The choice of a clustering method depends very much on the aims of your > analysis and especially on your concept of "similarity" between the > entities to cluster. One of the most often used methods for clustering > variables is to compute a dissimilarity matrix between the variables, where > dissimilarity is defined as 1-correlation or 1-|correlation| (depending on > if you want variables pointing in almost exactly the opposite direction > to be interpreted as similar or as extremely dissimilar). > Then you can apply one of the algorithms of hclust or agnes of library > cluster (the latter is not a bad choice as a default). > NAs are not a big problem because correlations can be properly computed in > presence of NAs. > > Christian > > > > > > > > > -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- > > r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html > > Send "info", "help", or "[un]subscribe" > > (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch > > _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._ > > > > -- > *********************************************************************** > Christian Hennig > Seminar fuer Statistik, ETH-Zentrum (LEO), CH-8092 Zuerich (current) > and Fachbereich Mathematik-SPST/ZMS, Universitaet Hamburg > hennig at stat.math.ethz.ch, http://stat.ethz.ch/~hennig/ > hennig at math.uni-hamburg.de, http://www.math.uni-hamburg.de/home/hennig/ > ####################################################################### > ich empfehle www.boag.de > >-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._