similar to: R equivalent of proc varclus

I am interested in finding an R version of SAS "Proc Varclus". SAS's Proc Varclus implements an oblique cluster analysis based on principal components. How can I find out if R has a package that runs the same algorithm implemented in SAS "Proc Varclus"? Thank you, Mary Helen Black __________________________ Mary Helen Black, M.S. Keck School of Medicine of USC

Hi, I'll apreciate your guidance on how can I re-create the output from SAS PROC VARCLUS in R. I've found the varclus function in Hmisc. However, is it possible to use that function to compute for each variable the 1-R**2 ratio (this is the ratio of 1 minus the R-squared with Own Cluster to one minus the R-squared in the Next Closest cluster)? Thanks in advance for any help, Lars.

Hi All, I'd like to build a package for the community that replicates the output produced by SAS "proc varclus". According to the SAS documentation, the first few steps are: 1. Find the first two principal components. 2. Perform an orthoblique rotation (quartimax rotation) on eigenvectors. 3. Assign each variable to the rotated component with which it has the higher squared

Dear R users: is there a package, similar to varclus in SAS or varclus in S, ported or written for R? Also, is there any other package in R that was designed for grouping the variables under different measures of distance (in cases where data is non-Gaussian, autocorrelated, and so on). Janusz. -- ** Janusz Kawczak ** ** UNC at Charlotte,

is there a smart way of determining the number of pairwise present data in a data matrix with missings (maybe as a by-product of some statistical function?) so far, i used several loops like: for (column1 in 1:99) { for (column2 in 2:100) { for (row in 1:500) { if (!is.na(matrix[row,column1]) & !is.na(matrix[row,column2])) { pairs[col1,col2] <- pairs[col1,col2]+1

I frequently use hclust on a similarity matrix. In R only a distance matrix is allowed. Is there a simple reliable transformation of a similarity matrix that will result in a distance matrix making hclust work the same as S-Plus with a similarity matrix? Venables & Ripley 3rd edition implies that a simple reversal of values will suffice. Thanks -Frank -- Frank E Harrell Jr

Hello R-help, Thanks for everyone's very helpful suggestions so far. I am now trying to use aregImpute for my missing data imputation. Here are the code and error messages. Any suggestions would be very much appreciated. Sincerely, Anders Corr ######################################## #Question for R-Help on aregImpute ######################################## #DOWNLOAD DATA (61Kb)

I am using R 1.3.0 on Windows 2000. For an experiment, I am wanting to find the most diverse 400 items to study in a possible 3200 items. Diversity here is based on a few hundred attributes. For this, I would like to do a clustering analysis and find 400 clusters (i.e. different from each other in some way hopefully). From each of these 400 clusters, I will pick a representative. I expect

R-helpers: I am using R 2.5 on Windows XP, packages all up to date. I have run into an issue with the MIcombine function of the mitools package that I hoped some of you might be able to help with. I will work through a reproducible example to demonstrate the issue. First, make a dataset from the pbc dataset in the survival package --------------- # Make a dataset library(survival) d <-

Hi, I'd got a matrix of correaltion values. Which cluster method can I use to cluster it? Best Regards...

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

Dear R I recently performed a cluster analysis. It produced the dendogram no problem but unfortunately the font size of the row.names were all cluttered due to their large size So I tried to change the font size using plclust(cluster.results, labels=iris$specie, cex=0.8) and R came back to me saying Error in plclust(cluster.results, labels = iris$specie, cex = 0.8) : unused argument(s)

Hi, I've recently purchased PowerBookG4 (panther) and installed RAqua 1.8.1. I also installed gcc version 3.3 through Xcode tools. But, for some reason, I'm having a hard time getting gcc work with R. For example, I get the following error while installing Hmisc package. I would appreciate any suggestion to fix this problem. Thanks Kosuke * Installing *source* package

Frank Harrell suggested I re-post with information about the version of R Heres's the information: > > version > _ > platform powerpc-apple-darwin6.8 > arch powerpc > os darwin6.8 > system powerpc, darwin6.8 > status > major 2 > minor 0.1 > year 2004 > month 11 > day 15 > language R Here's what happens

Hi: We're trying to install the Hmisc package on a Solaris 9 machine. Here's what we get: R CMD INSTALL /usr/local/srctree/Hmisc_3.4-2.tar.gz * Installing to library '/usr/local/lib/R/library' * Installing *source* package 'Hmisc' ... ** libs g95 -fPIC -g -O2 -c cidxcn.f -o cidxcn.o g95 -fPIC -g -O2 -c cidxcp.f -o cidxcp.o g95 -fPIC -g -O2 -c hoeffd.f -o

Hello everyone, Does anyone know how to implement the argument "unit" in R's plclust function ? I used to use Splus where this argument exists but it has not been implemented in R's plclust. The reason why I switched from Splus to R is that Ward's method is not implemented for S's hclust whereas it is implemented for R's hclust. What I would need is S's plclust

Hello, I'm trying to install a package that requires a Fortran compiler (Hmisc) using R CMD INSTALL. I downloaded the package source onto my Desktop, unzipped it, and then typed: R CMD INSTALL /Users/brianbeckage/Desktop/Hmisc * Installing *source* package 'Hmisc' ... ** libs g77 -fno-common -g -O2 -c cidxcn.f -o cidxcn.o g77 -fno-common -g -O2 -c cidxcp.f -o cidxcp.o g77

I am running Ubuntu 7.10 and R-2.6.0, and I am having trouble updating packages. There appears to be a problem involving gfortran. For example, here is the output of an attempt to update the Hmisc package. * Installing *source* package 'Hmisc' ... ** libs gfortran -fpic -g -O2 -c cidxcn.f -o cidxcn.o gfortran -fpic -g -O2 -c cidxcp.f -o cidxcp.o gfortran -fpic -g -O2 -c

Hi R-Help! I am trying to find a nicer way of extracting all the "complete" diagonals of a matrix. I am working with very large matrices that have many more rows than columns. I want to be able to extract each of the diagonals that are as long as the number of columns in the matrix. I have written a rather ugly function that presently does the job. It illustrates what I am trying to

Hello, I need to plot multiple confidence intervals for the same model parameter e.g. so for the same value of the parameter in point x_1 I would like to see four different confidence intervals so that I can compare the accuracy e.g. boot basic vs normal vs my own vs classic lm CI etc. I like very very much the plotCI implemented here: http://cran.r-project.org/web/packages/plotrix/index.html