similar to: varclus in Hmisc vs SAS 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

Dear List What is the R package equivalent of Proc Varclus or Information Value. ANy assistance in determining R equivalents of f Oblique Component Analysis (PROC VARCLUS), Information Value (IV) and Weight Of Evidence (WOE) analysis, and business intelligence http://www.nesug.org/proceedings/nesug06/an/da23.pdf Regards, Ajay Websites- http://decisionstats.com [[alternative HTML version

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,

Hi folks, Using R Version 1.6.2 (2003-01-10) on SuSE Linux 7.2, I just installed Hmisc_1.5-3.tar.gz and Design_1.1-5.tar.gz These were taken from http://hesweb1.med.virginia.edu/biostat/s/library/r Checked the dependencies: Hmisc: grid, lattice, mva, acepack -- all already installed Design: Hmisc, survival -- survival already installed, so installed Hmisc first All seems to go

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

Folks, I am looking for a means for calculating the 1-R^2 ratio for variable selection to mimic the values of PROC VARCLUS in SAS. While there may be better methods for variable selection, we are trying to duplicate published results at this time. To date, I have been unable to find a way to obtain this value from Hmisc::varclus , stats::hclust and clustvarsel::clustvarsel. Can any of you give

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

I'm trying to import a .sas7bdat dataset into R with sas.get. I'm using Win-7 and SAS 9.2. I have read the following post and did tweak the line (127, incidentally) of code. <http://r.789695.n4.nabble.com/R-Problem-with-sas-get-function-in-Hmisc-td81 4367.html> http://r.789695.n4.nabble.com/R-Problem-with-sas-get-function-in-Hmisc-td814 367.html However, I get the following

Dear All, I couple of weeks ago, I’ve asked for a package recommendation for nonlinear optimization. In my problem I have a fairly complicated non-linear objective function subject to one non-linear equality constrain. I’ve been suggested to use the *Rdonlp2* package, but I did not get any results after running the program for 5 hrs. Is it normal to run this type of programs for hours? Also,

I tried to use for the first time the Frank Harell's sas.get function (in Hmisc library) and cannot manage to import my sas datas. When I use the sas.get(library,member, ...) method, I get the following error : > sas.get("pme","Edfv1_a")->test.import.sas Error in sas.get("pme", "Edfv1_a") : library, "pme", is not a Unix directory In

Hi, Maybe I'm missing the point here...but let's suppose you are working with "large" data sets and using functions that take a significant amount of time to run in R. I woulnd't like to run these functions every time I call Sweave("myfile.Rnw") within R. What is the "common" practice to use Sweave in these situations. I would just run the function once,

Dear List, Several times I use this package I get the error message shown below. When I work out simple examples, it turns out to be fine, but when working with real and moderate size data sets I always get the same error. Do you know what could be the cause of the problem? Error in apply(mod.matrix[, components], 1, prod) : subscript out of bounds Error in

Many of you are using Sweave for making statistical reports. I thought it might be helpful to some to see an example of the setup I use (in Linux). For those of you who have not yet discovered the power and productivity gains from using Fritz Leisch's wonderful package, I encourage you to give Sweave a try. %File: model.nw %Usage: % Put library(Hmisc;Design;tools} in .First % Sweave model

Thomas, As F Harrel in a preceding message told me that you are the maintainer of Hmisc package, I write directly to you, with copy to the Rhelp list in case someone encountered the same problem and find some benefit in the response. I used quite often the function sas.get in Hmisc library which I is very, very useful. But, as trying to reuse it today it seems not to function anymore. I tried

Hi, I'm new in Latex and I'm trying to include an R chart into a Latex document. This is what I'm doing: 1) In R: save the chart as a a Postcript in a folder C:/xxx/Density.eps 2) In Latex (using TexWorks on windows xp) : In the preambule: \documentclass[11pt]{article} \usepackage{graphicx} \begin{document} blah..blah…blah \begin{figure} \centering

Let's say I have the data frame 'dd' below. I'd like to select one column from this data frame (say 'a') and keep its name in the resulting data frame. That can be done as in #2. However, what if I want to make my selection based on a vector of names (and again keep those names in the resulting data frame). My attempt is #4 but doesn't work. dd <- data.frame(a =

Dear group, Is there a way I could avoid the sort of duplication illustrated below? i.e., I have the same dplyr::summarise function on different group_by arguments. So I'd like to create a single summarise function that could be applied to both. My attempt below fails. df <- data.frame(matrix(rnorm(40), 10, 4), f1 = gl(3, 10, labels = letters[1:3]), f2 =

Hello, I'd like to produce a ggplot where the order of factors within facets is based on the average of another variable. Here's a reproducible example. My problem is that the factors are ordered similarly in both facets. I would like to have, within each facet of `f1', boxplots for 'x' within each factor `f2', where the boxplots are ordered based on the average of x

Dear experts, I'm attempting to solve a constrained optimization problem using the Rdonlp2 package. I created a Lagrange function (L=f(x)-lambda(g(x)-c)), where x is a vector of 16 parameters. This is what I'm using as objective function in the code below. In addition, I set bounds on these parameters (par.u and par.l). When I run the code, I get the error message shown below. Any idea