similar to: Orthoblique rotation on eigenvectors (SAS 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.

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

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,

I have the following situation I want to analyse with prcomp. Each subject has a curve called the contrast sensitivity function (CSF). This curve's overall shape is due to the additive output of 3 "channels" (eigenvectors). #this shows 3 SF channels; net CSF = c1 + c2+c3 x<-1:100 c1<-dnorm(x,mean=20,sd=20) c2<-dnorm(x,mean=50,sd=20) c3<-dnorm(x,mean=80,sd=20)

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

Thanks to Mr.Liviu Androvic and Mr.Richard Rowe helped me in PCA. Because I have just learn R language in a few day so I have many problem. 1) I don't know why PCA rotation function not run although I try many times. Would you please hepl me and explain how to read the PCA map (both of rotated and unrotated) in a concrete example. 2) Where I can find document relate: Plan S(A), S(A*B),

We have just put a package GPArotation on CRAN. The functions in this package perform an number of different orthogonal and oblique rotations for factor analysis, using the gradient projection algorithm described in Coen A. Bernaards and Robert I. Jennrich (2005), "Gradient Projection Algorithms and Software for Arbitrary Rotation Criteria in Factor Analysis, ", Educational and

We have just put a package GPArotation on CRAN. The functions in this package perform an number of different orthogonal and oblique rotations for factor analysis, using the gradient projection algorithm described in Coen A. Bernaards and Robert I. Jennrich (2005), "Gradient Projection Algorithms and Software for Arbitrary Rotation Criteria in Factor Analysis, ", Educational and

Dear all, I've used the 'prcomp' command to calculate the eigenvalues and eigenvectors of a matrix(gg). Using the command 'principal' from the 'psych' package  I've performed the same exercise. I got the same eigenvalues but different eigenvectors. Is there any reason for that difference? Below are the steps I've followed: 1. PRCOMP #defining the matrix

Hi, dear R pros I try to understand eigen(). I have seen, that eigen() gives the eigenvectors normalized to unit length. What shall I do to get the eigenvectors not normalized to unit length? E.g. take the example: A [,1] [,2] V1 0.7714286 -0.2571429 V2 -0.4224490 0.1408163 Calculating eigen(A) "by hand" gives the eigenvectors (example from Backhaus,

Hi, I try to calculate the angle between two first eigenvectors of different covariance matrices of biological phenotypic traits for different populations. My issue here is, that all possibilities to do so seem to normalize the eigenvectors to length 1. Although the helpfile of eigen() states, that using eigen(, symmetric = FALSE, EISPACK =TRUE) skips normalization this is (I guess) not applicable

Hi, I was wondering if function eigen() does something different from the function call eigen() in SAS. I'm in the process of translating a SAS code into a R code and the values of the eigenvectors and eigenvalues of a square matrix came out to be different from the values in SAS. I would also appreciate it if someone can explain the difference in simple terms. I'm pretty new to both

Hi, How the eigenvectors output by the eigen() function are ordered. The first column corresponds to the largest eigenvalue? or is the last column as in Octave? I'm performing a spatial-temporal analysis of some climatic variables so my matrices are MxN (locations*time)and I'm looking for the leading EOF's. As I have understand the eigenvectors columns represent those EOF's

Hi everybody, I have some problems with the function eigen. I have a square matrix and I want to calculate the eigenvalues and eigenvectors. I apply the function eigen and I get it, however when I solve the same problem in Statistica software, I realise that some eigenvectors are the opposite. How can I get the same values? Thanks in advance [[alternative HTML version deleted]]

Dear listserv, I am trying to perform a principal components analysis and create an output table of the eigenvalues for the dependent variables. What I want is to see which variables are driving each principal components axis, so I can make statements like, "PC1 mostly refers to seed size" or something like that. For instance, if I try the example from ?prcomp > prcomp(USArrests,

Hi ... Please could you help with probably a very simple problem I have. I'm completely new to R and am trying to follow a tutorial using R for Force Distribution Analysis that I got from ... http://projects.eml.org/mbm/website/fda_gromacs.htm. Basically, the MDS I preform outputs a force matrix (.fm) from the force simulation I perform. Then, this matrix is read into R and prcomp is

Inicio del mensaje reenviado: > De: Arnau Mir <arnau.mir@uib.es> > Fecha: 14 de noviembre de 2011 13:24:31 GMT+01:00 > Para: Martin Maechler <maechler@stat.math.ethz.ch> > Asunto: Re: [R] How to compute eigenvectors and eigenvalues? > > Sorry, but I can't explain very well. > > > The matrix 4*mp is: > > 4*mp > [,1] [,2] [,3] > [1,]

Hello. Consider the following matrix: mp <- matrix(c(0,1/4,1/4,3/4,0,1/4,1/4,3/4,1/2),3,3,byrow=T) > mp [,1] [,2] [,3] [1,] 0.00 0.25 0.25 [2,] 0.75 0.00 0.25 [3,] 0.25 0.75 0.50 The eigenvectors of the previous matrix are 1, 0.25 and 0.25 and it is not a diagonalizable matrix. When you try to find the eigenvalues and eigenvectors with R, R responses: > eigen(mp) $values [1]

Hello list, Sorry, these questions are not directly linked to R. If I consider an indefinte real matrix, I would like to know if the symmetry of the matrix is sufficient to say that their eigenvectors are real ? And what is the conditions to ensure that eigenvectors are real in the case of an asymmetric matrix (if some conditions exist)? Thanks in Advance, St?phane DRAY