similar to: smacof package for multidimensional scaling

anacor-0.9.0 is on CRAN (by De Leeuw and Mair) anacor does correspondence analysis and canonical correspondence analysis. It can make row plots, column plots, joint plots, Benz?cri plots, regression plots, and transformation plots. Where appropriate, plots can be in 3d using either rgl or scatterplot3d. Row and column points can be in standard scaling, Benz?cri scaling, Goodman scaling,

anacor-0.9.0 is on CRAN (by De Leeuw and Mair) anacor does correspondence analysis and canonical correspondence analysis. It can make row plots, column plots, joint plots, Benz?cri plots, regression plots, and transformation plots. Where appropriate, plots can be in 3d using either rgl or scatterplot3d. Row and column points can be in standard scaling, Benz?cri scaling, Goodman scaling,

http://www.cuddyvalley.org/psychoR/code now contains version 1.4 of ca.R There are already many versions of CA and CCA in various R packages, but this one has some unique features, so maybe it is useful. The function ca() can do simple CA, but it also allows for linear restrictions on the row and column scores (they can be restricted to be in the span of a number of covariates). Thus the

Dear all, My data includes almost three thousand people who rank ten categories into three variables. The simple example below is almost same except I have many missing values. x <- cbind( sample( LETTERS[1:10] , 3000 , replace = TRUE ) , sample( LETTERS[1:10] , 3000 , replace = TRUE ) , sample( LETTERS[1:10] , 3000 , replace = TRUE ) ) I try to figure

Over the last couple of years I have written quite a few R programs for various "psychometric" techniques, and I am regularly updating and expanding what is there. I now have (wholly or partially), or have planned -- gifi package (update to homals on CRAN). Code for multiple correspondence analysis, nonlinear principal component analysis, nonlinear multiset canonical

Dear R-helpers, I have dist class objects for 10 individuals rating the dissimilarities (on a 100-point scale) of the same 10 faces (analogous to the bread data). I would like to get an individual differences scaling jointly for the individual judges and the faces, and plot them on the same axes. This is the example: library(smacof) data(breakfast) res.rect<-smacofRect(breakfast,

Hi Michael, with res.uc$conf you'll get the single configurations for each rater. You can use these to produce the plot you want to have. Best, Patrick r-help-request at r-project.org wrote: > Send R-help mailing list submissions to > r-help at r-project.org > > To subscribe or unsubscribe via the World Wide Web, visit > https://stat.ethz.ch/mailman/listinfo/r-help >

I sent a query to R-Help about the availability of nonmetric multidimensional scaling (MDS) algorithms in R. I would like to thank Tony Rossini, Jonathan Baron, Sundar Dorai-Raj, and Brian Ripley for helpful replies. The gist of the replies is that isoMDS in the MASS library provides Kruskal's method for nonmetric MDS, sammon in the MASS library provides Sammon's nonlinear mapping method

A student of mine wants to use R to do some nonmetric multidimensional scaling. According to the R FAQ, there's a package called pcurve that computes multidimensional scaling solutions, but I was not able to locate it the contrib page (I am a Windows user with R version 1.4.1). Can anyone tell me whether it is possible to do nonmetric multidimensional scaling with R, and if so, how? John

I have a dissimilarity dataset with the form: 1 1 dissimilarity value 1 2 ... 1 3 1 4 2 2 2 3 2 4 ... I would like to do nonmetric multidimensional scaling with this data, but I am having trouble using this format. I would like to either find a function that accepts this format or find a way to easily convert this format to a matrix for use with existing functions. Thanks!

Many new things at http://www.cuddyvalley.org/psychoR/ The scalassoc package, which fits exponential distance association models to indicator matrices, it now at version 1.0.0. It seems to be robust and can analyze large examples easily. It is a major improvement (in speed and robustness) over the distassoc package, which is at the same site. scalassoc does something neat (if you like

but maybe of use to some: http://gifi.stat.ucla.edu/psychoR/derivatives Computes generalized eigenvalue solutions Ax=\lambda Bx and generalized singular value solutions Rz=\gamma Px and R'x=\gamma Qy for matrices that are differentiable functions of a vector of parameters. Along with the decomposition the code returns arrays with all first-order partial derivatives of the values/vector wrt

Dear R-helpers, I am running metaMDS in the vegan package, which uses isoMDS in MASS, to perform Nonmetric Multidimentional Scaling (NMDS). I have seen some authors report a p-value for the NMDS ordination based on randomization of the dataset. As I understand it this is meant to compare the stress in your dataset to multiple runs of randomized data. I do not see a way to perform such a test in

Hello I will be applying MDS (actually Isomap) to make a psychological "concept map" of the similarities between N concepts. I would like to scale to a large number of concepts, however, the resulting N*(N-1) pairwise similarities is prohibitive for a user survey. I'm thinking of giving people random subsets of the pairwise similarities. Does anyone have recommendations for this

http://idisk.mac.com/jdeleeuw-Public/jacobi This is paper/software for various techniques based on Jacobi plane rotations. There is R code for -- classical cyclical Jacobi Eigen diagonalization -- Jacobi-based SVD diagonalization -- approximate simultaneous diagonalization of symmetric matrices (De Leeuw/Pruzansky 1978) -- approximate simultaneous diagonalization of rectangular matrices

Hi, I am stuck in one problem when doing nonmetric multidimensional scaling. I use the function 'metaMDS' in the package 'vegan' to work on the presence/absence community data. The problem is when two samples are identical (dissimilarity = 0), metaMDS cannot work with zero dissimilarity. I don't want to delete the duplicates as they are true samples from different locations.

Hello, We are trying to find a function to compute "stress" in our multidimensional scaling analysis of a dissimilarity matrix. We've used "dist()" to create the matrix and "cmdscale()" for the scaling. In order to determine the number of dimensions we would like to plot stress vs. dimensions. However, we cannot find a pre-made command. It seems that other

Hello! Very often one can hear that MDS usually ends with two-dimensional solution. Of course, there are methods, like Scree-test (proposed by Kruskal and Wish, 1981), to determine optimal number of dimensions. However, I am trying to find references to this two-dimensional gold-standard. Can anyone point me to authors which explicitly states that two-dimensions are typical and easiest to

homals-0.9.0 is on CRAN -- by Jan de Leeuw and Patrick Mair This package implements the methods discussed in Gifi, Nonlinear Multivariate Analysis, Wiley, 1990. In the Gifi terminology it covers homals, princals, canals, morals, criminals, and overals. The R implementation fills several gaps in Gifi, adding multiple ordinal, numerical, and polynomial data transformations. Differences with

homals-0.9.0 is on CRAN -- by Jan de Leeuw and Patrick Mair This package implements the methods discussed in Gifi, Nonlinear Multivariate Analysis, Wiley, 1990. In the Gifi terminology it covers homals, princals, canals, morals, criminals, and overals. The R implementation fills several gaps in Gifi, adding multiple ordinal, numerical, and polynomial data transformations. Differences with