similar to: R-commands for MDS

The following code in clusplot.default (package cluster) is in error: x1 <- cmdscale(x, k = 2, eig = TRUE) var.dec <- sum(x1$eig)/sum(diag(x1$x)) if (var.dec < 0) var.dec <- 0 if (var.dec > 1) var.dec <- 1 x1 <- x1$points x1 has components with names "points" and "eig", not "x", so

The following problem occurs in R 1.4.0 and 1.3.1 for Windows95, but not in R 1.2.0 for Windows95. The problem does not occur in R 1.4.0 for Linux PC, Linux Alpha and HP-UX. Sometimes, the type of 'Size' of an object of type 'dist' changes from integer into double. Running cmdscale on such a 'dist' object gives invalid results. I don't know what should be considered

Hello all, I'm using R1.7.1 on Linux, generating sammon-optimized MDS plots from distance matrices. This is a calculation I run routinely, often on sample sets of up to 100 samples. This time, with three samples, the sammon function returned an error (shown below), which I tracked down to the cmdscale function it uses to find a starting configuration. In short, cmdscale is returning NaN

Dear R developers, there appears to be a small problem with function cmdscale: for non-Euclidean distance matrices, using option add=FALSE (the default), cmdscale misses the smallest eigenvalue. This affects GOF statistic g.1 (See Mardia, Kent + Bibby (1979): Multivariate Analysis, eq. (14.4.7). The corresponding formula in Cox + Cox (2001): Multidimensional Scaling, 2nd ed., p 38, would

Hello, I have a dilemma that I'm hoping the R gurus will be able to help resolve. For background: My data is in the form of a (dis)similarity matrix created from taking the inverse of normalized reaction times. That is, each cell of the matrix represents how long it took to distinguish two stimuli from one another-- a square matrix of 45X45 where the diagonal values are all zero (since this

GENERAL REFERENCE ON NONLINEAR OPTIMIZATION? What are your favorite references on nonlinear optimization? I like Bates and Watts (1988) Nonlinear Regression Analysis and Its Applications (Wiley), especially for its key insights regarding parameter effects vs. intrinsic curvature. Before I spent time and money on several of the refences cited on the help pages for "optim",

Hi, Maybe I have not been looking in the right spot, but, I have not been able to fine a command to automatically calculate the running cumulative sum of a vector. Is there such a command? Example of current code: > eig$values [1] 678.365651 6.769697 2.853783 > prop<-eig$values/sum(eig$values) > prop [1] 0.986012163 0.009839832 0.004148005 >

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

Hi all, I've been trying to figure out how to superimpose a histogram on a biplot that shows the relative contribution of each axis. I have been using the NIPALS function (http://biomserv.univ-lyon1.fr/~dray/ files/softwares/nipals.R) to run principal component analyses. Here is a toy example. source("http://biomserv.univ-lyon1.fr/~dray/files/softwares/nipals.R")

Hi I need to extract the data returned by Eigen to plot the eigenvectors. However, when I try and eigv = eigen(covariance); it returns an object with the matrices containing eigenvalues and vectors.. how can I extract the eigenvector matrix from this?? When I try mat = eig["vectors"] it returns a matrix with the "$vectors" string on top , how can I remove this? code: > eig

Hi, I am looking for the best multidimensional configuration for my data (47*47 distance matrix). I ve tried classical metric (cmdscale) and non metric MDS (isoMDS, nmds) but it is now difficult to choose the best solution because of the uncertainties in the definitions of the "stress" function. So, same problem, several questions : 1. Statistical consideration : With

Hi, I want to know whether there is any function in R to find STRESS after using cmdscale and estimating the coordinates, I have written these functions to find stress (for p =1,2,3,4,5) stress<-rep(0,5) for(p in 1:5) { datahat<-cmdscale(d,p) deltahat<-as.matrix(dist(datahat)) a<-0 b<-0 for(i in 1:n) { for(j in 1:n) { a<-d[i,j]^2+a b<-(d[i,j]-deltahat[i,j])^2+b } }

Hi all, I'm trying to perform an MDS of some data that I have. When I use cmdscale everything is fine and I get some interesting results however, the tends to be low. What I wnat to do is compare this with the Non-Metric MDS using isoMDS or sammon. However, when I try using these I get the following message. Error in isoMDS(x.dist) : zero or negative distance between objects 2 and 4

i know cmdscale and isoMDS inR can do classical and non-metric MDS.but i want to konw if there is packages can carry on "individual differences scaling" and "multidimensional analysis og preference"?both method are important one,but i can not find any clue on how to do it using R. anyone can help? thank you!

Dear R-listers Is there anyone who knows why I get different eigenvectors when I run MatLab and R? I run both programs in Windows Me. Can I make R to produce the same vectors as MatLab? #R Matrix PA9900<-c(11/24 ,10/53 ,0/1 ,0/1 ,29/43 ,1/24 ,27/53 ,0/1 ,0/1 ,13/43 ,14/24 ,178/53 ,146/244 ,17/23 ,15/43 ,2/24 ,4/53 ,0/1 ,2/23 ,2/43 ,4/24 ,58/53 ,26/244 ,0/1 ,5/43) #R-syntax

Folks: I'm trying to port some code from python over to R, and I'm running into a wall finding R code that can solve a generalized eigenvalue problem following this function model: http://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.eig.html Any ideas? I don't want to call python from within R for various reasons, I'd prefer a "native" R solution if one

Dear Sirs and madams :-) I am trying to teach myself multidimensional scaling. To that effect I have collected a survey asking people to rank 10 philosophers and politicians according to their preference. I have collected 61 answers. The data is organized in ten columns and 61 rows. the columns are "choice_1", "choice_2", "choice_3" etc. The cells is the name of the

Hello All, I'm having some trouble figuring out what the clearest way to plot my k-means clustering result on an my existing MDS. First I performed MDS on my distance matrix (note: I performed k-means on the MDS coordinates because applying a euclidean distance measure to my raw data would have been inappropriate) canto.MDS<-cmdscale(canto) I then figured out what would be my optimum

Dear All, I am not familar with R. I want to use PCA (principal components analysis) and MDS (multidimensional scaling). Can someone tell me which R package I should use for PCA and MDS? I appreciate your help in advance. Ray

What are the limitations on size of matrix for MDS functions? steve -- Steve Antos Priva-Technologies 847-640-9020 x3114 cell (540)409-1231 [[alternative HTML version deleted]]