similar to: Stress in multidimensional scaling

Hi; I am working with the similarity matrix below and I would like to plot a two-dimensional MDS solution such as each point in the plot has a label. This is what I did: data <- read.table('c:/multivariate/mds/colour.txt',header=FALSE) similarity <- as.dist(data) distance <- 1-similarity result.nmds <- nmds(distance) plot(result.nmds) (nmds and plot.nmds as defined at

Dear R developers and users! I have calculated metric MDS by cmdscale from matrix of distances (dissimilarities). I would like to ask you how can I estimate how well this new mapping represents characteristic features of my data set? Thank you for any suggestions. Best, tomek

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

Dear R users, How to find all single minima within each next part of analyzed vector (table) Select all minima (mass_value=min & mass_value<2) (many) in vector(table), BUT first put mask on table in order to select within one window mask (5 elements) only one local minimum, and next to search within the next time window mask the second minimum (only one local along second mask)

Hello I am using the following command but not able to text the values on the graph can someone please make suggestions for improvement #here is the command loc_mds <- cmdscale(dist.r, k = 7, eig = TRUE) loc_mds$eig sum(abs(loc_mds$eig[1:2]))/sum(abs(loc_mds$eig)) sum((loc_mds$eig[1:2])^2)/sum((loc_mds$eig)^2) x <-loc_mds$points[,1] y <-loc_mds$points[,2] plot(x, y,

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

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

Hi, I need to run a PCoA (PCO) for a data set wich has both positive and negative values for variables. I  could not find any distancecoefficient other than euclidean distace running for the data set. Are there any other coefficient works with negtive values.Also I cannot get summary out put (the eigen values) for PCO as for PCA.   Thanks. Dilshan [[alternative HTML version deleted]]

Hi, I want to know which distance is using in the function kmeans and if we can change this distance. Indeed, in the function pam, we can put a distance matrix in parameter (by the line "pam<-pam(dist(matrixdata),k=7)" ) but we can't do it in the function kmeans, we have to put the matrix of data directly ... Thanks in advance, Nicolas BOUGET

Hi gang, I'm perplexed- I have some code that uses for() loops that works fine in R version 2.8 on my mac, worked fine in version 2.8 on my old windows machine, but doesn't work in version 2.10 on windows. The loop implements a function over a data frame (code is included below). In Mac (running version 2.8), the results of the loop are what I expect: > p_unadj [1] 0.034939481

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 } }

Dear All, I am in the somewhat unfortunate position of having to reproduce the results previously obtained from (non-metric?) MDS on a "kinship" matrix using Statistica. A kinship matrix measures affinity between groups, and has its maximum values on the diagonal. Apparently, starting with a nxn kinship matrix, all it was needed to do was to feed it to Statistica flagging that the

Colleagues, I have a routine in package labdsv that calls a FORTRAN subroutine. Recently, I was informed that it sometimes gives different results on a PC and Mac, and that the PC version is clearly wrong. I tested it on linux (because I don't have a PC), and I get the same (incorrect) behavior as the PC. Simply by inserting debug WRITE statements in the FORTRAN I would get different,

Dear R list Would anyone be able to tell me whether it is possible to do "enhanced multidimensional scaling" (enhanced MDS) in R? In other words, something that goes beyond "cmdscale" by iteratively improving the fit between observed dissimilarities and inter-object distances, using the KYST algorithm (Kruskal, 1964). I have found several implementations of non-metric MDS

Hi, I am working with a species-by-trait .csv file (columns=traits, rows=species) and get the following warning message when trying to plot results of both metaMDS and pcoa:  "Warning message: In ordiplot(x, choices = choices, type = type, display = display,  :   Species scores not available" I am using a Gower's transformation in both procedures within the metaMDS or pcoa

Dear all, I was looking for methods in R that allow assessing the number of significant principal coordinates. Unfortunatly I was not very successful. I expanded my search to the web and Current Contents, however, the information I found is very limited. Therefore, I tried to write code for doing a randomization. I would highly appriciate if somebody could comment on the following approach.

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

Hi, How do you get the most common row from a matrix? If I have a matrix like this array(1:3,dim=c(4,5)) [,1] [,2] [,3] [,4] [,5] [1,] 1 2 3 1 2 [2,] 2 3 1 2 3 [3,] 3 1 2 3 1 [4,] 1 2 3 1 2 in which rows 1 and 4 are similar, I want to find that vector c (1,2,3,1,2). Atte Tenkanen University of Turku, Finland

I have a covariance matrix that is not positive semi-definite matrix and I need it to be via some sort of adjustment. Is there any R routine or package to help me do this? Thanks, Roger [[alternative HTML version deleted]]