similar to: analytical strategy for MDS/ smacof /dissimilarity matrix

Dear UserR's, The smacof package (see also our PsychoR repository on http://r-forge.r-project.org/projects/psychor/) is uploaded on CRAN. This package provides the following approaches of multidimensional scaling (MDS) based on stress minimization by means of majorization (smacof): - Simple smacof on symmetric dissimilarity matrices - smacof for rectangular matrices (unfolding models) -

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 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

Hi! I have a dissimilarity matrix X and try to compare it with X' = dist(cmdscsale(X,k)). If I increase k, I should expect that the error (or fit) should monotonically decrease, right. Here is a sample code; library(mva) set.seed(12345) x <- as.matrix(dist(matrix(rnorm(100),ncol=10,byrow=T))) # x[1,2]<-x[2,1]<-1000 ## <<--** 1 # x[5,6]<-x[6,5]<-1000 ##

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 } }

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

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 >

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]]

Dear List, This is Elaine, a postgraduate studying in bird distributions in East Asia. I want to calculate Simpson dissimilarity index, based on a presence/absence matrix of bird species in islands in East Asia. (matrix row: 36 islands/matrix column: species ID) (R package vegan to make NMDS and R package betapart) In most papers using vegan for NMDS and betapart for dissimilarity

Dear R-list members, I need an help with the Multidimensional Scaling analysis (MDS). So far I used the cmdscale() command in R, but I did not get the perceptual map I would love to see, and I would like to know if it is possible to get it using R, and if yes how. I also had a look to the functions isoMDS() and sammoc() but with no luck. I summarize the experiment I performed, and I would ask you

Hello, I would like to use my own dissimilarity matrix in a PAM clustering with method "pam" (cluster package) instead of a dissimilarity matrix created by daisy. I read data from a file containing the dissimilarity values using "read.csv". This creates a matrix (alternatively: an array or vector) which is not accepted by "pam": A call

Dear All, I get some problems using the 'phylo' and dissimilarity functions in R. I converted an output from 'hclust' into an order of phylo so as to be able to use the 'consensus' function on it. Each time I submit the consensus codes, my computer hangs. When I tried to see what the contents of the object converted into order phylo is, I get the message

On 2/21/2020 6:10 PM, TomK via samba wrote: > On 2/21/2020 2:24 PM, Rowland penny via samba wrote: >> On 21/02/2020 19:06, torch via samba wrote: >>> Am I missing something?? I don?t see where you are using the ?@? >>> symbol anywhere. >>> Mac is probably interpreting the parameters ?valid users? and ?write >>> list" (correctly, I think ;-) as a

Hi all, I'm trying to perform a hierarchical clustering on some dissimilarity data that I have but the data matrix I have already contains the dissimilarity values. These values are calculated using a separate program. The dissimilarity matrix in complete with no missing values but the hclust, and agnes routines require it in the form produced by daisy or dist. Is there any of converting

On 2/24/2020 12:47 AM, TomK via samba wrote: > On 2/23/2020 2:54 PM, TomK via samba wrote: >> On 2/23/2020 11:44 AM, Rowland penny via samba wrote: >>> On 23/02/2020 16:05, TomK wrote: >>>> On 2/21/2020 9:18 PM, Andrew Bartlett via samba wrote: >>>>> On Fri, 2020-02-21 at 20:48 -0500, TomK wrote: >>>>>> >>>>>>

On 2/21/2020 9:18 PM, Andrew Bartlett via samba wrote: > On Fri, 2020-02-21 at 20:48 -0500, TomK wrote: >> >> >>> >>> "Sadly this really appears to be is a client issue. You see there the >>> string Samba gets, so by the time Samba tries the process it the @ is >>> already interpreted and the string split. >>> >>> Sorry!

Hi, I have a set of nodes and a dissimilarity matrix for them, as well as a csv file in which the diss matrix has been converted to [node_1, node_2, dissimilarity] format. I would like to visualize this as a graph in Euclidean space (that is, similar nodes clumped together in clusters), rather than the seriation visualization given by dissplot(). I am using Network WorkBench for my

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

On 2/21/2020 12:52 AM, TomK via samba wrote: > Hey Guy's, > > When the user is 'joe at mds.xyz' login works fine on Win 10.? Same user > types on a Mac OS gives > > [2020/02/21 00:03:17.050984, 4, pid=12382, effective(0, 0), real(0, 0)] ../source3/smbd/sec_ctx.c:438(pop_sec_ctx) pop_sec_ctx (0, 0) - sec_ctx_stack_ndx = 1 [2020/02/21 00:03:17.051095, 3,

On 2/21/2020 2:24 PM, Rowland penny via samba wrote: > On 21/02/2020 19:06, torch via samba wrote: >> Am I missing something?? I don?t see where you are using the ?@? >> symbol anywhere. >> Mac is probably interpreting the parameters ?valid users? and ?write >> list" (correctly, I think ;-) as a LIST of 3 users: joe, at, mds.xyz >> or bob, at, mds.xyz.