similar to: limit of cmdscale function

The function cmdscale tries to copy names from the source to the result. This only works if the source is a matrix. If m is a matrix with labels (rownames) and d is an object of class "dist" with labels, this works: cmdscale(m) ...but with this, there are no labels in the results: cmdscale(d) However, this works: cmdscale(as.matrix(d)) My suggestion is to change, in

I have a dataframe like this (toy example): x y z "a" "a" 0 "a" "b" 1 "a" "c" 2 "b" "a" .9 "b" "b" 0 "b" "c" 1.3 "c" "a" 2.2 "c" "b" 1.1 "c" "c" 0 The observations are from a matrix like this: c 2.2 1.1 0.0 b 0.9 0.0

Hello, I am using 3.0.15pre2. Share definitions: [Grupes] path = /home/Grupes writeable = yes admin users = DOMAIN+administrator valid users = @"DOMAIN+domain users" store dos attributes = yes map hidden = no map system = no map archive = no dos filemode = yes File ACLs before opening: # file: Pazymejimai-forma2.xls # owner: root # group:

I am trying to obtain the Kruskal (1964) secondary least-squares monotonic transformation of a rank variable given 4 categorical variables in order to obtain optimal transformation for regression. The academic problem assigned is to compare R, SPSS (Conjoint Analysis), and SAS' proc transreg in speed and accuracy. Currently, SAS and SPSS are giving similar results, but R's are quite

Dear all, I'm completely new to R and at first I must say that it is a great program! But I have a problem with the function isoMDS from the MASS package. I have this code which I load with source() from a file: x <- c(163.59514923926784, 150.01448475257115, ...... {here are some more values}) x.sort <- sort(x) x.dist <- dist(x.sort) library(MASS) x.mds <- isoMDS(x.dist)

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'm trying to use isoMDS to project a directed graph to 2-dim vectors, but I got an error. #here is the code to create the graph using igraph package and run isoMDS on it. library(igraph) library(MASS) g<-make_graph(c(1,2, 2,3, 2,4, 3,4, 4,5, 5,6, 3,6, 1,6, 2,5),directed=TRUE) dist<-distances(g, mode="out") loc<-isoMDS(dist) # below is content of the dist matrix

Hello everyone, I have a question on isoMDS. My data set (of vegetation) with 210 samples is in this way: Rotfoehrenau Lavendelweidenau Silberweidenau .... 067_Breg.7 0 2 0 .... 071_Dona.4 0 2 6 .... ... I want to do an isoMDS-analysis with the dissimilarity index

If tbl is an object of class 'dist', you can do this: a <- sammon(tbl, k=3) But you can't do this: b <- isoMDS(tbl, k=3) Wouldn't it be sensible to have identical interfaces to sammon() and isoMDS() ? I think all that would be needed is to change this: isoMDS <- function(d, y=cmdscale(d, 2), maxit=50, trace=TRUE) { ...into this: isoMDS <-

Hi, For example: I built a half matrix "w" using a daisy(x, metric = c("euclidean")) http://www.nabble.com/file/p25211016/1.jpg And next I transformed this matrix "w" using isoMDS function, for example isoMDS(w, k=2) and as result I got: http://www.nabble.com/file/p25211016/2.jpg And now I have two questions: 1. If number in matrix w[2, 1] (= 0.41538462) match

Hi, I'm trying to do a non-metric multidimensional scaling using isoMDS. However, I have some '0' distances in my data, and I'm not sure how to deal with them. I'd rather not drop rows from the original data, as I am comparing several datasets (morphology and molecular data) for the same individuals, and it's interesting to see how much morphological variation can be

Hi, this is a second try to post this to the R-help mailing list. The first one has been rejected because of a too large attachment. Now I ask this without attaching the data. If you want to reproduce the results, please contact me directly to get the data. (First mail, rejected:) > Attached there is a 149*149 dissimilarity matrix; it is a file obtained by >

Dear useRs, last week I asked you about a problem related to isoMDS. It turned out that in my case isoMDS was trapped. Nonetheless, I still have some problems with other data sets. Therefore I would like to know if anyone here has experience with how well isoMDS performs in comparison to other non-metric MDS routines, like Minissa. I have the feeling that for large data sets with a high

When I type "isoMDS()" in the R environment, a error shows up: no such function "isoMDS". Why? And what should I do to use isoMDS? Additionally, I cannot install add-on packages. Typing "Rcmd.exe INSTALL e:/VR_7.2-44.zip" results in "'perl' is neither command nor executable file...". Why this happens?Thanks. The setup file I downloaded is

Hi all, I try to calculate the stress of a configuration using the formula in the isoMDS help, but I don't have the same result than the stress returned by isoMDS : > library(mva) > library(MASS) > data(swiss) > swiss.x <- as.matrix(swiss[,-1]) > swiss.dist <- dist(swiss.x) > swiss.mds <- isoMDS(swiss.dist) initial value 2.979731 iter 5 value 2.431486 iter 10

I am starting with a matrix in which rows are vegetation plots and columns are various characteristics including ID# and elevation. I removed elevation and ID columns to avoid having those characteristics influence the distances between points which I calculated using the "dist" command. The resulting distance file was then used in isoMDS. What I want to know is whether I can

> Date: Tue, 29 Feb 2000 17:03:51 +0100 (MET) > From: Andrea Rossetti <rossetti at markov.stat.unipg.it> [Mail to your address is undeliverable!!!] > I do this > > > valumod<-read.table("valu-medie.txt",header=T) > > library(mass) > > library(mva) > > valumod.x <- as.matrix(valumod) > > valumod.dist<-dist(valumod.x) > >

Hello all- I'm having some problems with memory consumption under R. I've tried increasing the appropriate memory values, but it keeps asking for more; I've even upped the heap size to 600M, significantly eating into swap (256M real, 500+M swap). So, performance slows to a crawl. What I'm trying to do is run isoMDS on a 4000x4000 matrix. My first question is, how much memory

Dear R-Help-Team, I tried to use isoMDS-Function of the MASS-Package: Message: Fehler: konnte Funktion "isoMDS" nicht finden (error: could not find function "isoMDS") so I tried to install the package MASS: > utils:::menuInstallPkgs() versuche URL 'http://cran.rakanu.com/bin/windows/contrib/2.8/VR_7.2-45.zip' Content type 'application/zip' length 1598763

I'm not sure this belong here... Package: MASS Version: 6.2-8 I get a core dump when I call isoMDS with an incorrect argument for y. With d as an object of class "dist": > isoMDS(d,2) Program received signal SIGSEGV, Segmentation fault. 0x40229e43 in VR_mds_init_data () at MASS.c:157 --please do not edit the information below-- Version: platform = i586-pc-linux-gnu