similar to: isoMDS results

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

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, 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

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

> 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) > >

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

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

I apply isoMDS to my data, but the result turns out to be bad as the stress value stays around 31! Yeah, 31 ,not 3.1... I don't know if I ignore something before recall isoMDS. My code as follow: m <- read.table("e:/tsdata.txt",header=T,sep=",") article_number <- ts(m, start = 2004,end=2008, frequency = 1 ,names=colnames(m))

Hi to all, 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) > valumod.mds <- isoMDS(valumod.dist) Error in isoMDS(valumod.dist) : zero or negative distance between objects 2 and 4 > Where is the error? Andrea Rossetti. PS I've attached the

Hi I have just (finally) started to poke around in R and wanted to analyse a stream fish dataset with 28 sites and 18 species. When trying to follow the Vegan manual to run nmds from distance measures calculated by the vegdist function it turns out that I have two sites (streams) with the exactly the same four species (I have used pres-abs data in this case). When I try to run isoMDS I get an

uhps!! I forgot to tell you that I'm running R 0.99.0 for windows32 on windows 95 osr2. Thank you very much for the latest R-1.0.0 (even if I don't tried it yet)!!! Andrea Rossetti University of Perugia (Italy) Statistical Science Department. ---------- Forwarded message ---------- Date: Tue, 29 Feb 2000 17:03:51 +0100 (MET) From: Andrea Rossetti <rossetti at markov.stat.unipg.it>

Dear all, I'm running a set of nonparametric MDS analyses, using a wrapper for isoMDS, on a 800x800 distance matrix. I noticed that setting the parameter k to larger numbers seriously increases the calculation time. Actually, with k=10 it calculates already longer than for k=2 and k=5 together. It's now calculating for 6 hours, and counting... There is quite a difference between the

Dear List: I have a question regarding an MDS procedure that I am accustomed to using. I have searched around the archives a bit and the help doc and still need a little assistance. The package isoMDS is what I need to perform the non-metric scaling, but I am working with similarity matrices, not dissimilarities. The question may end up being resolved simply. Here is a bit of substantive

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

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