similar to: CMDS

Over the last couple of years I have written quite a few R programs for various "psychometric" techniques, and I am regularly updating and expanding what is there. I now have (wholly or partially), or have planned -- gifi package (update to homals on CRAN). Code for multiple correspondence analysis, nonlinear principal component analysis, nonlinear multiset canonical

kalish at psy.uwa.edu.au wrote: > > I'm a newbie at R, and can't get libraries to really work. > I did this: > > library(help = mva) > cancor Canonical Correlations > cmdscale Classical (Metric) Multidimensional Scaling > dist Distance Matrix Computation > hclust Hierarchical Clustering

Classification: UNCLASSIFIED Caveats: NONE I am new to R and somewhat to Web server programming. I am a Java programmer, however, and have done quite a bit with X3D (extensible 3D) computer graphics. A statistician that I work with is doing multidimensional scaling (MDS) and provides some x,y,z's which I display using X3D. Currently he is using Permap which more than gets the job done

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

The vegan package has non-metric multidimensional scaling in addition to a variety of other ordination techniques. Regards, Helen Mills Poulos Yale University

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 List, I'm trying to interpret the results of the Kruskal's Non-metric Multidimensional Scaling algorithm (isoMDS, MASS package). The 'goodness of fit' is reported as "The final stress achieved (in percent)". What does this mean exactly? I've tried to google for an answer but I've not come up with a definitive answer. Regards, Dieter -- Dieter Vanderelst

Hi, I just encountered what I thought was strange behavior in MDS. However, it turned out that the mistake was mine. The lesson learned from my mistake is that one should plot on a square pane when plotting results of an MDS. Not doing so can be very misleading. Follow the example of an equilateral triangle below to see what I mean. I hope this helps others to avoid this kind of headache.

Hi, I am currently working on some data and feel that NMDS would return an excellent result. With my current data set however I have been experiencing some problems and cannot carry out metaMDS. I have tried with a few smaller data sets which I created for practice sake and this has worked fine. I think it is the set up of my data set that is causing me trouble. I have 18 columns and 18 rows,

I sent a query to R-Help about the availability of nonmetric multidimensional scaling (MDS) algorithms in R. I would like to thank Tony Rossini, Jonathan Baron, Sundar Dorai-Raj, and Brian Ripley for helpful replies. The gist of the replies is that isoMDS in the MASS library provides Kruskal's method for nonmetric MDS, sammon in the MASS library provides Sammon's nonlinear mapping method

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) -

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

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!

Hello, I read a lot about ordination, but I am still confused... I have data on species presence/absence for 8 different sites and I would like to represent my species and the sites on an ordination plot to see if some species are associated with specific sites. I used metaMDS function, which displays both sites and species and it seems to work well. However why are most people using CA instead of

I'm using R 1.6.2 on a Windows 2000 machine. I've plotted the results of an MDS run labeled by a numerical ID, and color coded by a group code: plot(cv.mds.spr$points, type="n", main="Non-Metric Multidimensional Scaling of SprRun CV Watersheds") text(cv.mds.spr$points, labels = as.character(cv.wshed.id.spr), col = codes(cv.wshed.grp), cex=.75) Question is, how do I

Hello fellow R users, I need to perform Guttman's Smallest Space Analysis, which is a type of Non-metric Multidimensional Scaling. Not wanting to reinvent the wheel i decided to look for a package which may have an implementation of it, but no success at all. Do you guys know of any package that perform SSA? I found 'isoMDS' and 'sammon', both on the MASS package, but though

I'm a newbie at R, and can't get libraries to really work. I did this: > library(help = mva) cancor Canonical Correlations cmdscale Classical (Metric) Multidimensional Scaling dist Distance Matrix Computation hclust Hierarchical Clustering kmeans K-Means Clustering prcomp

-----Messaggio originale----- Da: Michael Camann <camann at babylon.cnrs.humboldt.edu> A: r-help at stat.math.ethz.ch <r-help at stat.math.ethz.ch> Data: marted? 22 agosto 2000 21.02 Oggetto: [R] various ordinations >Colleagues, > >I'm developing a library of functions for community ecology analyses and >have a couple of questions that I've not been able to answer

A colleague has just passed me this interesting address: http://cc.oulu.fi/~jarioksa/softhelp/vegan.html "Vegan: R functions for vegetation ecologists" "Vegan package is intended to help vegetation ecologists and other community ecologists to use R. It is not a completely self-contained package, but it complements other R functions. .../... At this first stage, vegan implements

Hi all, I'm running a multidimensional scaling model on data that are first transformed into a similarity matrix using the program daisy in the cluster package. If I already provide the similarity matrix, the plotting routine will give me the variable names on the plot itself. But, it seems that when I use the daisy program to create the similarity matrix, I can't get the program