similar to: Multidimensional Scaling with pairwise Fst

Hello, We are trying to find a function to compute "stress" in our multidimensional scaling analysis of a dissimilarity matrix. We've used "dist()" to create the matrix and "cmdscale()" for the scaling. In order to determine the number of dimensions we would like to plot stress vs. dimensions. However, we cannot find a pre-made command. It seems that other

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

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 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

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 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

Hi, (Sorry for the cross-posting.) After manually installing the 32-bit libjack packages on my 64-bit system, the original problem ("Can't connect to JACK") is gone; now I get a whole new set of errors instead :) When I try to start the Kontakt 5 VST from festige (via fst, via WINE), this is what I get: ============================ ----------------yo... lets see...

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

Buenas tardes, Tengo un gráfico de dispersión y quiero indicar cada punto a qué individuo corresponde. ¿Cómo añado estas etiquetas de datos? El código es: res<-cmdscale(fst,k=2) plot(res[,1],res[,2],lwd=2,xlab="x",ylab="y") abline(h=0,v=0,col="blue") Muchas gracias, Gemma [[alternative HTML version deleted]]

Hi, I want to analyse DNA sequence data (mtDNA) in R as in calculate Fst, Heterozygosity and such summary statistics. Package Adagenet converts the DNA sequence into retaining only retaining the polymorphic sites and then calcuates Fst.. but is there any other way to do this? I mean analyse the DNA sequence as it is.. and calculate the statistics? Thanks! [[alternative HTML version deleted]]

Hi Does anyone know if there's a package I can use to do Multidimensional scaling ? Thank's EJ -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at

I've installed (maybe not sucessfully) wine 20041201, fst 1.6 and jack_fst-1.2. I'm using jack-0.99 When I try and execute jack_fst such as: #jack_fst Crystal.dll I receive the following error: err:dosmem:setup_dos_mem Cannot use first megabyte for DOS address space, please report can't connect to JACK I assume that the first error message is from wine. Is that true? I

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.

In applying multidimensional scaling, it seems to me that sometimes the underlying dimensionality of the matrix is 1. However I found a case where cmdscale failed when I tried k=1. Here it is: m<-matrix( c(.5,.81,.23,.47,.61, .19,.5,.06,.17,.28, .77,.94,.5,.74,.85, .53,.83,.26,.5,.64, .39,.72,.15,.36,.5), nrow=5) # BTW I think cmdscale uses only the lower triangle--how to enter only # that

Dear R developers, there appears to be a small problem with function cmdscale: for non-Euclidean distance matrices, using option add=FALSE (the default), cmdscale misses the smallest eigenvalue. This affects GOF statistic g.1 (See Mardia, Kent + Bibby (1979): Multivariate Analysis, eq. (14.4.7). The corresponding formula in Cox + Cox (2001): Multidimensional Scaling, 2nd ed., p 38, would

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

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

I have a dissimilarity dataset with the form: 1 1 dissimilarity value 1 2 ... 1 3 1 4 2 2 2 3 2 4 ... I would like to do nonmetric multidimensional scaling with this data, but I am having trouble using this format. I would like to either find a function that accepts this format or find a way to easily convert this format to a matrix for use with existing functions. Thanks!

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

Hi all, Does anyone know if there's R code available for doing Hybrid Multidimensional Scaling (or Semi-Strong HMDS, e.g. Belbin 1991 J. Veg. Sci. 2:491-)? I've found only commercial software that does it. Thanks, Anni