similar to: From Distance Matrix to 2D coordinates

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

Hi We have a few questions regarding the use of the "isoMDS" function. When we run "isoMDS" function using 60,000 x 60,000 data matrix, we get the following error message: ------------------------------------ cmdscale(d, k) : invalid value of 'n' Calls: isoMDS -> cmdscale ------------------------------------ We checked the source code of "cmdscale" and

Hi, I'm trying to build a thesaurus that will sensible values for rare words. I suspect the best algorithm to use is k-means although I'm not sure about that -- I would have preferred a k dimensional space with a binary cluster in each dimension so a word can belong to 0..k clusters, but I digress... I can measure the strength of correlation between words fairly easily by counting

Hello, I am struggling to produce an MDS plot using the randomForest package with a moderately large data set. My data set has one categorical response variables, 7 predictor variables and just under 19000 observations. That means my proximity matrix is approximately 133000 by 133000 which is quite large. To train a random forest on this large a dataset I have to use my institutions high

Hi, all I would like to use R to perform k-means clustering on my data which included 33 samples measured with ~1000 variables. I have already used kmeans package for this analysis, and showed that there are 4 clusters in my data. However, it's really difficult to plot this cluster in 2-D format since the "huge" number of variables. One possible way is to project the

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

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

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

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.

I'm looking for someone to explain the difference between these procedures. The function prcomp() does principal components anaylsis, and the function cmdscale() does classical multi-dimensional scaling (also called principal coordinates analysis). My confusion stems from the fact that they give very similar results: my.d <- matrix(rnorm(50), ncol=5) rownames(my.d) <-

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 not familar with R. I want to use PCA (principal components analysis) and MDS (multidimensional scaling). Can someone tell me which R package I should use for PCA and MDS? I appreciate your help in advance. Ray

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

Dear All, I am far from being a guru about parallel programming. Most of the time, I rely or randomForest for data mining large datasets. I would like to give a try also to the gradient boosted methods in GBM, but I have a need for parallelization. I normally rely on gbm.fit for speed reasons, and I usually call it this way gbm_model <- gbm.fit(trainRF,prices_train, offset = NULL, misc =

Hi Ralf, I tried the following > install.packages("RCurl") which went OK, but then same story when I tried to install tseries. > sessionInfo() R version 3.6.1 (2019-07-05) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Debian GNU/Linux 10 (buster) Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.8.0 LAPACK:

Dear All, Probably a one liner, but I am banging my head against the floor. Consider the following DF <- data.frame( x=1:10, y=10:1, z=rep(5,10), a=11:20 ) mn<-names(DF) but then I cannot retrieve a column by doing e.g, DF$mn[2] I tried to play with the quotes and so on, but so far with no avail. Any suggestion is welcome. Cheers Lorenzo

Hi, I have tried to develop multidimensional scaling for 3D space using PCA without success, yet;-) Is there some application ready in R? Cheers, Atte

Dear All, I would like to be able to plot histograms/densities on a semi-log or log-log scale. I found several suggestions online http://tolstoy.newcastle.edu.au/R/help/05/09/12044.html https://stat.ethz.ch/pipermail/r-help/2002-June/022295.html http://www.harding.edu/fmccown/R/#histograms Now, consider the code snippet taken from http://www.harding.edu/fmccown/R/#histograms # Get a random

Apologies for the previous mail (I sent it off too early by mistake). This is the correct example: rm(list=ls()) D_mean<-seq(-5,5,length=100) y<-exp(-D_mean^2/5) pdf("my.pdf") plot(D_mean,y,type="l",yaxt="n",lty=2,lwd=2,col="black", ylab = list(expression(paste(dN/dlogD[agg]," ["*cm^-3*"]"))), xlab = expression(paste(D[agg],"

dear useRs, i created a distance matrix, of certain voltage values. unfortunately, i lost the original values. i am only left with the distance matrix that i created from those values. i wanted to ask that is there a way in R to reverse distance matrix for the original values? thanks in advance eliza [[alternative HTML version deleted]]