search for: nonmetr

Dear R-helpers, I am running metaMDS in the vegan package, which uses isoMDS in MASS, to perform Nonmetric Multidimentional Scaling (NMDS). I have seen some authors report a p-value for the NMDS ordination based on randomization of the dataset. As I understand it this is meant to compare the stress in your dataset to multiple runs of randomized data. I do not see a way to perform such a test in vegan...

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 multidim...

...ale to a large number of concepts, however, the resulting N*(N-1) pairwise similarities is prohibitive for a user survey. I'm thinking of giving people random subsets of the pairwise similarities. Does anyone have recommendations for this situation? My current thoughts are to either 1) use nonmetric/gradient descent MDS which seems to allow missing data, or 2) devise some scheme whereby the data that are ranked in common by several people is used to derive a scaling factor for each person's ratings. Thanks for any advice,

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 prov...

Hi, I am stuck in one problem when doing nonmetric multidimensional scaling. I use the function 'metaMDS' in the package 'vegan' to work on the presence/absence community data. The problem is when two samples are identical (dissimilarity = 0), metaMDS cannot work with zero dissimilarity. I don't want to delete the duplicates...

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! [[alternative HTML version deleted]]

...urrently has pooled-adjacent-violaters for monotone regression and the hildreth-desopo coordinate descent method for quadratic programming). Status: will grow. -- ca package (simple correspondence analysis with various plotting options). Status: done. -- smacof package (metric and nonmetric multidimensional scaling, individual difference models, rectangular matrices, constrained scaling, metric nearness problem, Shepard-Luce models). Status: mostly done. -- logitfold package (logistic likelihood solution to nonmetric unfolding for binary and multicategory data, mult...

...diagonal, or user-specified constraints; fitting simplex or circumplex) - 3-way smacof for individual differences (including constraints for idioscal, indscal, and identity) - Sphere projections (spherical smacof, primal and dual algorithm). Each of these approaches is implemented in a metric and nonmetric manner including primary, secondary, and tertiary approaches for tie handling. Various 2- and 3D-plots are provided and a package vignette is included. Patrick _______________________________________________ R-packages mailing list R-packages at r-project.org https://stat.ethz.ch/mailman/list...

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

Hello! Very often one can hear that MDS usually ends with two-dimensional solution. Of course, there are methods, like Scree-test (proposed by Kruskal and Wish, 1981), to determine optimal number of dimensions. However, I am trying to find references to this two-dimensional gold-standard. Can anyone point me to authors which explicitly states that two-dimensions are typical and easiest to

Hi all, I have a visualization question regarding sets. My problem is as follows: I have several sets, each having some members (or 'points'). I would like to connect the points, while keeping the sets. For example, if I have 3 sets (Set 1 contains points A, B, C): Set 1: ------ A, B, C Set 2: ----- A, D, E Set 3: ----- C, D, G, H I would like each set to be represented by a circle

...0.28843372 1.46582729 Parviscopa -0.92508363 0.97772972 attr(,"shrinkage") [1] 0.6744097 0.7139844 attr(,"centre") [1] 1.788934e-18 -3.794708e-19 attr(,"old.wa") [1] FALSE > mdsg.mds$call metaMDS(comm = mdsg) > mds1.mds Call: metaMDS(comm = mds1) Nonmetric Multidimensional Scaling using isoMDS (MASS package) Data: wisconsin(sqrt(mds1)) Distance: bray Dimensions: 2 Stress: 9.804838 Two convergent solutions found after 3 tries Scaling: centring, PC rotation, halfchange scaling Species: expanded scores based on ?wisconsin(sqrt(mds1))?...

Hi, I've been using R for a while now but I've got a problem with metaMDS (in the vegan package) that I can't quite figure out. I have a set of proportion data (from 0-1, rows sum to 1) that I apply metaMDS to using the command: nMDS.set=metaMDS(sqrt(test.set),distance="euclidean",k=3,zerodist="add",autotransform=FALSE) I am using a squared-chord distance

Sorry to bother you kind folks again with my questions. I am trying to learn as much as I can about all this, and I will admit that I don't have the proper background, but I hope that someone can at least point me in the correct direction. I have created a test matrix for what I want to do: s1 s2 s3 s4 s5 s1 10 5 0 8 7 s2 5 10 0 0 5 s3 0 0 10 0 0 s4 8 0 0 10 0 s5 7

Dear All, I am struggling with the following problem: I am given a NxN symmetric matrix P ( P[i,i]=0, i=1...N and P[i,j]>0 for i!=j) which stands for the relative distances of N points. I would like use it to get the coordinates of the N points in a 2D plane. Of course, the solution is not unique (given one solution, I can translate or rotate all the points by the same amount and generate

Hi, I don't understand the following. When I create a small artificial set of date information in class POSIXct, I can calculate the mean and the median: a = as.POSIXct(Sys.time()) a = a + 60*0:10; a [1] "2009-03-10 11:30:16 EDT" "2009-03-10 11:31:16 EDT" "2009-03-10 11:32:16 EDT" [4] "2009-03-10 11:33:16 EDT" "2009-03-10 11:34:16

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Alternatively, just type debug(optim) before using it, then step through it by hitting enter repeatedly... When you're done, do undebug(optim). Andy > From: Spencer Graves > > Have you looked at the code for "optim"? If you > execute "optim", it > will list the code. You can copy that into a script file and walk > through it line by line to