similar to: Difference between prcomp and cmdscale

Hello, I have a dilemma that I'm hoping the R gurus will be able to help resolve. For background: My data is in the form of a (dis)similarity matrix created from taking the inverse of normalized reaction times. That is, each cell of the matrix represents how long it took to distinguish two stimuli from one another-- a square matrix of 45X45 where the diagonal values are all zero (since this

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

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

Full_Name: Laurent Gautier Version: 1.3.0-patched OS: IRIX 6.5 Submission from: (NULL) (130.225.67.199) Hello, The function La.eigen, called by cmdscale in the package mva behaves an unexplicable way (for me). The following lines show what happened. I tried the very same on linux, and it worked fine. >a <- matrix(c(1,2,3,2),3,3) >a [,1] [,2] [,3] [1,] 1 2 3 [2,]

Hi. I know matrices that use distances between places works fine when using cmdscale. However, what about matricies such as: A B C D E A 0 1 23 12 9 B 1 0 10 12 3 C 23 10 0 23 4 D 12 12 23 0 21 E 9 3 4 21 0 i.e. matrices which do not represent physical distances between places (as they would not make sense for real distances such as the one above)

I have a dataframe like this (toy example): x y z "a" "a" 0 "a" "b" 1 "a" "c" 2 "b" "a" .9 "b" "b" 0 "b" "c" 1.3 "c" "a" 2.2 "c" "b" 1.1 "c" "c" 0 The observations are from a matrix like this: c 2.2 1.1 0.0 b 0.9 0.0

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

Hello all, I'm using R1.7.1 on Linux, generating sammon-optimized MDS plots from distance matrices. This is a calculation I run routinely, often on sample sets of up to 100 samples. This time, with three samples, the sammon function returned an error (shown below), which I tracked down to the cmdscale function it uses to find a starting configuration. In short, cmdscale is returning NaN

The function cmdscale tries to copy names from the source to the result. This only works if the source is a matrix. If m is a matrix with labels (rownames) and d is an object of class "dist" with labels, this works: cmdscale(m) ...but with this, there are no labels in the results: cmdscale(d) However, this works: cmdscale(as.matrix(d)) My suggestion is to change, in

Dear people, I used clusplot to plot a partition result. The partition result was from pamk with a distance object as input. Then I applied cmdscale on the same distance object for coordinates to make another scatterplot. My problem is this: the coordinates from the cmdscale calculation, though with the same shape, were different in scale and rotation from the scatter plot yielded by clusplot.

Full_Name: Cam Webb Version: 1.6.0 (fink X11 compile) OS: Mac OS X (Jaguar) Submission from: (NULL) (64.168.28.87) This is an unpredictable, intermittant hang during cmdscale of the mva library. Some data never cause a problem, other data always do, abut I can't track down the difference in the structure of the data. Sometimes the function will work for `difficult' data after it has

Thanks, no malloc or free equivalents either (no heap). So, there are no others (to your knowledge) who have built an LLVM backend for a platform with no “normal” stack? I found a presentation about some FPGA work (using LLVM) but it doesn’t seem to apply to my platform. Perhaps someone else on the mailing list will have come across this rarity? Thank you again for your time and

I'm using the WINDOWS 95 version of R (with midified menu "exe.zip"): I tried to do library(mva) data(quakes) quakes<-quakes[sample(seq(1:1000),100),1:5] cmd.dsq<-cmdscale(dsq) Error in .C("dblcen", x, as.integer(n)) : C/Fortran function not in load table how can I solve this problem? -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-

I'm using rseptbeta for w95, I tried to do: library(mva) data(quakes) quakes<-quakes[sample(seq(1:1000),100),1:5] cmd.dsq<-cmdscale(dsq) Error in .C("dblcen", x, as.integer(n)) : C/Fortran function not in load table how can I solve this problem? Andrea Rossetti _______________________________________________________ Statistica & Informatica per la Gestione delle Imprese

I'm using rseptbeta for w95, I tried to do: > library(mva) > data(eurodist) > cmdscale(eurodist) Error in .C("dblcen", x, as.integer(n)) : C/Fortran function not in load table > how can I solve this problem? Andrea Rossetti _______________________________________________________ Statistica & Informatica per la Gestione delle Imprese | Universit? degli Studi di

Hi. I know matrices that use distances between places works fine when using cmdscale. However, what about matrices such as: A B C D E A 0 1 23 12 9 B 1 0 10 12 3 C 23 10 0 23 4 D 12 12 23 0 21 E 9 3 4 21 0 i.e. matrices which do not represent physical distances between places (as they would not make sense for real distances such as the one

Thanks for your response. Please see below. From: Bruce Hoult [mailto:brucehoult at sifive.com] Sent: Thursday, December 13, 2018 5:58 PM To: jjones at prc-hsv.com Cc: LLVM Developers Mailing List <llvm-dev at lists.llvm.org> Subject: Re: [llvm-dev] LLVM Backend for a platform with no (normal) stack Do you have a register that you can store a memory address >> yes in and

Hello All, The basic premise of what I want to do is the following: I have 20 "entities" for which I have ~500 measurements each. So, I have a matrix of 20 rows by ~500 columns. The 20 entities fall into two classes: "good" and "bad." I eventually would like to derive a model that would then be able to classify new entities as being in "good

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 Sir or Ma'am; I have found a wealth of help and information on writing an LLVM backend. And, my platform has no stack. Can you point me to any information that would specifically address creating a backend for this kind of platform? In previous wanderings, I thought I ran across a phrase "platforms with no stack such as FPGAs", but I can't find that mention, now.