similar to: MDS size limitations

Hi all, I'm trying to perform an MDS of some data that I have. When I use cmdscale everything is fine and I get some interesting results however, the tends to be low. What I wnat to do is compare this with the Non-Metric MDS using isoMDS or sammon. However, when I try using these I get the following message. Error in isoMDS(x.dist) : zero or negative distance between objects 2 and 4

Hi, guys. Have a strange problem with LLVM in my project ( https://github.com/ababo/AntOS). Cannot instantiate JIT execution engine (NULL returns; message: Interpreter has not been linked in.). As you can see from the code I call InitializeNativeTarget. Also I tried to directly include the "llvm/ExecutionEngine/JIT.h" header, but with no success. I link with `llvm-config --ldflags

Isn't there someone to help me with this issue? I'm very upset about this stupid problem which wasted the whole day. BTW, I can create JIT from main.cpp, but not in the required source file (runtime.cpp), so this is not about linking. Very weird. 2011/9/2 Semion Prihodko <semion.ababo at gmail.com> > I cannot call the constructor explicitly, because ForceJITLinking is a name

Dear all, I'm trying to get a two dimensional embedding of some data using different meythods, among which princomp(), cmds(), sammon() and isoMDS(). I have a problem with sammon() because the coordinates I get are all equal to NA. What does it mean? Why the method fails in finding the coordinates? Can I do anything to get some meaningful results? Thank you very much Domenico

Hi again Sorry, in looking again at sammon and isoMDS I see that they seem to do exactly what I want, except that they are non-metric, which means, as I understand it, that they relate the rank orders of the variables rather than the actual distances. Could I use these non-metric MDS packages even if my distances are metric? Thanks Karen -- Karen Kotschy Centre for Water in the Environment

Hola, alguien me puede decir cómo descargar la librería "kohonen", pues lo he intentado con download.packages y no hay forma. No sé si es porque pongo mal el directorio de destino o porque esa librería esta dentro de otra y no la localizo. Ya he usado en otras ocasiones el comando download.packages y no me había dado problemas gracias [[alternative HTML version deleted]]

I see two problems in your code 1) you need to #include "llvm/ExecutionEngine/JIT.h" 2) you must pass an empty string to EngineBuilder::setErrorStr. See JIT::createJIT for the reason. Jeff On Sat, Sep 3, 2011 at 8:33 AM, Semion Prihodko <semion.ababo at gmail.com> wrote: > Isn't there someone to help me with this issue? I'm very upset about this > stupid problem

Hi, I need math function which is used in: isoMDS, Sammon and metaMDS method. Anybody know where I may find it? Any manual or webside? I would be very happy Thanks a lot ! -- View this message in context: http://n4.nabble.com/math-function-MDS-method-tp1008294p1008294.html Sent from the R help mailing list archive at Nabble.com.

Hi All, I am still learning R, but making, IMO, great strides. I learned about Kohonen/Self-Organizing Maps in class and I would like to try to replicate some of the things we have seen in class. Below is my code. I am trying to create a u-matrix. In the documentation on page 9 it appears the type of plot, dist.neighbours should do the trick, however, I am getting an error: (Error in

I would really like some or any advice on how I can improve (or fix??) the following analysis. I hope I have provided a completely runnable code - it doesn't produce any errors for me. The resulting plot at the end shows a pretty poor correlation (just speaking visually here) to the test set. How can I improve the performance of the mapping and prediction? Here are some of the data

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

Does anyone know of any SOM library for R? or any stand alone freeware? A search from google returns SOM_PAK 3.1 developed at Helsinki University of Technology. Is there newer version? Jun -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

All, The kohonen predict function is returning NA for SOM predictions regardless of data used... even the package example for a SOM using wine data is returning NA's Does anyone have a working example SOM. Also, what is the purpose of trainY, what would be the dependent data for an unsupervised SOM? As may be apparent to you by my questions, I am very new to kohonen maps and am very grateful

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

I hit this while testing HMM with nouveau on linux-5.5-rc5. I'm not a lockdep expert but my understanding of this is that an invalidation callback could potentially call kzalloc(GFP_KERNEL) which could cause another invalidation and recursively deadlock. Looking at the drivers/gpu/drm/nouveau/nvkm/ layer, I do see a number of places where GFP_KERNEL is used for allocations and I don't see

On Wed, Jan 13, 2016 at 03:38:24PM -0800, Philip Reames wrote: > I don't think that arbitrary limiting the complexity of the search is the > right approach. There are numerous ways the LVI infrastructure could be > made more memory efficient. Fixing the existing code to be memory efficient > is the right approach. Only once there's no more low hanging fruit should > we

Hello I will be applying MDS (actually Isomap) to make a psychological "concept map" of the similarities between N concepts. I would like to scale 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

Hi, I'm trying to utilize the kohonen package to build SOM's. However, trying this on my data I get the error: "Argument data should be numeric" when running the som(data.train, grid = somgrid(6, 6, "hexagonal")) function. As you see, there is a problem with the data type of data.train which is a list. When I try to convert it to "numeric" I get the error:

I have a data set with 10 variables, and about 8000 instances (or objects/rows/samples). In addition I have one more ('class') variable that I have about 10 instances for, but for which I wish to impute values for. I am a little confused how to go about doing this, mostly as I'm not well-versed in it. Do I train the SOM with a data object that contains just the first 10 variables