similar to: A small error in mca ?

Dear all, I tried out the example in the help document for mca (the multiple correspondence analysis of the MASS package): farms.mca <- mca(farms, abbrev=TRUE) farms.mca plot(farms.mca) But the graphic that I get seems unfeasible to me: I cannot recognize the numbers (printed in black) because they are all overlapping and concealing each other. I don ?t dare using my own data, which

Hello, I'm an absolute beginner with R and neophite in data analysis, so please bear with me if I ask stupid question. I'm trying to do a correspondence analysis using R and MASS corresp function, but I get an error message which I'm unable to interpret: > data(weblog) > library(MASS) > corresp(~ url + fromurl, data=weblog) Error in svd(t(t(x1 * Dr) * Dc)) : error 306 in

Greetings. I'm about to embark on my first big (to me at least!) R project, which will be to write a function to plot three-dimensional multiple correspondence analysis (mca) plots in a manner similar to scatterplot3d(). (plot.mca() plots only two dimensions, even though mca() will calculate more.) Before I do so, however, I would love to know that I'm not reinventing the wheel or any

Dear list: I have tried MASS's mca function and SAS's PROC corresp on the farms data (included in MASS, also used as mca's example), the results are different: R: mca(farms)$rs: 1 2 1 0.059296637 0.0455871427 2 0.043077902 -0.0354728795 3 0.059834286 0.0730485572 4 0.059834286 0.0730485572 5 0.012900181 -0.0503121890 6

Enio Jelihovschi" eniojelihovs@gmail.com Date: Fri, 20 Oct 2006 11:28:12 -0200 Subject: CORRESPONCE ANALYSIS Dear All I am new R user, trying to do correspondence analysis using the function mca of the package MASS. My question is: In the following example farms.mca <- mca(farms, abbrev = T) # Use levels as names plot(farms.mca, cex = rep(0.7, 2), axes = F) How can I change the

bonjour, Je voudrais faire une analyse des correspondances multiples avec R. avec les repr?sentation graphiques correspondantes avec R. je ne sais pas comment proc?der .. en vour remerciant par avance Faouzi

Lately I'm getting this error quite a bit: Error in La.svd(x, nu, nv) : error code 1 from Lapack routine 'dgesdd' I'm running R 2.5.0 on a 64 bit Intel machine running Fedora (8 I think). Maybe the 64 bit platform is more fragile about declaring convergence. I'm seeing way more of these errors than I ever have before. From R-Help I see that this issue comes up from time to

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Hi all, this is probably simple and I'm just doing something stupid, sorry about that :-) I'm trying to convert words (strings of letters) into a fairly small dimensional space (say 10, but anything between about 5 and 50 would be ok), which I will call a feature vector. The the distance between two words represents the similarity of the

Hi, I'm trying to do PCA on a n by p wide matrix (n < p), and I'd like to get more principal components than there are rows. However, svd() only returns a V matrix of with n columns (instead of p) unless the argument nv=p is set (prcomp calls svd without setting it). Moreover, the eigenvalues returned are always min(n, p) instead of p, even if nv is set: > x <-

Hi, I'm just wondering if it is planned to include the Lapack routine DGESDD (and friends) in R-1.4.0? This is faster (supposedly by a factor of ~6 for large matrices) than DGESVD which is currently (R-1.3.1) called by La.svd. And if it is not in the plans yet, is there a chance it could be? I've added it to my local version of R-1.3.1 and so far see a factor of 4 improvement over

--=====================_24736660==_ Content-Type: text/plain; charset="iso-8859-1"; format=flowed Content-Transfer-Encoding: quoted-printable SVD-Error on R 1.1.0 Windows 98 I get the following error applying svd on a positive definite matrix : > sk2 [,1] [,2] [,3] [,4] [,5] [1,] 1.0460139783 0.084356992 -2.810553e-04

I am trying to perform Singular Value Decomposition (SVD) on a Term Document Matrix I created using the 'tm' package. Eventually I want to do a Latent Semantic Analysis (LSA). There are 5677 documents with 771 terms (the DTM is 771 x 5677). When I try to do the SVD, it runs out of memory. I am using a 12GB Dual core Machine with Windows XP and don't think I can increase the memory

Estimados No me resultó, describo paso a paso y adjunto base de datos str((df[,1:2]))# evaluo el tipo de variable salida data.frame': 101 obs. of 2 variables: $ alergia1 : chr "no" "no" "si" "si" ... $ parasitismo1: chr "si" "si" "si" "si" ... #esto es lo que quiero hacer library(MASS) farms.mca <-

Dear R-devel: I could use some advice about matrix calculations and steps that might make for faster computation of generalized inverses. It appears in some projects there is a bottleneck at the use of svd in calculation of generalized inverses. Here's some Rprof output I need to understand. > summaryRprof("Amelia.out") $by.self self.time self.pct

Hello! I suppose this is more a matrix theory question than a question on R, but I will give it a try... I am using La.svd to compute the singular value decomposition (SVD) of a variance matrix, i.e., a symmetric nonnegative definite square matrix. Let S be my variance matrix, and S = U D V' be its SVD. In my numerical experiments I always got U = V. Is this necessarily the case? Or I might

Hello List, i need help for eigen and svd functions. I have a non-symmetric square matrix. These matrix is not positive (some eigenvalues are negative). I want to diagonalise these matrix. So, I use svd and eigen and i compare the results. eigen give me the "good" eigenvalues (positive and negative). I compare with another software and the results are the same. BUT, when i use svd,

Dear All, I want to 'impose' supplementary points to an mca plot -- using V&R MASS library -- and I wonder if anyone had any luck. The book (4th edition) says it can be done using predict.mca but there are no examples provided in the help pages. Would appreciate any help/pointers. Thanks Marwan btw, to Professor Ripley -- the abbrev=TRUE option for labels does not seem to work.

Dear All, Under Debian GNU/Linux La.svd (with method = "dgesdd") sometimes gives the error "Error in La.svd(data, nu = 0, nv = min(nrow, ncol), method = "dgesdd") : LAPACK routine DGESDD gave error code -12" It seems not to depend on the data per se, but on the relationship between numbers of rows and columns. For example, if the number of columns is 100,

Dear R-devel, I had attempted to compile r-devel (dated Oct. 31, 2001) on WinNT with link to ATLAS, with mostly success. However, when I tried the following, I got a visit from Dr. Watson: R : Copyright 2001, The R Development Core Team Version 1.4.0 Under development (unstable) (2001-10-31) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under

At 01:37 PM 5/10/00 +0200, ralle wrote: >Hi, >I have a problem understanding what is going on with eigen() for >nonsymmetric matrices. >Example: >h<-rnorm(6) >> dim(h)<-c(2,3) >> c<-rnorm(6) "c" is not a great choice of identifier! >> dim(c)<-c(3,2) >> Pi<-h %*% c >> eigen(Pi)$values >[1] 1.56216542 0.07147773 These could