similar to: eigen bug?

Dear list, usually, I can break a running R process with Ctrl-C. This does not seem to work if R was started from within ESS. Is there anything analogous? Thanks, Christian *********************************************************************** Christian Hennig University of Hamburg, Fachbereich Mathematik - SPST (Schwerpunkt Mathematische Statistik und Stochastische Prozesse, Zentrum fuer

Hi, with small matrices eigen works as expected: > eigen(cbind(c(1,4),c(4,7)), only.values = TRUE) $values [1] 9 -1 $vectors NULL > eigen(cbind(c(1,4),c(4,7))) $values [1] 9 -1 $vectors [,1] [,2] [1,] 0.4472136 -0.8944272 [2,] 0.8944272 0.4472136 > eigen(cbind(c(1,-1),c(1,-1))) $values [1] -3.25177e-17+1.570092e-16i -3.25177e-17-1.570092e-16i $vectors

Dear list, I am faced with the following model: y=E+P+M+H+PxE+Error y is a response, E and P are factors with fixed effects. M is a random effect nested in P and H is a random effect nested in M. PxE is interaction of P and E. It seems that I should fit such a model with the lme function of library nlme, but I was not able to figure out from the help page how to specify the formula. In the

Dear list, I tried to install R-1.1.0 under SuSe Linux 5.2. ./configure --prefix=/usr/applic/packages/R make make install worked without error. make check led to make[1]: Entering directory `/export/old/usr/applic/packages/R/R-1.1.0/tests' make[2]: Entering directory `/export/old/usr/applic/packages/R/R-1.1.0/tests' make[3]: Entering directory

Dear list, the following should fit the model log(PEPC.Wert)=fKTemp+fHerk+interaction(fKTemp,fHerk)+fMub+error, where fKTemp, fHerk are fixed effect factors and fMub is a random effects factor nested in fHerk (values are different fopr different values of fHerk). > logpepcr1 <- lme(log(PEPC.Wert) ~ fKTemp*fHerk, random= ~ 1 | fMub, na.action=na.omit) The following should be without the

Dear list, I use R 1.3.0 on Solaris. Today I realized that the html help search engine does not work at the moment (this means that I always get the search engine page again if I try to specify a search word, and click "search" or one of the prespecified topics), presumably since the latest netscape update. Do you have an idea what can be wrong? Perhaps this must be fixed not by the

Hi R-users, I am trying to estimate function parameters using optim(). My count observations follows a Poisson like distribution. The problem is that I wanna express the lambda coefficient, in the passion likelihood function, as a linear function of other covariates (and thus of other coefficients). The codes that I am using (except data frame) are the following (FYI the parameters need to be

Dear All i have a little puzzle about eigenvector in the R. As we know that the eigenvector can be displayed on several form. For example A=matrix(c(1,2,4,3),2,2) if we want to get the eigenvalue and eigenvector, the code followed eigen(A) $values [1] 5 -1 $vectors [,1] [,2] [1,] -0.7071068 -0.8944272 [2,] -0.7071068 0.4472136 however, we also can calculate the vector matrix

Hi, Why the qr() produces a negative Q compared with Gram-Schmidt? (note example below, except Q[2,3]) Here is an example, I calculate the Q by Gram-Schmidt process and compare the output with qr.Q() a <- c(1,0,1) b <- c(1,0,0) c <- c(2,1,0) x <- matrix(c(a,b,c),3,3) ########################## # Gram-Schmidt ########################## A <- matrix(a,3,1) q1 <-

Full_Name: David Pleydell Version: 2.2.1 OS: Debian Etch Submission from: (NULL) (193.55.70.206) There appears to be a conflict between the chol functions from the Matrix and the SparseM packages. chol() can only be applied to a matrix of class dspMatrix if SparseM is not in the path. with gratitude David > library(Matrix) > sm <- as(as(Matrix(diag(5) + 1), "dsyMatrix"),

How can I remove columns with NaN entries ? Here is my simple example: > data <- read.csv("test.csv") > xdata <- data[3:length(data)] > xs <- lapply(xdata, function(x){(x - mean(x))/sqrt(var(x))}) > x <- data.frame(xs) > x C D E F 1 -0.7071068 NaN -0.7071068 -0.7071068 2 0.7071068 NaN 0.7071068 0.7071068

> Date: Wed, 30 Jun 1999 11:12:24 +0200 (MET DST) > From: Torsten Hothorn <hothorn at amadeus.statistik.uni-dortmund.de> > > yesterday I had a little shock using qr (or lm). having a matrix > > X <- cbind(1,diag(3)) > y <- 1:3 > > the qr.coef returns one NA (because X is singular). So I computed the > Moore-Penrose inverse of X (just from the

Dear all, I've used the 'prcomp' command to calculate the eigenvalues and eigenvectors of a matrix(gg). Using the command 'principal' from the 'psych' package  I've performed the same exercise. I got the same eigenvalues but different eigenvectors. Is there any reason for that difference? Below are the steps I've followed: 1. PRCOMP #defining the matrix

The following seems to be an bug in prcomp(): > test <- ts( matrix( c(NA, 2:5, NA, 7:10), 5, 2)) > test Time Series: Start = 1 End = 5 Frequency = 1 Series 1 Series 2 1 NA NA 2 2 7 3 3 8 4 4 9 5 5 10 > prcomp(test, scale.=TRUE, na.action=na.omit) Erro en svd(x, nu = 0) : infinite or missing values in 'x'

In the manual page for prcomp(), it says that sdev is "the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the covariance/correlation matrix, though the calculation is actually done with the singular values of the data matrix)." ?However, this is not what I'm finding. ?The values appear to be the standard deviations of a reprojection of

Hi everyone, It's a very minor point, but could we ensure that eigen and La.eigen return a *matrix* for the "vectors" component of the list by including a "drop = FALSE", as specified in the help file, ie put list(values = z$values[ord], vectors = if (!only.values) z$vectors[, ord, drop = FALSE]) Thanks, Jonathan. --please do not edit the information

Dear all, It is not a R related problem rather than statistical/mathematical. However I am posting this query hoping that anyone can help me on this matter. My problem is to get the Geometrical Interpretation of Eigen value and Eigen vector of any square matrix. Can anyone give me a light on it? Thanks and regards, Arun [[alternative HTML version deleted]]

Hi, I have a question for my simulation problem: I would like to generate a positive (or semi def positive) covariance matrix, non singular, in wich the spectral decomposition returns me the same values for all dimensions but differs only in eigenvectors. Ex. sigma [,1] [,2] [1,] 5.05 4.95 [2,] 4.95 5.05 > eigen(sigma) $values [1] 10.0 0.1 $vectors [,1]

Dear list, here is something I do not understand about cov.rob. > dat <- rmvnorm(200,rep(0,10),diag(10)) > cov.rob(dat,method="mcd") > cov.rob(dat,method="mcd",quantile.used= floor(3*211/4)) # All fine; default for quantile.used is floor(211/2) > dat <- rmvnorm(20,rep(0,10),diag(10)) > cov.rob(dat,method="mcd") # quantile.used is floor(31/2).

Hi, this is a second try to post this to the R-help mailing list. The first one has been rejected because of a too large attachment. Now I ask this without attaching the data. If you want to reproduce the results, please contact me directly to get the data. (First mail, rejected:) > Attached there is a 149*149 dissimilarity matrix; it is a file obtained by >