similar to: Solving Matrices

Dear R users & Experts, This is just a curiousity, I was wondering why the dominant eigenvetor and eigenvalue of the following matrix is given as the third. I guess this could complicate automatic selection procedures. 0 0 0 0 0 5 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 Please

Hi, I am using eigen to get an eigen decomposition of a square, symmetric matrix. For some reason, I am getting a column in my eigen vectors (the 52nd column out of 601) that is a column of all NAs. I am using the option, symmetric=T for eigen. I just discovered that I do not get this behavior when I use the option EISPACK=T. With EISPACK=T, the 52nd eigenvector is (up to rounding error) a

Hi, I am hoping some one can help with this. I am using nlme to fit a random coefficients model. It ran for hours before returning Error: Singularity in backsolve at level 0, block 1 The model is > plavix.nlme<-nlme(PLX_NRX~loglike(PLX_NRX,PD4_42D,GAT_34D,VIS_42D,MSL_42D,SPE_ROL,XM2_DUM,THX_DUM,b0,b1,b2,b3,b4,b5,b6,b7,alpha), + data=data, + fixed=list(b0 +

Dear R-users, Is there a preferred method for testing whether a real symmetric matrix is positive definite? [modulo machine rounding errors.] The obvious way of computing eigenvalues via "E <- eigen(A, symmetric=T, only.values=T)$values" and returning the result of "!any(E <= 0)" seems less efficient than going through the LU decomposition invoked in

Dear R users, even if this question is not related to an issue about R, probably some of you will be able to help me. I have a square matrix of dimension k by k with alpha on the diagonal and beta everywhee else. This symmetric matrix is called symmetric compound matrix and has the form a( I + cJ), where I is the k by k identity matrix J is the k by k matrix of all ones a = alpha - beta c =

I apologise for not including a reproducible example with this query but I hope that I can make things clear without one. I am fitting some finite mixture models to data. Each mixture component has p parameters (p=29 in my application) and there are q components to the mixture. The number of data points is n ~ 1500. I need to select a good q and I have been considering model selection methods

Dear R-users, I computed determinant of a square matrix "var.r" using the SVD output: detr _ 1 d _ svd(var.r)$d for (i in 1:length(d)) { detr _ detr*d[i] } print(detr) 30.20886 BUT when I tried : det(var.r) I got : -30.20886 Is this because SVD output will only give absolute of the eigenvalues ?, If this is the case how can I get the original eigenvalues? Thanks, Agus

Hi all, I'm trying to do model reduction for logistic regression. I have 13 predictor (4 continuous variables and 9 binary variables). Using subject matter knowledge, I selected 4 important variables. Regarding the rest 9 variables, I tried to perform data reduction by principal component analysis (PCA). However, 8 of 9 variables were binary and only one continuous. I transformed the data by

Hello all, I looking at package dse or vars or mAr I know how to simulate a VAR(p) process, my problem is that most of those processes are unstable (not weakly stationary). Do anybody know how to generate a random VAR (or VARMA even better) process that is weakly stationary? Thanks -- View this message in context: http://r.789695.n4.nabble.com/simulating-stable-VAR-process-tp4261177p4261177.html

Hi all, I am trying to use mle() to find a self-defined function. Here is my function: test <- function(a=0.1, b=0.1, c=0.001, e=0.2){ # omega is the known covariance matrix, Y is the response vector, X is the explanatory matrix odet = unlist(determinant(omega))[1] # do cholesky decomposition C = chol(omega) # transform data U = t(C)%*%Y WW=t(C)%*%X beta = lm(U~W)$coef Z=Y-X%*%beta

R friends, I need to find the determinant of this matrix x 1 0 0 1 x 1 0 0 1 x 1 0 0 1 x det yields x^4-3x^2+1 I can then use polyroot to find the roots of the coefficients. The question is about the use of "x", which is what I'm solving for. thanks in advance, and this is a back-burner question. Apologies if I have posted this incorrectly/to the wrong place, I'm a newbie

Hello, When I use det() and qr() on complex matrices the result is in some cases indeterministic. The documentation speaks of numeric matrices (and not of complex matrices) but det() uses qr() which should handle complex matrices correctly. I've also tried using only qr() with similar results. det() returns a value that is not the determinant of the complex matrix (in accordance with

Full_Name: Krzysztof Podgorski Version: R version 2.4.1 (2006-12-18) OS: Windows XP Submission from: (NULL) (130.235.3.79) The function ''det'' works improperly for a singular matrix and returns a non-zero value even if ''solve'' reports singularity. The matrix is very simple as shown below. A <- diag(rep(c(64,8), c(8,8))) A[9:16,1] <- 8 A[1,9:16] <- 8

Hi- I am trying to fit a log function to my data, with the ultimate goal of finding the second derivative of the function. However, I am stalled on the first step of fitting a curve. When I use the following code: FG2.model<-(nls((CO2~log(a*Time)+b), start=setNames(coef(lm(CO2 ~ log(Time), data=FG2)), c("a", "b")),data=FG2)) I get the following error: Error in

Hey, Can anyone answer this question. I am working with really large datasets and most of the programs I have been running take quite some time. I heard that R may be faster in Unix. I sthis true and if so can anyone reccomend which system and requirements may allow things to go faster for? Thanks!! Elizabeth Lawson --------------------------------- [[alternative

Some of you may have seen a message on s-news by Stefan Steinhaus regarding his paper on "Comparison of mathematical programs for data analysis". He compares S-PLUS 4.5 with several other programs. He does not include R in the comparisons. On p. 28 of his report he gives the URL the Auckland site along with URL's for two other systems but comments that "I didn't received

Dear all, I want to compute the eigenvalues of a complex matrix for some statistics. Comparing it to its matlab/octave sibling, I don't get the same eigenvalues in R computing it from the exact same matrix. In R, I used eigen() and arpack() that give different eigenvalues. In matlab/octave I used eig() and eigs() that give out the same eigenvalues but different to the R ones. For real

Hello, Can you get eigenvalues in addition to eigevectors using prcomp? If so how? I am unable to use princomp due to small sample sizes. Thank you in advance for your help! Rebecca Young -- Rebecca Young Graduate Student Ecology & Evolutionary Biology, Badyaev Lab University of Arizona 1041 E Lowell Tucson, AZ 85721-0088 Office: 425BSW rlyoung at email.arizona.edu (520) 621-4005

Dear All, I want to know if there is some easy and reliable way to estimate the number of dominant eigenvalues when applying PCA on sample covariance matrix. Assume x-axis is the number of eigenvalues (1, 2, ....,n), and y-axis is the corresponding eigenvalues (a1,a2,..., an) arranged in desceding order. So this x-y plot will be a decreasing curve. Someone mentioned using the elbow (knee)

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