similar to: Eigen values

I am new in R and I want to solve this problem; I have a matrix X (with n-rows and p-colums) my problem is to obtain the products of the vectors of rows and print out only the maximum value and identify the row that gives the maximum value. Thanks Oyeyemi, G.M --------------------------------- Don't pick lemons. [[alternative HTML version deleted]]

Hi everyone, please I'm having problem importing data from Stata and excel. Help me out. Thanks --------------------------------- [[elided Yahoo spam]] [[alternative HTML version deleted]]

Hi all, Recently our statistics students noticed that their Gibbs samplers were crashing due to some NaNs in some parameters. The NaNs came from mvrnorm (Ripley & Venables' MASS package multivariate normal sampling function) and with some more investigation it turned out that they were generated by function eigen, the eigenvalue computing function. The problem did not seem to happen

Dear everyone, I am having problem simulating multivariate data. Though I was able to simulate the data, but finding the variance-covariance matrix of simulated data did not give exact covariance matrix used in simulating the data. Unlike some other packages, like stata, using command "corr2data" will simulate data having the covariance matrix exactly with the specified covariance

I am trying to read a csv file using the code; contol <- read.csv("RBS.csv") This is the error message I got; Error in file(file, "r") : unable to open connection In addition: Warning message: In file(file, "r") : cannot open file 'RBS.csv', reason 'No such file or directory' Where was the mistake? -- OYEYEMI, Gafar Matanmi (Ph.D) Senior

Hi everyone. My problem is about multivariate analysis. Given a multivariate data X, of dimension (n by p), n is the number of observations while p is the number of variables. I wanted a sample of size p+1 that will have its product of the eigen values of the covariance matrix to be the minimum among all the possible samples (n combination (p+1)). I have written a syntax with worked well, but the

Hi, I run into problem when writing a syntax, I don't know syntax that will return true or false if an integer is odd or even. Thanks OYEYEMI, Gafar Matanmi Department of Statistics University of Ilorin P.M.B 1515 Ilorin, Kwara State Nigeria Tel: +2348052278655 Tel: +2348068241885 [[alternative HTML version deleted]]

Hello all, I have a problem of getting the locations of elements in a vector or matrix. I know one can use the command "which.min" or which.max" to get the location of minimum or maximum values in a vector. But is there any command to get the first k minimum values or maximum values in a vector. Thanks --------------------------------- You rock. That's why

Hello, I would like to simulate randomly from a multivariate normal distribution using a correlation matrix, rho. I do not have sigma. I have searched the help archive and the R documentation as well as doing a standard google search. What I have seen is that one can either use rmvnorm in the package: mvtnorm or mvrnorm in the package: MASS. I believe I read somewhere that the latter was

Hi, dear R pros I try to understand eigen(). I have seen, that eigen() gives the eigenvectors normalized to unit length. What shall I do to get the eigenvectors not normalized to unit length? E.g. take the example: A [,1] [,2] V1 0.7714286 -0.2571429 V2 -0.4224490 0.1408163 Calculating eigen(A) "by hand" gives the eigenvectors (example from Backhaus,

Hello, does anybody know another faster function for random multivariate normal variable simulation? I'm using mvrnorm, but as profiling shows, my algorithm spends approximately 50 % in executing mvrnorm function. Maybe some of you knows much faster function for multivariate normal simulation? I would be very gratefull for advices. -- View this message in context:

Dear R-users,                       I have problem or at cross-road on using discrminant analysis for my set of data. All my dependent variables are all categorical, hence the normalty assumption is no longer valid. Is it still right to use discrminant analysis or is there any non-parametric technique or approach to discriminant analysis? Thanks OYEYEMI, Gafar Matanmi Department of Statistics

Dear R-helpers                         I am having problem or reservation analyzing my data using discrminant analysis. The problem is that all my dependent variables are categorical which means that the normalty assumption is no longer valid. Can one still use discrminant analysis for such data? Is there non-parametric technique of doing discrminant analysis in R. Thanks  OYEYEMI, Gafar

Hello, I have a question about multivariate Shapiro-Wilks test. I tried to analyze if the data I have are multivariate normal, or how far they are from being multivariate normal. However, any time I did >mshapiro.test(mydata) I get the message: Error in solve.default(R %*% t(R), tol = 1e-18) : system is computationally singular: reciprocal condition number = 5.38814e-021 I tried

R-3.0.1 rev 62743, binary downloaded from CRAN just now; macosx 10.8.3 Hello, eigen(symmetric=TRUE) behaves strangely when given complex matrices. The following two lines define 'A', a 100x100 (real) symmetric matrix which theoretical considerations [Bochner's theorem] show to be positive definite: jj <- matrix(0,100,100) A <- exp(-0.1*(row(jj)-col(jj))^2) A's being

I would like to deprecate La.eigen. It is used in a few packages (ade4, fpc, gss, mvtnorm and smoothSurv on CRAN), but only in usages where replacing `La.eigen' by `eigen' would call exactly the same code. The reason for wanting to deprecate it is that little-used interfaces tend to get overlooked, e.g. PR#5406, a report on eigen, needed to be applied to La.eigen as well. We have also

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 R-users, Recently, I have come across a weird problem. I run a large number of iterations and at one of the step within each iteration, I calculate the eigen values of a updated covariance matrix. From all my intermediate output, the code freezes after printing out the covariance matrix but before printing out the eigen values. So, obviously it stops at the only step, the eigen() function

Dear Sir, I am an R user. I am in problem to find eigen vectors in R. For the following matrix eigen vectors are not right. I can not understand why?? For the 1st eigen value and 2nd eigen value are same, but the eigen vectors are not same. *HOW CAN I RESOLVE THE PROBLEM??* *>c=matrix(c(1,0,0,1,2,0,-3,5,2),nrow=3,byrow=T)> eigen(c)$values[1] 2 2 1$vectors [,1] [,2]

Sorry to bother everyone, but I've looked in all of the help files and manuals I have and I can't find the answer to this question. I'm doing principle component analysis by calculating the eigen vectors of a correlation matrix that I have that is composed of 21 parameters. I have the eigen vectors and their values that R produced for me but I'm not sure how to tell which