similar to: eigen vector question

Hi. I was comparing the components from princomp's loadings and the eigen given the same input. I found that the sign of componenets (+/-) are opposite between the two components (from princmop and eigen) but the magnitudes are identical. Why? Thanks! [[alternative HTML version deleted]]

Consider the following output [R2.2.0; Windows XP] > set.seed(160706) > X <- matrix(rnorm(40),nrow=10,ncol=4) > Xpc <- princomp(X,cor=FALSE) > summary(Xpc,loadings=TRUE, cutoff=0) Importance of components: Comp.1 Comp.2 Comp.3 Comp.4 Standard deviation 1.2268300 0.9690865 0.7918504 0.55295970 Proportion of Variance 0.4456907 0.2780929

Full_Name: Sundar Dorai-Raj Version: 1.8.1 OS: Windows 2000 Professional Submission from: (NULL) (12.64.199.173) I discovered this problem when trying to use princomp in package:mva when a column in my matrix was all zeros and I set cor = TRUE (thus division by 0). Doing so hangs R, never to return. I have to shut down Rterm in the Task Manager and lose all work from the current image. I tracked

Hi all, I am doing bootstrap on a distance matrix, in which samples have been drawn with replacement. After that I do PCA on a resulted matrix, and these 2 steps are repeated 1000 times. pca(x) is a vector where I wanted to store all 1000 PCAs; and x is from 1 to 1000 SampleD is a new matrix after resampling; I am getting the following error message, which I don't understand: ....

Hi all: I have a question on how to go about creating a correlation matrix. I have a huge amount of data....21 variables for 3471 times. I want to see how each of the variables correlate to each other. Any help would be appreciated, including which package and which functions I should use to do this. Thanks, Jessica Higgs Masters Student Department of Meteorology Penn State University

Using R-studio, I am trying to run a structural equation model and I am running into problems with testing my primary model. Once I specify everything and try to run it I get this error: Error in eigen(S, symmetric = TRUE, only.values = TRUE) : 0 x 0 matrix And when I look at the object for my primary model in my workspace, which is created after I specify it, it lists all my model components,

Dear all, I'm using R 2.8.1 under Vista. I programmed a Simulation with the code enclosed at the end of the eMail. After the simulation I want to analyse the columns of the single simulation-runs, i.e. e.g. Simulation[[1]][,1] sth. like that but I cannot address these columns... Can anybody please help? Best, Thomas ############################ CODE ############################

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]

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]]

Hello! When using princomp() for principal components analysis, the resulting loadings matrix differs between R and the output from other programs (S-plus and SAS) The difference is that the sign of some columns in the loadings matrix. The absolute values, however, are the same. This sign difference also exists when comparing the princomp() results with the manually calculated eigen vectors using

... I checked and Brian and I are both right (see bottom for prior mail exchange). Let me explain: ============================================================= 1. Indeed, in principle, princomp allows data matrices with are wider than high. Example: > x1 [,1] [,2] [,3] [,4] [1,] 1 1 2 2 [2,] 1 1 2 2 > princomp(x1) Call: princomp(x = x1) Standard deviations:

... I checked and Brian and I are both right (see bottom for prior mail exchange). Let me explain: ============================================================= 1. Indeed, in principle, princomp allows data matrices with are wider than high. Example: > x1 [,1] [,2] [,3] [,4] [1,] 1 1 2 2 [2,] 1 1 2 2 > princomp(x1) Call: princomp(x = x1) Standard deviations:

Full_Name: cajo ter Braak Version: 2.1.1 OS: Windows Submission from: (NULL) (137.224.10.105) # I would like to attach the matrix C in the Rdata file; it is 50x50 and comes from a geostatistical problem (spherical covariogram) > rm(list=ls(all=TRUE)) > load(file= "test.eigen.Rdata") > ls() [1] "C" "eW" > > sym.check = max(abs(C - t(C))) # should

I am trying to bring data into R from an excel spreadsheet in order to perform several statistical tests on it. I was trying to use odbcConnectExcel in the RODBC package. Once I am connected to the excel file, how do I select rows and columns from the file in order to analysis them in R.

Hi, I am using R to do a principal components analysis for a class which is generally using SPSS - so some of my question relates to SPSS output (and this might not be the right place). I have scoured the mailing list and the web but can't get a feel for this. It is annoying because they will be marking to the SPSS output. Basically I'm getting different values for the component

I have a question about princomp(mva) that I hope isn't too newbie. I used the sample data from Table 1.1 in "Manly (1986/1994) Multivariate Statistical Methods: a primer. Chapman and Hall" on sparrow body measurements. I rescaled the data to mean 0 and SD 1, and the covariance matrix is: V1 V2 V3 V4 V5 V1 1.0000000 0.7349642 0.6618119

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

$ R --version R 1.6.1 (2002-11-01). So I would like to perform principal components analysis on a 16X16 correlation matrix, [princomp(cov.mat=x) where x is correlation matrix], the problem is princomp complains that it is not non-negative definite. I called eigen() on the correlation matrix and found that one of the eigenvectors is close to zero & negative (-0.001832311). Is there any way

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