similar to: LDA: normalization of eigenvectors (see SPSS)

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,

Hi all, I can't fathom why the accented "?" in the following ggplot2 graph makes R hangs when using tikzdevice,? whereas it works using simple pdf device. ###### library(tikzDevice) library(ggplot2) options(tikzDefaultEngine = "luatex") tikzLualatexPackages =c( ? "\\usepackage{tikz}\n", ? "\\usepackage[active, tightpage,psfixbb]{preview}\n", ?

Dear R-helpers, I'm using R 2.5.0 under Windows and am trying to use nlsList from nlme 3.1-80 with the selfstart function for the four parametric logistic function. My first test went well, but now I'm trying to do some more sophisticated things and it does not work anymore. I simulate my data from a five parametric logistic function like this:

> 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

I'm only now realizing that we have severe problems with R on our AMD 'Opteron' and 'Athlon64' clients running Redhat Enterprise with all 64-bit libraries (AFAICS). The Lapack problem happens for R-patched and R-devel both on the Opteron and the Athlon64. Here are platform details: o "gcc -v" and "g77 -v" both end with the line gcc version 3.2.3

Hi, I need to find the z-score of the data present in a speardsheet. The values needs to be calculated for each gene across the samples (refer the example). And, it should be a simple thing, but I am unable to do it right now ! The example re the structure of the spreadsheet is - # Example: MyFile <- read.csv( text= "Names,'Sample_1','Sample_2','Sample_3'

Dear all, I have a problem with interactions in lmer. I have 2 factors (garden and gebiet) which interact, plus one other variable (home), dataframe arr. When I put: / lmer (biomass ~ home + garden:gebiet + ( 1|Block), data = arr)/ it writes: /Error in lme4::lFormula(formula = biomass ~ home + garden:gebiet + (1 | : rank of X = 28 < ncol(X) = 30/ In the lmer help I found out that if not

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

Hello list I have been given some Excel sheets with data laid like this: Col1 Col2 A 3 2 3 B 4 5 4 C 1 4 3 I was hoping to import this into R as a csv and then get the mean and SD for each letter in column 1. Could someone give me some guidance on best to approach this? Thanks Iain

Full_Name: Claus Pastor Version: 2.9.2 OS: Windows XP Submission from: (NULL) (82.113.106.4) I use the following function to compute MutualInformation Matrix of factorial members of a dataframe. I use package minet for this: my.MI = function(test) { ## die Diagonale gibt die Entropien, Teile durch entropy von y und du erh?lst den Zusammenhang ## kenne X und sage daraus y voraus require(minet)

Hi, I try to calculate the angle between two first eigenvectors of different covariance matrices of biological phenotypic traits for different populations. My issue here is, that all possibilities to do so seem to normalize the eigenvectors to length 1. Although the helpfile of eigen() states, that using eigen(, symmetric = FALSE, EISPACK =TRUE) skips normalization this is (I guess) not applicable

Dear R Helper, I am writing because I seek to perform a varimax rotation on my Principal Components Analysis (PCA) solution. (I have been performing PCA's using the eigen command in R.) If you can tell me how to perform this rotation when I use the eigen command (or the princomp command) I would be thrilled. Thanks so much! Wendy Treynor Ann Arbor, MI USA

I have the following situation I want to analyse with prcomp. Each subject has a curve called the contrast sensitivity function (CSF). This curve's overall shape is due to the additive output of 3 "channels" (eigenvectors). #this shows 3 SF channels; net CSF = c1 + c2+c3 x<-1:100 c1<-dnorm(x,mean=20,sd=20) c2<-dnorm(x,mean=50,sd=20) c3<-dnorm(x,mean=80,sd=20)

Hi everybody, I have some problems with the function eigen. I have a square matrix and I want to calculate the eigenvalues and eigenvectors. I apply the function eigen and I get it, however when I solve the same problem in Statistica software, I realise that some eigenvectors are the opposite. How can I get the same values? Thanks in advance [[alternative HTML version deleted]]

Hi ... Please could you help with probably a very simple problem I have. I'm completely new to R and am trying to follow a tutorial using R for Force Distribution Analysis that I got from ... http://projects.eml.org/mbm/website/fda_gromacs.htm. Basically, the MDS I preform outputs a force matrix (.fm) from the force simulation I perform. Then, this matrix is read into R and prcomp is

Hi, I was wondering if function eigen() does something different from the function call eigen() in SAS. I'm in the process of translating a SAS code into a R code and the values of the eigenvectors and eigenvalues of a square matrix came out to be different from the values in SAS. I would also appreciate it if someone can explain the difference in simple terms. I'm pretty new to both

Dear listserv, I am trying to perform a principal components analysis and create an output table of the eigenvalues for the dependent variables. What I want is to see which variables are driving each principal components axis, so I can make statements like, "PC1 mostly refers to seed size" or something like that. For instance, if I try the example from ?prcomp > prcomp(USArrests,

Hi, How the eigenvectors output by the eigen() function are ordered. The first column corresponds to the largest eigenvalue? or is the last column as in Octave? I'm performing a spatial-temporal analysis of some climatic variables so my matrices are MxN (locations*time)and I'm looking for the leading EOF's. As I have understand the eigenvectors columns represent those EOF's

Hello, I am working on a variable selection problem and I wonder whether there is some function or package in R works similar to the 'PROC REG' in SAS? Thank you. Some facts about 'PROC REG': PROC REG in SAS first composes a crossproducts matrix. The matrix can be calculated from input data, reformed from an input correlation matrix, or read in from an SSCP data set. For each

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