similar to: factor analysis (pca): how to get the 'communal

Dear expe-R-ts, I try some test data for a factorAnalysis (resp. pca) in the sense of Prof. Ripley's MASS ? 11.1, p. 330 ff., just to prepare myself for an analysis of my own empirical data using R (instead of SPSS). 1. the data. ## The test data is (from the book of Backhaus et al.: Multivariate ## Analysemethoden. Springer 2000 [9th ed.], p. 300 ff):

Dear all, I have a question about the 'sdev' value returned by the princomp function (which does principal components analysis). On the help page for princomp it says 'sdev' is 'the standard deviations of the principal components'. However, when I calculate the principal components for the USArrests data set, I don't find this to be the case: Here is how I

I am doing Principal Component Analysis (PCA) on assets data for household income prediction. The problem is that the assets data are rank ordered (usually binary ... possess car/don't possess car), so the normal correlation is inappropriate for the calculation of the PCA. Instead one has to use the polychoric correlation coefficient. It uses the "random.polychor.pa" package.

My previous post about prcomp and princomp was done in some haste as I had long ago indicated to Kurt that I would try to have this ready for the June release, and it appeared that I would miss yet another release. I also need to get it out before it becomes hopelessly buried by other work. Brian Ripley kindly pointed out some errors, and also pointed out that I was suggesting replacing some

Hello, I have a short question about the prcomp function. First I cite the associated help page (help(prcomp)): "Value: ... SDEV 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). ROTATION the matrix of variable loadings

A bit of a newbee to R and factor rotation I am trying to understand factor rotations and their implementation in R, particularly the GPArotation library. I have tried to reproduce some of the examples that I have found, e.g., I have taken the values from Jacksons example in "Oblimin Rotation", Encyclopedia of Biostatistics

Last week I asked about data ellipses with rgl:::ellipse3d() with lines showing the principal axes. (The goal is a visual demonstration of PCA as a rotation of variable space to component space.) I was trying, unsuccessfully, to use princomp() to generate the PCA axes and plot them using segments3d: > > PC <- princomp(trees) > > sdev <- PC$sdev # component standard

Dear R-devel list members, Ben Fairbank draw it to my attention that factanal() (in the stats package) doesn't report factor correlations for oblique rotations. Looking at the source, I see that factanal also doesn't save the factor-transformation (rotation) matrix from which these correlations can be computed. I've modified the source, attached below, so that the transformation

Dear R-helpers, Using the package Lattice, I performed a PCA. For example pca.summary <- summary(pc.cr <- princomp(USArrests, cor = TRUE)) The Output of "pca.summary" looks as follows: Importance of components: Comp.1 Comp.2 Comp.3 Comp.4 Standard deviation 1.5748783 0.9948694 0.5971291 0.41644938 Proportion of Variance 0.6200604

--=-hiYzUeWcRJ/+kx41aPIZ Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: 8bit Hi, on March 10 I filed a wishlist bug report asking for the inclusion of some changes to factanal() and the associated print method. The changes were originally proposed by John Fox in 2005; they make print.factanal() display factor correlations if factanal() is called with rotation =

Hi, Try: dat1<- t(dat[,-1]) ?colnames(dat1) <- dat[,1] covmat <- cov(dat1) A.K. I'm running into this error and I'm not sure how fix it Error: is.numeric(x) || is.logical(x) is not TRUE This is my data frame geneExpression_Lab10.txt This is the homework instructions in case you want to see Lab10.pdf This is my code dat <-read.delim("~/Dropbox/Homework/R

R version: 1.7.1 OS: Red Hat Linux 7.2 When "covmat" is supplied in princomp(), the output value "center" is all NA's, even though the input matrix was indeed centered. I haven't read anything about this in the help file for princomp(). See code below for an example: pc2$center is all NA's. Jerome Asselin x <- rnorm(6) y <- rnorm(6) X <- cbind(x,y)

On p. 48 of "Statistics Complements" to the 3rd MASS edition, http://www.stats.ox.ac.uk/pub/MASS3/VR3stat.pdf I read that the orthogonal rotations of Z Lambda^-1 remain uncorrelated, where Z is the PC and Lambda is the diag matrix of singular values. However, the example below that text is > A <- loadings(ir.pca) %*% diag(ir.pca$sdev) If ir.pca$sdev are the singular values,

Dear List, First off, my deepest gratitude to the Sweave developers: this tool has improved my quality greatly. A question in my work I use \Sexpr{} statements scalar values and the xtable package for all manner of tables. What I'd like to do is to use a vector inline, rather than a whole separate table. Something like: %%%%%%%%%%%%%%%% begin code % Latex junk % Sweave block:

Dear R-helpers, I´m in the context of writing a general function for error propagation in R. There are somehow a few questions I would like to ask (discuss), as my statistical knowledge is somewhat restricted. Below is the function I wrote, the questions are marked. Many thanks in advance. propagate <- function(expr, varList, type = c("stat", "raw"), cov = TRUE) {

After doing a PCA using princomp, how do you view how much each component contributes to variance in the dataset. I'm still quite new to the theory of PCA - I have a little idea about eigenvectors and eigenvalues (these determine the variance explained?). Are the eigenvalues related to loadings in R? Thanks, Paul -- View this message in context:

Good day all. This is to thank all those who have helped in fixing this problem. Starting with a text book was indeed a problem, however, that gave me a clue of what I was looking for. This, with your contributions added to other materials I got on the net, put me on the right track. Thank you so much. Warmest regards Ogbos On 31 January 2010 14:07, S Ellison <S.Ellison@lgc.co.uk> wrote:

Hi, I had a look at the MASS4 scripts in the MASS package, in Ch 11.3 Factor Analysis, there is a section of codes like: data(ability.cov) ability.FA <- factanal(covmat = ability.cov, factors = 1) ability.FA (ability.FA <- update(ability.FA, factors = 2)) #summary(ability.FA) round(loadings(ability.FA) %*% t(loadings(ability.FA)) + diag(ability.FA$uniq), 3)

Hi, I am learning how to do principal component analysis in R. However, since I am family with only a few built-in functions like prcomp, sd, cor, I started manually with examples in text books while trying to use the few functions I know to manipulate what they have in the text. From the example in the text I obtained a data set. Using cor and cov, I calculated the correlation and covariance of

Dear All, The help for prcomp, under "Value" says: sdev: the standard deviation of the principal components (i.e., the eigenvalues of the cov matrix, though the calculation is actually done with the singular values of the data matrix). The way I read it, it implies that the sdev are the eigenvalues, but I think that sdev is actually the square root of the