similar to: sum of squared loadings after varimax?

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

Hello again- Thanks to Prof. Ripley for responding to my previous question. I would like to clarify my question using sample code. I will use some sample code taken from ?prcomp Again, I would like to compare the % variance explained by each PC before and after rotation. < code follows > data(USArrests) pca = prcomp(USArrests, scale = TRUE) # proportion variance explained by each

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

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

In comparing the results of princomp and prcomp I find: 1. The reported standard deviations are similar but about 1% from each other, which seems well above round-off error. 2. princomp returns what I understand are variances and cumulative variances accounted for by each principal component which are all equal. "SS loadings" is always 1. 3. Same happens

I'm using R for PCA and? factor analysis. I want to create biplots of varimax rotated factors that color-code points by their classification. My research is on streams that are urban and rural. So, I want to color code them by this classification. If you just do a biplot from prcomp or princomp, you cannot add this color. So, I have used some code developed by a graduate student in our

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

Hi, I am puzzeled with a differing result of princomp in R and FACTOR in SPSS. Regarding the amount of explained Variance, the two results are the same. However, the loadings differ substantially, in the unrotated as well as in the rotated form. In both cases correlation matrices are analyzed. The sums of the squared components is one in both programs. Maybe there is an obvious reason, but I

hi, I have two questions: #first (SPSS vs. R): I just compared the output of different PCA routines in R (pca, prcomp, princomp) with results from SPSS. the loadings of the variables differ vastly! in SPSS the variables load constantly higher than in R. I made sure that both progr. use the correlation matrix as basis. I found the same problem with rotated values (varimax rotation and rtex=T

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

How does one rotate the loadings from a principal component analysis? Help on function prcomp() from package mva mentions rotation: Arguments retx a logical value indicating whether the rotated variables should be returned. Values rotation the matrix of variable loadings (i.e., a matrix whose olumns contain the eigenvectors). The function princomp returns this in the element

The help for prcomp on R 1.0.0 states that the component sdev of the return value is the eigenvalues of the cov matrix. Am I completely mistaken, or should this be the _square root_ of the eigenvalues? Also, the documentation is not very clear about how tol is used to omit components. (The _code_ is clear, though. :-) -- B/H

Hello all, I've run a principal component regression using the PLS package. I then applied varimax rotation (i.e., using http://stat.ethz.ch/R-manual/R-patched/library/stats/html/varimax.html). I cannot figure out how to extract the factor loadings post-varimax. Is there a command to do this? scores(x) does not do it. Thanks and happy holidays -- View this message in context:

Dear Users, I ran factor analysis using R and SAS. However, I had different outputs from R and SAS. Why they provide different outputs? Especially, the factor loadings are different. I did real dataset(n=264), however, I had an extremely different from R and SAS. Why this things happened? Which software is correct on? Thanks in advance, - TY #R code with example data # A little

According to R help: princomp() uses eigenvalues of covariance data. prcomp() uses the SVD method. yet when I run the (eg., USArrests) data example and compare with my own "hand-written" versions of PCA I get what looks like the opposite. Example: comparing the variances I see: Using prcomp(USArrests) ------------------------------------- Standard deviations: [1] 83.732400 14.212402

Hello, not understanding the output of prcomp, I reduce the number of components and the output continues to show cumulative 100% of the variance explained, which can't be the case dropping from 8 components to 3. How do i get the output in terms of the cumulative % of the total variance, so when i go from total solution of 8 (8 variables in the data set), to a reduced number of

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,

Hi everyone, I am trying to get the factor score for each individual case from a principal component analysis, as I understand, both princomp() and prcomp() can not produce this factor score, the principal() in psych package has this option: scores=T, but after running the code, I could not figure out how to show the factor score results. Here is my code, could anyone give me some advice please?

It may depend on which decomposition method you are using, princomp uses eigen whereas prcomp use svd. What does Statistica use? -----Original Message----- From: Williams, Allyson Sent: Tuesday, 3 September 2002 10:20 AM To: r-help at stat.math.ethz.ch Subject: Hello, I'm doing a pca analysis and get unrotated PCA results (using "pca"). I then used "varimax" to