similar to: Factor rotation (e.g., oblimin, varimax) and PCA

We have just put a package GPArotation on CRAN. The functions in this package perform an number of different orthogonal and oblique rotations for factor analysis, using the gradient projection algorithm described in Coen A. Bernaards and Robert I. Jennrich (2005), "Gradient Projection Algorithms and Software for Arbitrary Rotation Criteria in Factor Analysis, ", Educational and

We have just put a package GPArotation on CRAN. The functions in this package perform an number of different orthogonal and oblique rotations for factor analysis, using the gradient projection algorithm described in Coen A. Bernaards and Robert I. Jennrich (2005), "Gradient Projection Algorithms and Software for Arbitrary Rotation Criteria in Factor Analysis, ", Educational and

I am running a PCA, but would like to rotate my data and limit the number of factors that are analyzed. I can do this using the "principal" command from the psych package [principal(my.data, nfactors=3,rotate="varimax")], but the issue is that this does not report scores for the Principal Components the way "princomp" does. My question is: Can you get an

Dear all I'm unable to find an example of extracting the rotated scores of a principal components analysis. I can do this easily for the un-rotated version. data(mtcars) .PC <- princomp(~am+carb+cyl+disp+drat+gear+hp+mpg, cor=TRUE, data=mtcars) unclass(loadings(.PC)) # component loadings summary(.PC) # proportions of variance mtcars$PC1 <- .PC$scores[,1] # extract un-rotated scores of

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:

I am using R mainly for multiple linear regression and principal components analyses and I am quite happy with it. I think it is a worderful work you have done. But I am having a problem when using Varimax for rotating loadings obtaines from a princomp (I use the cor = TRUE option): the variance across the components in the variables does not mantain. Let me explain it with an example in which

Is it possible to retrieve sums of squared loadings after applying varimax rotation? Here's the setup to my problem: I ran PCA using prcomp(). I then applied the Kaiser criterion to retain only the components having eigenvalues >= 1. (I know there's debate about the wisdom of that criterion, but I don't want to get sucked into that.) I then fed the reduced set of components to

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

Hi! I am trying to analyse with R a database that I have previously analysed with SPSS. Steps with SPSS: Factorial analysis Extraction options : I select = Principal component analysis Rotation: varimax Steps with R: I have tried it with varimax function with factanal or with princomp...and the results are different of what I have with SPSS. I think that varimax function is incorporated in

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

hello, subsequently to a NMDS analysis (performed with metaMDS or isoMDS) is it possible to rotate the axis through a varimax-rotation? Thanks in advance. Bernd Panassiti

Hi everyone- I have used several methods to calculate principal components rotated using the varimax procedure. This is simple enough. But I would like to calculate the % of variance explained associated with each PC before and after rotation. factanal returns the % of variance explained associated with each PC but I cannot seem to get it to change after rotation. Many thanks for your

Hello, recently I was looking at the implementation of the "varimax" rotation procedure from the "stats" package and to me it looks quite different from the algorithm originally suggested by Kaiser in 1958. The R procedure iteratively uses singular value decompositions of some matrices whereas Kaiser proposed to iteratively compute rotation matrices between all pairs of

Dear R users the factanal pacakge is always MLE, which package can do varimax rotation with the results from princomp ? thank you yong

Hi: How i can make Varimax and Promax Rotation in R 0.63 thanks Jorge M. A. Magalhães -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at

Dear Folks I need to rotate PCA loadings and scores using R. I have run a pca using princomp and I have rotated PCA results with varimax. Using varimax R gives me back just rotated PC loadings without rotated PC scores. Does anybody know how I can obtain/calculate rotated PC scores with R? Your kindly help is appreciated in advance Francesca [[alternative HTML version deleted]]

--=-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 =

Hello, I need to do a principal component analysis with EQUAMAX-rotation. Unfortunately the function principal() I use normally for PCA does not offer this rotation specification. I could find out that this might be possible somehow with the package GPArotation but until now I could not figure out how to use this in the principal component analysis. Maybe someone can give an example on how to do

I was wondering whether the following inconsistency of the promax rotation function with the results of a promax rotation using SAS should be considered a bug in the promax function of R. Any comments will be highly appreciated. The following is a loading matrix obtained from a varimax rotation in SAS: # Factor loadings after varimax rotation x <- t(array(c(0.78107, 0.35573,

In some documentation for a package I am working on I have > \code{\link[stats]{varimax}} > \code{\link[stats]{promax}} The link to varimax works, but not the one to promax. Promax is an alias under \name{varimax}. This kind of link works within a package, but I'm not sure if it is suppose to work when it is a link to another package. Is this a known limitation or bug, or