similar to: Factor Analysis with orthogonal and oblique rotation

Hi, In a discussion of factor analysis in "Using Multivariate Statistics" by Tabachnick and Fidell, two matrices are singled out as important for interpreting an exploratory factor analysis (EFA) with an oblique promax rotation. One is the "structure matrix". The structure matrix contains the correlations between variables and factors. However, these correlations may be

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

# I have a question about how factanal is calculating the regression factor # scores based on an oblique rotation (promax) of the factors. # # As is explained in the help file, regression factor scores are # obtained as # # hat f = Lambda' Sigma^-1 x # # However, according to Harman's "Modern Factor Analysis" (e.g. second # edition, pp. 351-352) the formula is # # hat f = Phi

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

--=-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, everyone, This is a little silly, but I cant figure out the algorithm behind lm.fit function used in the context of promax rotation algorithm: The promax function is: promax <- function(x, m = 4) { if(ncol(x) < 2) return(x) dn <- dimnames(x) xx <- varimax(x) x <- xx$loadings Q <- x * abs(x)^(m-1) U <- lm.fit(x, Q)$coefficients d <-

Hi, I found a discrepancy between results in R and Stata for a factor analysis with a promax rotation. For Stata: . *rotate, factor(2) promax* (promax rotation) Rotated Factor Loadings Variable | 1 2 Uniqueness -------------+-------------------------------- pfq_amanag~y | -0.17802 0.64161 0.70698 pfq_bwalk_~ø | 0.72569 0.05570

I am performing a large (105 variable) factor analysis with factanal, specifying promax rotation. I kow that some loadings over 1.0 are not unsual with that rotation, but I have some as large as 1.2, which seems extreme. I am skirting the assumptions of the model by using responses on a 7-point rating scale as data; I may have to go back and compute polychoric correlations instead of product

Full_Name: conor dolan Version: 1.4.0 OS: windows98 Submission from: (NULL) (146.50.170.247) On the basis of Lawley and Maxwell's explanation in Factor Analysis as a Statistical Method (London: Butterworth, 1971), the following line in the promax routine (library, mva): attr(z, "covariance") <- crossprod(U) should be attr(z, "covariance") <- solve(crossprod(U))

Hi, I am learining how to use R, specifically the princomp() function, and I am trying to replicate the results I get in SPSS with no luck, can anyone give me the equivalent options in SPSS' factor analysis routine? Thanks, Adam _____________________________________________________________________ Adam F. Simon Department of Political Science 44 Gowen Hall University of

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 friends, I'm using R to produce the following Factor Analysis: > matriz.cor<-hetcor(matrix(as.factor(data), ncol=variables, byrow=T))$correlations > factanal(x=data, factors=2, covmat=matriz.cor, scores='regression') Then the screen output shows the following message: Error en factanal(x = data, factors = 2, covmat = matrix, : requested scores without

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

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,

Thanks to Brian Ripley for the time and effort put into developing the factanal package for R. I have found it very useful. However, in trying to replicate some results from previous research, I have run into the need for a couple of extensions and was wondering if they might find their way into future implementations. 1. Though maximum likelihood estimates might be more rigorous and

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

Dear R users, I have been doing rotation on Principal components analyse, with varimax function and promax. Following those changes, I cannot find now how to get the spatial evolution and the variance. Indeed, with the function princomp that has been used to get the principal components, arguments such as $scores and $sdev were available to get the spatial evolution and the variance, with

Dear R users, I have been doing some eof analysis using princomp function, then the eof results were rotated with varimax and promax functions. Those functions are working fine. However, after their uses, I cannot find how to obtain the temporal evolution and the variance of the obtain components, as varimax and promax does not have the arguments $scores and $sdev. If any body, know how to

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

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