Re: [R] factor analysis

I haven't seen an answer to this post, so I thought I would try to generate a response. Regarding your first question (Can i use this factor analysis somehow despite the poor cumulative variance of the first three factors ?), I would ask, for what purpose? And, What are the

[R] factor analysis

Hi there, i´ve trouble understanding the factanal output of R. i am running a a FA on a dataset with 10 variables. i plotted eigenvalues to finde out how many factors to try. i think the elbow is @ 3 factors. here are my eigenvalues: 2.6372766 1.5137754 1.0188919 0.8986154 0.8327583 0.7187473

Re: [R] Factor analysis

I wrote some rough functions for principal factor, principal-components factor, and iterated principal factor analysis. I think they are workable, the same results as stata can be retained. In addition, functions for gls and uls factor analysis is in progress, which is based on the algorithms of

[R] Factor analysis

Hi, is there any other routine for factor analysis in R then factanal? Basically I'am interested in another extraction method then the maximum likelihood method and looking for unweighted least squares. Thanks in advance Sigbert Klinke __

[R] factor analysis and pattern matrix

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

[R] factor analysis and pattern matrix

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

[R] factor analysis using Principal Component Method

Is there any R function to perform factor analysis using Principal Component Method? why factanal() method is always mle ? __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide

Re: [R] factor analysis using Principal Component Method

The 'factor.model.stat' function that is available in the public domain area of the Burns Statistics website may or may not satisfy your needs. Patrick Burns [EMAIL PROTECTED] +44 (0)20 8525 0696 http://www.burns-stat.com (home of S Poetry and A Guide for the Unwilling S User) Mario Alfonso

Re: [R] factor analysis using Principal Component Method

Mario Alfonso Morales Rivera wrote: Is there any R function to perform factor analysis using Principal Component Method? why factanal() method is always mle ? Because PCM is not factor analysis (the two methods fit different models) and R didn't want to take part in the mislabeling

[R] factor analysis - discrepancy in results from R vs. Stata

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 2Uniqueness

Re: [R] factor analysis - discrepancy in results from R vs. Stata

I don't believe promax is uniquely defined. Not only are there differences in the criterion (R allows a choice), it is an optimization problem with multiple local optima. In fact the same is true of factanal, and the first thing to check would be to see if the same FA solution has been found.

[R] factor analysis backwards

Hello! How can I do a factor analysis backwards to get an arbitrary covarianz matrix out of an arbitrary number of generated random variables that have a correlation near zero. Or the same question shorter: How to generate random variables that have a spezial correlation pattern. I would like

Re: [R] factor analysis backwards

more, as described in the posting guide (have you read it?) -- Bert Gunter -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Stefan Premke Sent: Wednesday, April 12, 2006 6:52 AM To: r-help@stat.math.ethz.ch Subject: [R] factor analysis backwards Hello! How

Re: [R] factor analysis backwards

Stefan Premke wrote: Hello! How can I do a factor analysis backwards to get an arbitrary covarianz matrix out of an arbitrary number of generated random variables that have a correlation near zero. Or the same question shorter: How to generate random variables that have a spezial

[R] Factor Analysis

I am very new to factor analysis as well as R. I am trying to run a factor analysis on the residual returns on common stock (residual to some model) and trying to determine if there are any strong factors remaining. After running factanal, I can obtain the factor loadings but how do I get

Re: [R] Factor Analysis

On Fri, 27 Jan 2006, Krish Krishnan wrote: I am very new to factor analysis as well as R. I am trying to run a factor analysis on the residual returns on common stock (residual to some model) and trying to determine if there are any strong factors remaining. After running factanal, I can

Re: [R] Factor Analysis functions...

hi all, In the library ‘ade4’, there are two eigenanalysis which enable the ordination of the categorical variables. 1- Multiple Correspondence Analysis (MCA, Tenenhaus Young 1985) performs the multiple correspondence analysis of a factor table (see the function ‘dudi.acm’). this function is

[R] Factor Analysis functions...

Hi all -- I'm running a Factor Analysis on my dataset, and I've located the factanal() and princomp() methods. I don't want to do a PCA, so it looks like I should use factanal(), but factanal() requires specifying the number of factors you expect from the analysis. Are there any

Re: [R] Factor Analysis Biplot

On Fri, 2005-04-15 at 12:49 +1200, Brett Stansfield wrote: Dear R Dear S, When I go to do the biplot biplot(eurofood.fa$scores, eurofood$loadings) Error in 1:p : NA/NaN argument Potential sources of error (guessing: no sufficient detail given in the message): - you ask scores from

[R] Factor Analysis Biplot

Dear R help I am having difficulty doing a biplot of the first two factors of a factor analysis. I presume it is because the values in factor 2 for Milk and NUTS are not displayed in the component loadings. Loadings: Factor1 Factor2 RedMeat0.561 -0.112 WhiteMeat 0.593 -0.432

[R] Factor Analysis Biplot

Dear R When I go to do the biplot biplot(eurofood.fa$scores, eurofood$loadings) Error in 1:p : NA/NaN argument I think this is because the component loadings don't show values for some variables Loadings: Factor1 Factor2 RedMeat0.561 -0.112 WhiteMeat 0.593 -0.432 Eggs

[R] Factor analysis with dichotomous variables

Hello, I would like to conduct an exploratory factor analysis with dichotomous data. Do any R routines exist for this purpose? I recall reading something about methods with tetrachoric correlations. Any help would be appreciated. Best, Tom Denson Department of Psychology University of Southern

RE: [R] Factor analysis with dichotomous variables

:31 PM To: [EMAIL PROTECTED] Subject: [R] Factor analysis with dichotomous variables Hello, I would like to conduct an exploratory factor analysis with dichotomous data. Do any R routines exist for this purpose? I recall reading something about methods with tetrachoric correlations. Any help would

Re: [R] Factor analysis with dichotomous variables

] [mailto:[EMAIL PROTECTED] On Behalf Of Tom Denson Sent: Friday, December 17, 2004 12:31 PM To: [EMAIL PROTECTED] Subject: [R] Factor analysis with dichotomous variables Hello, I would like to conduct an exploratory factor analysis with dichotomous data. Do any R routines exist for this purpose

[R] factor analysis package

Hello everyone, is there a package/packages for factor analysis, particularly PCA? thanks, Katja Katja Löytynoja Taitoniekantie 9 A 218 40 740 Jyväskylä Finland tel.+35814 608058 cell.+35850 336 0174 [EMAIL PROTECTED] __ [EMAIL PROTECTED]

Re: [R] factor analysis package

Katja Loytynoja [EMAIL PROTECTED] writes: Hello everyone, is there a package/packages for factor analysis, particularly PCA? help.search(factor analysis) help.search(principal components) (Whether PCA qualifies as factor analysis is debatable, though...) -- O__ Peter Dalgaard

Re: [R] factor analysis package

R comes with support for factor analysis and PCA (*not* the same thing) in package stats which is normally loaded. Try help.search(factor analysis) help.search(principal components) On Tue, 15 Jun 2004, Katja Loytynoja wrote: Hello everyone, is there a package/packages for factor analysis,

Re: [R] factor analysis

Hello: The website http://ourworld.compuserve.com/homepages/jsuebersax/tetra.htm might provide you with further hints and information on implementing polychoric correlations. Further information related to your inquiry can also be found on http://www.unt.edu/rss/class/rich/5840/ In addition,

Re: [R] factor analysis

Dear Allan, I assume that the categorical data are ordinal. There are methods for factor analyzing ordinal data (e.g., using polychoric correlations) and mixed ordinal and interval data, but as far as I know, these aren't implemented in R. John On Thu, 13 May 2004 18:32:11 +0200 allan clark

Re: [R] factor analysis

hi all, In the library ‘ade4’, there are two eigenanalysis which enable the ordination of the categorical variables. 1- Multiple Correspondence Analysis (MCA, Tenenhaus Young 1985) performs the multiple correspondence analysis of a factor table (see the function ‘dudi.acm’). 2- the “mixed

[R] Factor Analysis

Hello, I am encountering a problem while doing factor analysis in R. I am using correlation matrix of the performance data of funds.And it gives me error message saying singular matrix in use. Now when I try to find the determinant of this matrix it is indeed singular. The problem is when I use

Re: [R] Factor Analysis

To obtain an nonsingular estimate of an (n x n) covariance or correlation matrix, you need at least (n+1) observations. However, you can obtain estimates of the largest k singular values or eigenvalues with only (k+1) observations. The principal components routine must use something like

Re: [R] factor analysis (pca): how to get the 'communalities'?

Brett Magill schrieb: | If interested, on my web site I have code to do factor analysis by PC. Does | exactly as below, but a nice wrapper to print methods, rotations, sorting, and | other conveniences. | | home.earthlink.net/~bmagill/MyMisc.html | | The relevant code snipets are prinfact,

Re: [R] factor analysis (pca): how to get the 'communalities'?

On Fri, 3 Jan 2003, Wolfgang Lindner wrote: I try some test data for a factorAnalysis (resp. pca) in the sense of Prof. Well, factor analysis and pca are different things, and only one is appropriate in a given problem. Ripley's MASS § 11.1, p. 330 ff., Eh? Would that be *Venables

Re: [R] factor analysis (pca): how to get the 'communalities'?

Scot, thank you very much for your wonderful clear and short fix of my first problem: seeing your solution as one-liner in the impressive insightful syntax of R is really an aesthetic experience for me: | I ran your example and found that you can get the eigenvalues SPSS by [..] |

Re: [R] factor analysis (pca): how to get the 'communalities'?

If interested, on my web site I have code to do factor analysis by PC. Does exactly as below, but a nice wrapper to print methods, rotations, sorting, and other conveniences. home.earthlink.net/~bmagill/MyMisc.html The relevant code snipets are prinfact, plot.pfa, and print.pfa, along with

[R] factor analysis (pca): how to get the 'communalities'?

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.: