Did you try R ? http://lib.stat.cmu.edu/R/CRAN/index.html
It is not that simple, but it is free and it's very powerfull.
Christophe
Greetings
I have an urgent need for a easy to use, free software to calculate the Kaiser-Meyer-Olkin (KMO) measure of matrices with ~1700 observations. I have tried to use Trujillo's m-file (http://www.mathworks.com/matlabcentr...bjectId=12736), but its not working.
Also, in case someone mentions Bartlett's Sphericity Test, the data i work on cannot be normalized, and as far as i know, Bartlett can only be applied to normalized data.
Thanks in advance
Did you try R ? http://lib.stat.cmu.edu/R/CRAN/index.html
It is not that simple, but it is free and it's very powerfull.
Christophe
Last edited by Christophe Genolini; 12-10-2007 at 06:56 AM. Reason: don't want the roll a troll
I have installed R and managed to access my data files through it. However, it seems i have to download a package that contains the KMO test. The list of available packages is quite large (looking here: http://lib.stat.cmu.edu/R/CRAN/index.html). All i found about factor analysis these two:
http://lib.stat.cmu.edu/R/CRAN/src/c...actoMineR.html
http://lib.stat.cmu.edu/R/CRAN/src/c...tions/ifa.html
However none of them have a function to calculate KMO. Any ideas where i could search for more packages? Or any other software?
YOu could look for the R mailing list, if you have software for mailing lists the news group is : gmane.comp.lang.R.general
Trying to configure thunderbird to work on gmane.comp.lang.R.general, and trying to use my yahoo mail with that. Does that work or do i need a pop3 email? Is there any other config on gmane.comp.lang.R.general that i need to connect to it?
Solving this is tougher than i thought Im considering breaking into the statistics lab at uni at night with a pendrive.
I know you can join this mailing list through the web and you don't *have* to use a mailing list program.
Never tried it but this is a function you should try
> http://tolstoy.newcastle.edu.au/R/help/05/12/17235.htmlCode:library(corpcor) ###needs the function pcor.sq kmo.test <- function(df) { cor.sq = cor(df)^2 cor.sumsq = (sum(cor.sq)-dim(cor.sq)[1])/2 library(corpcor) pcor.sq = cor2pcor(cor(df))^2 pcor.sumsq = (sum(pcor.sq)-dim(pcor.sq)[1])/2 kmo = cor.sumsq/(cor.sumsq+pcor.sumsq) return(kmo) }
You can use (never used it either...)
Factor -- a comprehensive factor analysis program. Provides univariate and multivariate descriptive statistics of input variables (mean, variance, skewness, kurtosis), Var charts for ordinal variables, dispersion matrices (user defined , covariance, pearson correlation, polychoric correlation matrix with optional Ridge estimates). Uses MAP, PA (Parallel Analysis), and PA - MBS (with marginally bootstrapped samples) to determine the number of factors/components to be retained. Performs the following factor and component analyses: PCA, ULS (with Heywood correction), EML, MRFA, Schmid-Leiman second-order solution, and Factor scores. Rotation methods: Quartimax, ,Varimax , Weighted Varimax, Orthomin , Direct Oblimin, Weighted Oblimin, Promax, Promaj , Promin, and Simplimax. Indices used in the analysis: dispersion matrix tests (determinant, Bartlett's, Kaiser-Meyer-Olkin), goodness of fit: Chi-Square ,non-normed fit index, comparative fit index, goodness of fit index, adjusted GFI, RMS error of approx, and estimated non-centrality parameter (NCP), reliabilities of rotated components , simplicity indices: Bentler’s, and loading simplicity index. Provides mean, variance and histogram of fitted and standardized residuals, and automatic detection of large standardized residuals.
Last edited by mp83; 12-28-2007 at 04:44 PM.
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