On Tue, 12 Jun 2007, Spencer Graves wrote: > The problem with applying prcomp to binary data is that it's not > clear what problem you are solving. > > The standard principal components and factor analysis models > assume that the observations are linear combinations of unobserved > "common" factors (shared variability), normally distributed, plus normal > noise, independent between observations and variables. Those > assumptions are clearly violated for binary data. > > RSiteSearch("PCA for binary data") produced references to 'ade4' > and 'FactoMineR'. Have you considered these? I have not used them, but > FactoMineR included functions for 'Multiple Factor Analysis for Mixed > [quantitative and qualitative] Data'
AFAIK, that is not using 'factor analysis' in the same sense as e.g. factanal(). Continuous underlying variables with binary manifest variables is part of latent variable analysis. Package 'ltm' covers a variety of such models. But to begin to give advice we would need to know the scientific problem for which Ranga Chandra Gudivada is looking for a tool. Simon Blomberg mentioned ordination, but that is only one of several classes of uses of PCA (which finds a linear subspace that both has maximal variance within and is least-squares fitting to the data). > > Hope this helps. > Spencer Graves > > Josh Gilbert wrote: >> I don't understand, what's wrong with using prcomp in this situation? >> >> On Sunday 10 June 2007 12:50 pm, Ranga Chandra Gudivada wrote: >> >>> Hi, >>> >>> I was wondering whether there is any package implementing Principal >>> Component Analysis for Binary data >>> >>> Thanks chandra >>> >>> >>> --------------------------------- >>> >>> >>> [[alternative HTML version deleted]] >>> >>> ______________________________________________ >>> R-help@stat.math.ethz.ch mailing list >>> https://stat.ethz.ch/mailman/listinfo/r-help >>> PLEASE do read the posting guide >>> http://www.R-project.org/posting-guide.html and provide commented, minimal, >>> self-contained, reproducible code. >>> >> >> ______________________________________________ >> R-help@stat.math.ethz.ch mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.