Peter, I am not sure if anyone answered your question about doing biplots using polychoric output and PCA.
The biplot.psych example # 3 shows how to do this library(psych) > responses <- table2df(bock.table[,2:6],count=bock.table[,7], > labs= paste ("lsat6.",1:5,sep="”)) > W <- polychoric(responses, smooth=TRUE,global=TRUE,polycor=F, > ML = FALSE, std.err=FALSE,progress=TRUE) #this returns an object with both the correlations and the difficulties #get the object returned by principal (see biplot.psych example 3) p3 <- principal(r = W$rho, nfactors = 3, rotate = "Promax") # works if you actually give it the matrix p3$scores <- factor.scores(responses,p3) #find the scores from the response data set with the p3 pca solution biplot.psych(p3) Bill On Dec 17, 2013, at 2:18 PM, Peter Maclean <pmaclean2...@yahoo.com> wrote: > I have data set with binary responses. I would like to > conduct polychoric principal component analysis (pPCA). I know there are > several packages used in PCA but I could not find one that directly estimate > pPCA and graph the individuals and variables maps. I will appreciate any help > that expand these reproducible scripts. > #How to conduct polychoric principal component analysis pPCA using > #either of these packages > library(psych) > library(FactoMineR) > library(nsprcomp) > > #Bock and Liberman (1970) data set of 1000 observations of the LSAT > #from psych > > data(bock) > responses <- table2df(bock.table[,2:6],count=bock.table[,7], > labs= paste ("lsat6.",1:5,sep="")) > fix(responses) > describe(responses) > #Estimate the polychoric correlation matrix to be used in > #PCA using psych > W <- polychoric(responses, smooth=TRUE,global=TRUE,polycor=F, > ML = FALSE, std.err=FALSE,progress=TRUE) > #Regular PCA using stat, psych and FactoMiner, respectively > #There is no option for including the matrix > princomp(responses, cor=TRUE) #What kind of correlation is used here? > > principal(r = responses, nfactors = 3, rotate = "Promax") > principal(r = W, nfactors = 3, rotate = "Promax") #Do not work > > PCA(responses, scale.unit=TRUE, ncp=3, graph=T) > #How to conduct polychoric principal component analysis using either of #the > above package and producing individual and variable factor maps as #above > > Peter Maclean > Department of Economics > UDSM > > ______________________________________________ > R-help@r-project.org 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. > William Revelle http://personality-project.org/revelle.html Professor http://personality-project.org Department of Psychology http://www.wcas.northwestern.edu/psych/ Northwestern University http://www.northwestern.edu/ Use R for psychology http://personality-project.org/r It is 5 minutes to midnight http://www.thebulletin.org ______________________________________________ R-help@r-project.org 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.