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
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