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.
