A bit late, but you might like to look at http://www.qimr.edu.au/davidD/polyr.R
Regarding the original posters queries: You can analyse polychoric correlations as if they were Pearson correlations using standard software (eg sem), and this usually doesn't do too badly, or go to AWLS (Browne) in LISREL etc, or ML analysis of the full multidimensional contingency table using programs such as Mx, or as you noted, mvtnorm (Mx uses Alan Genz's algorithms). You can check model assumptions, and compare the results to those from similar loglinear models. For example, for a 3-way table, a single factor model based on polychoric correlations should fit "perfectly", if the no higher order interaction assumption is right, | David Duffy (MBBS PhD) ,-_|\ | email: [EMAIL PROTECTED] ph: INT+61+7+3362-0217 fax: -0101 / * | Epidemiology Unit, Queensland Institute of Medical Research \_,-._/ | 300 Herston Rd, Brisbane, Queensland 4029, Australia GPG 4D0B994A v ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
