Thanks for all the help. Juergen Gross supplied a program which does just what Belsley suggested.
Chuck Cleland, John Fox and Andy Liaw all made useful programming suggestions. John Fox asked <<< (1) I've never liked this approach for a model with a constant, where it makes more sense to me to centre the data. I realize that opinions differ here, but it seems to me that failing to centre the data conflates collinearity with numerical instability. >>> Opinions do differ. A few years ago, I could have given more details (my dissertation was on this topic, but a lot of the details have disappeared from memory); I think, though, that Belsley is looking for a measure that deals not only with collinearity, but with several other problems, including numerical instability (the subtitle of his later book is Collinearity and Weak Data in Regression). I remember being convinced that centering was generally not a good idea, but there are lots of people who disagree and who know a lot more statistics than I do. <<< (2) I also disagree with the comment that condition indices are easier to interpret than variance-inflation factors. In either case, since collinearity is a continuous phenomenon, cutoffs for large values are necessarily arbitrary. >>> While any cutoff is arbitrary (and Belsley advises against using a cutoff rigidly) he does provide some evidence of how regression models with different condition indices are affected by them. <<< (3) If you're interested in figuring out which variables are involved in each collinear relationship, then (for centred and scaled data) you can equivalently (and to me, more intuitively) work with the principal-components analysis of the predictors. >>> This would also work. <<< (4) I have doubts about the whole enterprise. Collinearity is one source of imprecision -- others are small sample size, homogeneous predictors, and large error variance. Aren't the coefficient standard errors the bottom line? If these are sufficiently small, why worry? >>> I think (correct me if I am wrong) that the s.e.s and the condition indices serve very different purposes. The condition indices are supposed to determine if small changes in the input data could make big differences in the results. Belsley provides some examples where a tiny change in the data results in completely different results (e.g., different standard errors, different coefficients (even reversing sign) and so on). Peter Peter L. Flom, PhD Assistant Director, Statistics and Data Analysis Core Center for Drug Use and HIV Research National Development and Research Institutes 71 W. 23rd St www.peterflom.com New York, NY 10010 (212) 845-4485 (voice) (917) 438-0894 (fax) ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help