Dear Peter,
I'm sorry that I've taken a while to get back to you -- I was away for a
few days.
In the example that you give from Belsley (1991), the predictors are
essentially perfectly linearly related; for example
> summary(lm(x2a ~ x3a + x4a))
Call:
lm(formula = x2a ~ x3a + x4a
Dear John
An interesting discussion!
I would be the last to suggest ignoring such diagnostics as Cook's D;
as you point out, it diagnoses a problem which condition indices do not:
Whether a point is influential.
OTOH, condition indices diagnose a problem which Cook's D does not:
Would shifting t
Dear Peter,
At 08:24 AM 7/24/2003 -0400, Peter Flom wrote:
(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 numeri
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
Dear Peter and Uwe,
I don't have a copy of Belsley's 1991 book here, but I do have Belsley,
Kuh, and Welsch, Regression Diagnostics (Wiley, 1980). If my memory is
right, the approach is the same: Belsley's collinearity diagnostics are
based on a singular-value decomposition of the scaled but un
Peter Flom wrote:
Has anyone programmed condition indexes in R?
I know that there is a function for variance inflation factors
available in the car package; however, Belsley (1991) Conditioning
Diagnostics (Wiley) notes that there are several weaknesses of VIFs:
e.g. 1) High VIFs are sufficient b
Has anyone programmed condition indexes in R?
I know that there is a function for variance inflation factors
available in the car package; however, Belsley (1991) Conditioning
Diagnostics (Wiley) notes that there are several weaknesses of VIFs:
e.g. 1) High VIFs are sufficient but not necessary co
