It does say "may". You could take it as a recommendation to make sure you know 
exactly what it means for your case.

Strange things happen in no-intercept models (especially if factors are 
involved, which is not the case here), and VIFs depend on R^2 which also has 
complications in those cases.

Generically with two variables, the VIF is 1/(1-R^2), with the R^2 being the 
squared "correlation" between the predictors.  This is also the case when the 
intercept is absent, but the correlation is based on sum(x1*x2) without first 
centering the variables. 

If you view the VIF as an extra multiplier in the expression for 
Var(betahat_i), that is still true without the intercept. However (with or 
without intercept) you have the issue that the colloquial interpretation 
(adding variables increases variance of betahat) ignores the fact that adding a 
variable may reduce the residual variation. 

A better interpretation could be to compare Var(betahat_i) to what it could 
have been in an optimal design where the predictors are orthogonal. But without 
centering, it is difficult to have orthogonality between two positive 
variables. 

-pd


> On 17 Nov 2025, at 14.42, Brian Smith <[email protected]> wrote:
> 
> I have below model
> 
> library(car)
> 
> data(Duncan)
> vif(lm(prestige ~ income + education - 1, data=Duncan))
> 
> With this, I am getting a warning message
> 
> Warning message:
> 
> In vif.default(lm(prestige ~ income + education - 1, data = Duncan)) :
> 
>  No intercept: vifs may not be sensible.
> 
> 
> Why for model with no intercept, vifs may not be sensible?
> 
> ______________________________________________
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-- 
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Office: A 4.23
Email: [email protected]  Priv: [email protected]

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