Confusion with infinite VIF confidence intervals

[jmgirard]

While teaching with easystats, I am finding many students are getting confused and misled by the visualization of infinite confidence intervals around VIF. Quite a few every semester write up something like, "given the arrows extending above 10, multicollinearity is indicated." Could the infinite ones just be hidden?

library(easystats)
#> # Attaching packages: easystats 0.7.4
#> ✔ bayestestR  0.16.0   ✔ correlation 0.8.7 
#> ✔ datawizard  1.1.0    ✔ effectsize  1.0.0 
#> ✔ insight     1.3.0    ✔ modelbased  0.11.0
#> ✔ performance 0.14.0   ✔ parameters  0.26.0
#> ✔ report      0.6.1    ✔ see         0.11.0

Working Well

fit <- lm(body_mass ~ 1 + sex + bill_len + bill_dep, data = penguins)
(cc <- check_collinearity(fit))
#> # Check for Multicollinearity
#> 
#> Low Correlation
#> 
#>      Term  VIF   VIF 95% CI adj. VIF Tolerance Tolerance 95% CI
#>       sex 1.50 [1.33, 1.76]     1.22      0.67     [0.57, 0.75]
#>  bill_len 1.36 [1.22, 1.60]     1.17      0.73     [0.63, 0.82]
#>  bill_dep 1.40 [1.25, 1.64]     1.18      0.72     [0.61, 0.80]
plot(cc)

Confusing to Students

fit2 <- lm(body_mass ~ 1 + sex + year, data = penguins)
(cc2 <- check_collinearity(fit2))
#> # Check for Multicollinearity
#> 
#> Low Correlation
#> 
#>  Term  VIF  VIF 95% CI adj. VIF Tolerance Tolerance 95% CI
#>   sex 1.00 [1.00, Inf]     1.00      1.00     [0.00, 1.00]
#>  year 1.00 [1.00, Inf]     1.00      1.00     [0.00, 1.00]
plot(cc2)

^{Created on 2025-05-23 with reprex v2.1.1}

[bwiernik]

Is omitting improper CIs really better though? I'd fear that would lead to misinterpreting the 1 VIF point estimate as indicating no collinearity, when really the interpretation should be "the CI says we don't have enough data here to interpret this". That seems like it's an important lesson to learn, even if it's tricky.