check_collinearity()
checks regression models for
multicollinearity by calculating the variance inflation factor (VIF).
multicollinearity()
is an alias for check_collinearity()
.
(When printed, VIF are also translated to Tolerance values, where
tolerance = 1/vif
.)
check_collinearity(x, ...)
multicollinearity(x, ...)
# S3 method for default
check_collinearity(x, verbose = TRUE, ...)
# S3 method for glmmTMB
check_collinearity(
x,
component = c("all", "conditional", "count", "zi", "zero_inflated"),
verbose = TRUE,
...
)
x  A model object (that should at least respond to 

...  Currently not used. 
verbose  Toggle off warnings or messages. 
component  For models with zeroinflation component, multicollinearity
can be checked for the conditional model (count component,

A data frame with three columns: The name of the model term, the variance inflation factor and the factor by which the standard error is increased due to possible correlation with other terms.
Multicollinearity should not be confused with a raw strong correlation
between predictors. What matters is the association between one or more
predictor variables, conditional on the other variables in the
model. In a nutshell, multicollinearity means that once you know the
effect of one predictor, the value of knowing the other predictor is rather
low. Thus, one of the predictors doesn't help much in terms of better
understanding the model or predicting the outcome. As a consequence, if
multicollinearity is a problem, the model seems to suggest that the
predictors in question don't seems to be reliably associated with the
outcome (low estimates, high standard errors), although these predictors
actually are strongly associated with the outcome, i.e. indeed might have
strong effect (McElreath 2020, chapter 6.1).
Multicollinearity might arise when a third, unobserved variable has a causal
effect on each of the two predictors that are associated with the outcome.
In such cases, the actual relationship that matters would be the association
between the unobserved variable and the outcome.
Remember: “Pairwise correlations are not the problem. It is the
conditional associations  not correlations  that matter.”
(McElreath 2020, p. 169)
The variance inflation factor is a measure to analyze the magnitude of multicollinearity of model terms. A VIF less than 5 indicates a low correlation of that predictor with other predictors. A value between 5 and 10 indicates a moderate correlation, while VIF values larger than 10 are a sign for high, not tolerable correlation of model predictors (James et al. 2013). The Increased SE column in the output indicates how much larger the standard error is due to the association with other predictors conditional on the remaining variables in the model.
If interaction terms are included in a model, high VIF values are expected. This portion of multicollinearity among the component terms of an interaction is also called "inessential illconditioning", which leads to inflated VIF values that are typically seen for models with interaction terms (Francoeur 2013).
There is also a plot()
method implemented in the seepackage.
Francoeur, R. B. (2013). Could Sequential Residual Centering Resolve Low Sensitivity in Moderated Regression? Simulations and Cancer Symptom Clusters. Open Journal of Statistics, 03(06), 24–44.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (eds.). (2013). An introduction to statistical learning: with applications in R. New York: Springer.
McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. 2nd edition. Chapman and Hall/CRC.
Vanhove, J. (2019). Collinearity isn't a disease that needs curing. webpage
m < lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
check_collinearity(m)
#> # Check for Multicollinearity
#>
#> Low Correlation
#>
#> Term VIF Increased SE Tolerance
#> gear 1.53 1.24 0.65
#>
#> Moderate Correlation
#>
#> Term VIF Increased SE Tolerance
#> wt 5.05 2.25 0.20
#> cyl 5.41 2.33 0.18
#> disp 9.97 3.16 0.10
# plot results
if (require("see")) {
x < check_collinearity(m)
plot(x)
}