Collinearity implies two variables are near perfect linear combinations of one
another. Multicollinearity involves more than two variables. In the presence of multicollinearity,
regression estimates are unstable and have high standard errors. coll_diag
returns variance inflation factor, tolerance and condition indices. Collinearity is spotted by
finding 2 or more variables that have large proportions of variance (.50 or more) that correspond
to large condition indices. A rule of thumb is to label as large those condition indices in the range of 30 or larger.
Big values of VIF and small values of Tolerance indicate multicollinearity.
coll_diag(model) vif_tol(model) eigen_cindex(model)
lm
coll_diag
returns an object of class "coll_diag"
.
An object of class "coll_diag"
is a list containing the
following components:
Belsley, D. A., Kuh, E., and Welsch, R. E. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. New York: John Wiley & Sons.