VIF {AICcPermanova} | R Documentation |
This function calculates the maximum Variance Inflation Factor (VIF) for a given model. The VIF is a measure of collinearity among predictor variables within a regression model. It quantifies how much the variance of an estimated regression coefficient is increased due to collinearity. A VIF of 1 indicates no collinearity, while values above 1 indicate increasing levels of collinearity. A VIF of 5 or greater is often considered high, indicating a strong presence of collinearity.
VIF(model)
model |
A regression model, such as those created by lm, glm, or other similar functions. |
The maximum VIF value.
Belsley, D. A., Kuh, E., & Welsch, R. E. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. John Wiley & Sons.
Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W. (2004). Applied Linear Statistical Models. McGraw-Hill/Irwin.
O'Brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41(5), 673-690.