Generalised Variance Inflation Factors
Calculate generalised variance inflation factors for terms in a fitted model(s) via the variance-covariance matrix of coefficients.
VIF(mod, data = NULL, env = parent.frame())
mod |
A fitted model object, or a list or nested list of such objects. |
data |
An optional dataset, used to first refit the model(s). |
env |
Environment in which to look for model data (if none supplied). |
VIF calculates generalised variance inflation factors (GVIF)
as described in Fox & Monette (1992), and also implemented in the
vif function in the car package. However, whereas vif
returns both GVIF and GVIF^(1/(2*Df)) values, VIF simply returns the
squared result of the latter measure, which equals the standard VIF for
single-coefficient terms and is the equivalent measure for
multi-coefficient terms (e.g. categorical or polynomial). Also, while
vif returns values per model term (i.e. predictor variable),
VIF returns values per coefficient, meaning that the same value will
be returned per coefficient for multi-coefficient terms. Finally, NA
is returned for any terms which could not be estimated in the model (e.g.
aliased).
A numeric vector of the VIFs, or an array, list of vectors/arrays, or nested list.
Fox, J. and Monette, G. (1992) Generalized Collinearity Diagnostics. Journal of the American Statistical Association 87, 178-183. https://doi.org/dm9wbw
# Model with two correlated terms
m <- Shipley.Growth[[3]]
VIF(m) # Date & DD somewhat correlated
VIF(update(m, . ~ . - DD)) # drop DD
# Model with different types of predictor (some multi-coefficient terms)
d <- data.frame(
y = rnorm(100),
x1 = rnorm(100),
x2 = as.factor(rep(c("a", "b", "c", "d"), each = 25)),
x3 = rep(1, 100)
)
m <- lm(y ~ poly(x1, 2) + x2 + x3, data = d)
VIF(m)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.