Description Usage Arguments Details Value References Examples

Calculate generalised variance inflation factors for terms in a fitted model(s) via the variance-covariance matrix of coefficients.

1 |

`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).
Defaults to the |

`VIF()`

calculates generalised variance inflation factors (GVIF) as
described in Fox & Monette (1992), and also implemented in
`car::vif()`

. 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. doi: 10/dm9wbw

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
# 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)
``` |

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