# vif

##### Variance Inflation Factors

Calculates variance-inflation and generalized variance-inflation factors for linear models.

- Keywords
- regression

##### Usage

```
vif(mod)
## S3 method for class 'lm':
vif(mod)
## S3 method for class 'default':
vif(mod)
```

##### Arguments

- mod
- an unweighted
`lm`

object.

##### Details

If all terms in the model have 1 df, then the usual variance-inflation
factors are calculated.
If any terms have more than 1 df, then generalized variance-inflation factors
(Fox and Monette, 1992) are calculated. These are interpretable as the inflation
in size of the confidence ellipse or ellipsoid for the coefficients of the term in
comparison with what would be obtained for orthogonal data.
The generalized vifs
are invariant with respect to the coding of the terms in the model (as long as
the subspace of the columns of the model matrix pertaining to each term is
invariant). To adjust for the dimension of the confidence ellipsoid, the function
also prints $GVIF^{1/(2\times df)}$.
Currently, `vif`

is only defined for linear models; `vif.default`

is
a dummy function that generates an error.

##### Value

- A vector of vifs, or a matrix containing one row for each term in the model, and columns for the GVIF, df, and $GVIF^{1/(2\times df)}$.

##### References

Fox, J. and Monette, G. (1992)
Generalized collinearity diagnostics.
*JASA*, **87**, 178--183.
Fox, J. (1997)
*Applied Regression, Linear Models, and Related Methods.* Sage.

##### Examples

```
data(Duncan)
vif(lm(prestige~income+education, data=Duncan))
## income education
## 2.104900 2.104900
vif(lm(prestige~income+education+type, data=Duncan))
## GVIF Df GVIF^(1/2Df)
## income 2.209178 1 1.486330
## education 5.297584 1 2.301648
## type 5.098592 2 1.502666
```

*Documentation reproduced from package car, version 1.0-14, License: GPL version 2 or newer*

### Community examples

**evelynes28@hotmail.com**at Dec 28, 2018 car v3.0-2

## NEW EXAMPLE > vif(lm(Poverty ~ Illiteracy_level + Tech_access, data = log_dataset)) Illiteracy_level Tech_access 1.7663 1.7663