# vif

0th

Percentile

##### 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.

• vif
• vif.lm
• vif.default
##### 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