# vif

##### Calculate the Variance Inflation Factor

The VIF for predictor $i$ is $1/(1-R_i^2)$, where $R_i^2$ is the $R^2$ from a regression of predictor $i$ against the remaining predictors.

- Keywords
- regression

##### Usage

```
vif(x, ...)
## S3 method for class 'default':
vif(x, y.name, na.action = na.exclude, ...) ## x is a data.frame
## S3 method for class 'formula':
vif(x, data, na.action = na.exclude, ...) ## x is a formula
## S3 method for class 'lm':
vif(x, na.action = na.exclude, ...) ## x is a "lm" object computed with x=TRUE
```

##### Arguments

- x
`data.frame`

, or`formula`

, or`lm`

object computed with`x=TRUE`

.- na.action
- See
`na.action`

. - ...
- additional arguments.
- y.name
- Name of Y-variable to be excluded from the computations.
- data
- A data frame in which the variables specified in the formula will be found. If missing, the variables are searched for in the standard way.

##### Details

A simple diagnostic of collinearity is the
*variance inflation factor, VIF*
one for each regression coefficient (other than the intercept).
Since the condition of collinearity involves the predictors but not
the response, this measure is a function of the $X$'s but not of $Y$.
The VIF for predictor $i$ is $1/(1-R_i^2)$, where $R_i^2$ is the
$R^2$ from a regression of predictor $i$ against the remaining
predictors. If $R_i^2$ is close to 1, this means that predictor $i$
is well explained by a linear function of the remaining predictors,
and, therefore, the presence of predictor $i$ in the model is
redundant. Values of VIF exceeding 5 are considered evidence of
collinearity: The information carried by a predictor having such a VIF
is contained in a subset of the remaining predictors. If, however,
all of a model's regression coefficients differ significantly from 0
($p$-value $

##### Value

- Vector of VIF values, one for each X-variable.

##### References

Heiberger, Richard~M. and Holland, Burt (2004b).
*Statistical Analysis and Data Display: An Intermediate Course
with Examples in S-Plus, R, and SAS*.
Springer Texts in Statistics. Springer.
ISBN 0-387-40270-5.

##### See Also

##### Examples

```
usair <- read.table(hh("datasets/usair.dat"),
col.names=c("SO2","temp","mfgfirms","popn",
"wind","precip","raindays"))
usair$lnSO2 <- log(usair$SO2)
usair$lnmfg <- log(usair$mfgfirms)
usair$lnpopn <- log(usair$popn)
usair.lm <- lm(lnSO2 ~ temp + lnmfg + wind + precip, data=usair, x=TRUE)
vif(usair.lm) ## the lm object must be computed with x=TRUE
vif(lnSO2 ~ temp + lnmfg + wind + precip, data=usair)
vif(usair)
vif(usair, y.name="lnSO2")
```

*Documentation reproduced from package HH, version 2.1-15, License: GPL (>= 2)*