# outlierTest

##### Bonferroni Outlier Test

Reports the Bonferroni p-values for Studentized residuals in linear and generalized linear models, based on a t-test for linear models and normal-distribution test for generalized linear models.

- Keywords
- regression, htest

##### Usage

```
outlierTest(model, ...)
## S3 method for class 'lm':
outlierTest(model, cutoff=0.05, n.max=10, order=TRUE,
labels=names(rstudent), ...)
## S3 method for class 'outlierTest':
print(x, digits=5, ...)
```

##### Arguments

- model
- an
`lm`

or`glm`

model object. - cutoff
- observations with Bonferonni p-values exceeding
`cutoff`

are not reported, unless no observations are nominated, in which case the one with the largest Studentized residual is reported. - n.max
- maximum number of observations to report (default,
`10`

). - order
- report Studenized residuals in descending order of magnitude?
(default,
`TRUE`

). - labels
- an optional vector of observation names.
- ...
- arguments passed down to methods functions.
- x
`outlierTest`

object.- digits
- number of digits for reported p-values.

##### Details

For a linear model, p-values reported use the t distribution with degrees of
freedom one less than the residual df for the model. For a generalized
linear model, p-values are based on the standard-normal distribution. The Bonferroni
adjustment multiplies the usual two-sided p-value by the number of
observations. The `lm`

method works for `glm`

objects. To show all
of the observations set `cutoff=Inf`

and `n.max=Inf`

.

##### Value

- an object of class
`outlierTest`

, which is normally just printed.

##### References

Cook, R. D. and Weisberg, S. (1982)
*Residuals and Influence in Regression.* Chapman and Hall.
Fox, J. (2008)
*Applied Regression Analysis and Generalized Linear Models*,
Second Edition. Sage.
Fox, J. and Weisberg, S. (2011)
*An R Companion to Applied Regression*, Second Edition, Sage.
Weisberg, S. (2005) *Applied Linear Regression*, Third Edition, Wiley.
Williams, D. A. (1987)
Generalized linear model diagnostics using the deviance and single
case deletions. *Applied Statistics* **36**, 181--191.

##### Examples

`outlierTest(lm(prestige ~ income + education, data=Duncan))`

*Documentation reproduced from package car, version 2.0-11, License: GPL (>= 2)*