# outlier.test

##### Bonferroni Outlier Test

Reports the Bonferroni p-value for the most extreme observation. At present, there are methods for studentized residuals in linear and generalized linear models.

##### Usage

```
outlier.test(model, ...)
outlier.test.lm(model, labels=names(rstud))
outlier.test.glm(model, labels=names(rstud))
```

##### Arguments

- model
- a suitable model object.
- labels
- an optional vector of observation names.
- ...
- not for the user.

##### Details

For a linear model, the p-value reported is for the largest absolute studentized residual, using the $t$ distribution with degrees of freedom one less than the residual df for the model. For a generalized linear model, the largest absolute studentized residual is also used, but with the standard-normal distribution. The Bonferroni adjustment multiplies the usual two-sided p-value by the number of observations.

##### Value

- an object of class
`outlier.test`

, which is normally just printed.

##### References

Cook, R. D. and Weisberg, S. (1984)
*Residuals and Influence in Regression.) Wiley.
Fox, J. (1997)
Applied Regression, Linear Models, and Related Methods. Sage.
Williams, D. A. (1987)
Generalized linear model diagnostics using the deviance and single
case deletions. Applied Statistics 36, 181--191.*
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*Documentation reproduced from package car, version 0.8-3, License: GPL version 2 or newer*