Reports the Bonferroni p-values for testing each observation in turn to be a mean-shift outlier, based Studentized residuals in linear (t-tests), generalized linear models (normal tests), and linear mixed models.

`outlierTest(model, ...)`# S3 method for lm
outlierTest(model, cutoff=0.05, n.max=10, order=TRUE,
labels=names(rstudent), ...)
# S3 method for lmerMod
outlierTest(model, ...)

# S3 method for outlierTest
print(x, digits=5, ...)

model

an `lm`

, `glm`

, or `lmerMod`

model object; the `"lmerMod"`

method calls the `"lm"`

method and can take the same arguments.

cutoff

observations with Bonferroni 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.

an object of class `outlierTest`

, which is normally just
printed.

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`

.

Cook, R. D. and Weisberg, S. (1982)
*Residuals and Influence in Regression.* Chapman and Hall.

Fox, J. (2016)
*Applied Regression Analysis and Generalized Linear Models*,
Third Edition. Sage.

Fox, J. and Weisberg, S. (2019)
*An R Companion to Applied Regression*, Third Edition, Sage.

Weisberg, S. (2014) *Applied Linear Regression*, Fourth Edition, Wiley.

Williams, D. A. (1987)
Generalized linear model diagnostics using the deviance and single
case deletions. *Applied Statistics* **36**, 181--191.

# NOT RUN { outlierTest(lm(prestige ~ income + education, data=Duncan)) # }