outlier.test: Bonferroni Outlier Test
Description
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.
Value
- an object of class
outlier.test, which is normally just
printed.
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.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.
[object Object]
data(Duncan)
outlier.test(lm(prestige~income+education, data=Duncan))
## max|rstudent| df unadjusted p Bonferroni p
## 3.134519 41 0.003177202 0.1429741
##
## Observation: minister
regression
htest