Bonferroni Outlier Test
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 class 'lm' outlierTest(model, cutoff=0.05, n.max=10, order=TRUE, labels=names(rstudent), ...) ## S3 method for class 'lmerMod' outlierTest(model, ...) ## S3 method for class 'outlierTest' print(x, digits=5, ...)
model |
an |
cutoff |
observations with Bonferroni p-values exceeding
|
n.max |
maximum number of observations to report (default, |
order |
report Studenized residuals in descending order of magnitude?
(default, |
labels |
an optional vector of observation names. |
... |
arguments passed down to methods functions. |
x |
|
digits |
number of digits for reported p-values. |
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.
an object of class outlierTest, which is normally just
printed.
John Fox jfox@mcmaster.ca and Sanford Weisberg
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.
outlierTest(lm(prestige ~ income + education, data=Duncan))
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