outlierTest {car}  R Documentation 
Reports the Bonferroni pvalues for testing each observation in turn to be a meanshift outlier, based Studentized residuals in linear (ttests), 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 pvalues 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 pvalues. 
For a linear model, pvalues reported use the t distribution with degrees of
freedom one less than the residual df for the model. For a generalized
linear model, pvalues are based on the standardnormal distribution. The Bonferroni
adjustment multiplies the usual twosided pvalue 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))