| outlier.test {car} | R Documentation |
Reports the Bonferroni p-value for the most extreme observation. At present, there are methods for studentized residuals in linear and generalized linear models.
outlier.test(model, ...)
## S3 method for class 'lm':
outlier.test(model, labels=names(rstud), ...)
## S3 method for class 'glm':
outlier.test(model, labels=names(rstud), ...)
## S3 method for class 'outlier.test':
print(x, digits=options("digits")[[1]], ...)
model |
a suitable model object. |
labels |
an optional vector of observation names. |
... |
arguments passed down to methods functions. |
x |
outlier.test object. |
digits |
number of digits for printed output. |
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.
an object of class outlier.test, which is normally just
printed.
John Fox jfox@mcmaster.ca
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.
outlier.test(lm(prestige~income+education, data=Duncan)) ## max|rstudent| df unadjusted p Bonferroni p ## 3.134519 41 0.003177202 0.1429741 ## ## Observation: minister