outlier.t.test {alr3} | R Documentation |
Compute significance levels for the mean-shift outlier model using the Bonferroni inequality
outlier.t.test(m, order=TRUE, bound=1)
m |
A model of type lm. |
order |
If TRUE, order the cases according to the p-value. If FALSE, don't order. |
bound |
Ignore cases with p-value bigger or equal to this value. |
Returns length(res)*2*(1-pt(abs(res),df)), where res = rstandard(m) is the vector of Studentized residuals. These are two-sided Bonferroni significance levels for testing a single outlier.
A data frame with columns giving the value of the studentized residual and corresponding Bonferroni p-value, and one row for each case for which the bound is satisfied.
Sanford Weisberg, sandy@stat.umn.edu
Weisberg, S. (2005). Applied Linear Regression, third edition, Wiley.
data(cloud) m1 <- lm(logb(Rain,2)~S+logb(C,2)+logb(P,2)+E+A,data=cloud,subset=-2) outlier.t.test(m1)