influence.measures {base} | R Documentation |
This suite of functions can be used to compute some of the regression diagnostics discussed in Belsley, Kuh and Welsch (1980), and in Cook and Weisberg (1982).
influence.measures(lm.obj) summary.infl (object, digits = max(2, .Options$digits - 5), ...) print.infl (x, digits = max(3, .Options$digits - 4), ...) rstandard(lm.obj) rstudent(lm.obj) dfbetas(lm.obj) dffits(lm.obj) covratio(lm.obj) cooks.distance(lm.obj) hat(xmat)
lm.obj |
the results returned by lm . |
xmat |
the `X' or design matrix. |
The primary function is influence.measures
which produces a
class "infl"
object tabular display showing the DFBETAS for
each model variable, DFFITS, covariance ratios, Cook's distances and
the diagonal elements of the hat matrix. Cases which are influential
with respect to any of these measures are marked with an asterisk.
The functions dfbetas
, dffits
,
covratio
and cooks.distance
provide direct access to the
corresponding diagnostic quantities. Functions rstandard
and
rstudent
give the standardized and Studentized residuals
respectively. (These re-normalize the residuals to have unit variance,
using an overall and leave-one-out measure of the error variance
respectively.)
Note that cases with weights == 0
are dropped from all
these functions.
Belsley, D. A., E. Kuh and R. E. Welsch (1980). Regression Diagnostics. New York: Wiley.
Cook, R. D. and S. Weisberg (1982). Residuals and Influence in Regression. London: Chapman and Hall.
## Analysis of the life-cycle savings data ## given in Belsley, Kuh and Welsch. data(LifeCycleSavings) lm.SR <- lm(sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings) summary(inflm.SR <- influence.measures(lm.SR)) inflm.SR which(apply(inflm.SR$is.inf, 1, any)) # which observations `are' influential dim(dfb <- dfbetas(lm.SR)) # the 1st columns of influence.measures all(dfb == inflm.SR$infmat[, 1:5]) rstandard(lm.SR) rstudent(lm.SR) dffits(lm.SR) covratio(lm.SR) ## Huber's data [Atkinson 1985] xh <- c(-4:0, 10) yh <- c(2.48, .73, -.04, -1.44, -1.32, 0) summary(lmH <- lm(yh ~ xh)) influence.measures(lmH)