influence.measures(model)
rstandard(model, ...)
"rstandard"(model, infl = lm.influence(model, do.coef = FALSE), sd = sqrt(deviance(model)/df.residual(model)), ...)
"rstandard"(model, infl = influence(model, do.coef = FALSE), type = c("deviance", "pearson"), ...)
rstudent(model, ...)
"rstudent"(model, infl = lm.influence(model, do.coef = FALSE), res = infl$wt.res, ...)
"rstudent"(model, infl = influence(model, do.coef = FALSE), ...)
dffits(model, infl = , res = )
dfbeta(model, ...)
"dfbeta"(model, infl = lm.influence(model, do.coef = TRUE), ...)
dfbetas(model, ...)
"dfbetas"(model, infl = lm.influence(model, do.coef = TRUE), ...)
covratio(model, infl = lm.influence(model, do.coef = FALSE), res = weighted.residuals(model))
cooks.distance(model, ...)
"cooks.distance"(model, infl = lm.influence(model, do.coef = FALSE), res = weighted.residuals(model), sd = sqrt(deviance(model)/df.residual(model)), hat = infl$hat, ...)
"cooks.distance"(model, infl = influence(model, do.coef = FALSE), res = infl$pear.res, dispersion = summary(model)$dispersion, hat = infl$hat, ...)
hatvalues(model, ...)
"hatvalues"(model, infl = lm.influence(model, do.coef = FALSE), ...)
hat(x, intercept = TRUE)lm.influence or influence (the latter
only for the glm method of rstudent and
cooks.distance).glm objects) to use,
see default.glm method for rstandard.x?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.)
Values for generalized linear models are approximations, as described in Williams (1987) (except that Cook's distances are scaled as $F$ rather than as chi-square values). The approximations can be poor when some cases have large influence.
The optional infl, res and sd arguments are there
to encourage the use of these direct access functions, in situations
where, e.g., the underlying basic influence measures (from
lm.influence or the generic influence) are
already available.
Note that cases with weights == 0 are dropped from all
these functions, but that if a linear model has been fitted with
na.action = na.exclude, suitable values are filled in for the
cases excluded during fitting.
The function hat() exists mainly for S (version 2)
compatibility; we recommend using hatvalues() instead.
Cook, R. D. and Weisberg, S. (1982) Residuals and Influence in Regression. London: Chapman and Hall.
Williams, D. A. (1987) Generalized linear model diagnostics using the deviance and single case deletions. Applied Statistics 36, 181--191.
Fox, J. (1997) Applied Regression, Linear Models, and Related Methods. Sage.
Fox, J. (2002) An R and S-Plus Companion to Applied Regression. Sage Publ.; http://www.socsci.mcmaster.ca/jfox/Books/Companion/.
influence (containing lm.influence). ‘plotmath’ for the use of hat in plot annotation.