Regression Diagnostics

Usage

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)

Arguments

lm.obj the results returned by lm.
xmat the `X' or design matrix.

Description

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).

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 rstudent, dfbetas, dffits, covratio and cooks.distance provide direct access to the corresponding diagnostic quantities.

References

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.

See Also

lm.influence.

Examples

## Analysis of the life-cycle savings data
## given in Belsley, Kuh and Welsch.
data(savings)
lm.SR <- lm(sr ~ pop15 + pop75 + dpi + ddpi, data = savings)
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])
rstudent(lm.SR)
dffits(lm.SR)
covratio(lm.SR)

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