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Regression Diagnostics
Syntax
influence.measures(lm.obj)
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 'lminf'
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)