| lm.influence {base} | R Documentation |
This function provides the basic quantities which are used in forming a wide variety of diagnostics for checking the quality of regression fits.
lm.influence(lm.obj)
lm.obj |
an object as returned by |
The influence.measures() and other functions listed in
See Also provide a more user oriented way of computing a
variety of regression diagnostics.
A list containing the following components:
hat |
a vector containing the diagonal of the “hat” matrix. |
coefficients |
the change in the estimated coefficients which results when the i-th case is dropped from the regression is contained in the i-th row of this matrix. |
sigma |
a vector whose i-th element contains the estimate of the residual standard deviation obtained when the i-th case is dropped from the regression. |
The coefficients returned by the R version
of lm.influence differ from those computed by S.
Rather than returning the coefficients which result
from dropping each case, we return the changes in the coefficients.
This is more directly useful in many diagnostic measures.
Note that cases with weights == 0 are dropped (contrary
to the situation in S).
if a model has been fitted with na.action=na.exclude (see
na.exclude), cases excluded in the fit are
considered here.
Belsley, D. A., Kuh, E. and Welsch, R. E. (1980) Regression Diagnostics. New York: Wiley.
summary.lm for summary and related methods;
influence.measures,
hat for the hat matrix diagonals,
dfbetas,
dffits,
covratio,
cooks.distance,
lm.
## Analysis of the life-cycle savings data
## given in Belsley, Kuh and Welsch.
data(LifeCycleSavings)
summary(lm.SR <- lm(sr ~ pop15 + pop75 + dpi + ddpi,
data = LifeCycleSavings),
corr = TRUE)
str(lmI <- lm.influence(lm.SR))
## For more `user level' examples, use example(influence.measures)