lm.influence              package:base              R Documentation

Regression Diagnostics

Description:

     This function provides the basic quantities which are used in
     forming a wide variety of diagnostics for checking the quality of
     regression fits.

Usage:

     lm.influence(lm.obj)

Arguments:

  lm.obj: an object as returned by `lm'.

Details:

     The `influence.measures()' and other functions listed in See Also
     provide a more user oriented way of computing a variety of
     regression diagnostics.

Value:

     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.

Note:

     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.

References:

     Belsley, D. A., Kuh, E. and Welsch, R. E. (1980) Regression
     Diagnostics. New York: Wiley.

See Also:

     `summary.lm' for `summary' and related methods;
     `influence.measures',
     `hat' for the hat matrix diagonals,
     `dfbetas', `dffits', `covratio', `cooks.distance', `lm'.

Examples:

     ## 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)

