

   RReeggrreessssiioonn DDiiaaggnnoossttiiccss

        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)

   AArrgguummeennttss::

     lm.obj: the results returned by `lm'.

       xmat: the `X' or design matrix.

   DDeessccrriippttiioonn::

        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 pro-
        duces 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', `covra-
        tio' and `cooks.distance' provide direct access to the
        corresponding diagnostic quantities.

   RReeffeerreenncceess::

        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.

   SSeeee AAllssoo::

        `lm.influence'.

   EExxaammpplleess::

        ## 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)

        ## Huber's data [Atkinson 1985]
        xh <- c(-4:0, 10)
        yh <- c(2.48, .73, -.04, -1.44, -1.32, 0)
        summary(lmH <- lm(yh ~ xh))
        influence.measures(lmH)

