plot.lm                 package:base                 R Documentation

Plot Diagnostics for an lm Object

Description:

     Four plots (selectable by `which') are currently provided: a plot
     of residuals against fitted values, a Scale-Location plot of
     sqrt{| residuals |} against fitted values, a Normal Q-Q plot, and
     a plot of Cook's distances versus row labels.

Usage:

     plot(x, which = 1:4,
             caption = c("Residuals vs Fitted", "Normal Q-Q plot",
                         "Scale-Location plot", "Cook's distance plot"),
             panel = points,
             sub.caption = deparse(x$call), main = "",
             ask = prod(par("mfcol")) < length(which) && dev.interactive(),
             ...,
             id.n = 3, labels.id = names(residuals(x)), cex.id = 0.25)

Arguments:

       x: `lm' object, typically result of `lm' or `glm'.

   which: If a subset of the plots is required, specify a subset of the
          numbers `1:4'.

 caption: Captions to appear above the plots

   panel: Panel function.  A useful alternative to `points' is
          `panel.smooth'.

sub.caption: common title-above figures if there are multiple; used as
          `sub' (s.`title') otherwise.

    main: title to each plot-in addition to the above `caption'.

     ask: logical; if `TRUE', the user is asked before each plot, see
          `par(ask=.)'.

     ...: other parameters to be passed through to plotting functions.

    id.n: number of points to be labelled in each plot, starting with
          the most extreme.

labels.id: vector of labels, from which the labels for extreme points
          will be chosen.  `NULL' uses observation numbers.

  cex.id: magnification of point labels.

Details:

     `sub.caption'-by default the function call-is shown as a subtitle
     (under the x-axis title) on each plot when plots are on separate
     pages, or as a subtitle in the outer margin (if any) when there
     are multiple plots per page.

     The ``Scale-Location'' plot, also called ``Spread-Location'' or
     ``S-L'' plot, takes the square root of the absolute residuals in
     order to diminish skewness (sqrt{| E |} is much less skewed than |
     E | for Gaussian zero-mean E).

     This `S-L' and the Q-Q plot use standardized residuals which have
     identical variance (under the hypothesis).  They are given as R[i]
     / (s*sqrt(1 - h.ii)) where h.ii are the diagonal entries of the
     hat matrix, `lm.influence()'`$hat', see also `hat'.

Author(s):

     John Maindonald and Martin Maechler.

References:

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

     Cook, R. D. and Weisberg, S. (1982) Residuals and Influence in
     Regression. London: Chapman and Hall.

     Hinkley, D. V. (1975) On power transformations to symmetry.
     Biometrika 62, 101-111.

     McCullagh, P. and Nelder, J. A. (1989) Generalized Linear Models.
     London: Chapman and Hall.

See Also:

     `termplot', `lm.influence', `cooks.distance'.

Examples:

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

     ## 4 plots on 1 page; allow room for printing model formula in outer margin:
     par(mfrow = c(2, 2), oma = c(0, 0, 2, 0))
     plot(lm.SR)
     plot(lm.SR, id.n = NULL)               # no id's
     plot(lm.SR, id.n = 5, labels.id = NULL)# 5 id numbers

     ## Fit a smmooth curve, where applicable:
     plot(lm.SR, panel = panel.smooth)
     ## Gives a smoother curve
     plot(lm.SR, panel = function(x,y) panel.smooth(x, y, span = 1))

     par(mfrow=c(2,1))# same oma as above
     plot(lm.SR, which = 1:2, sub.caption = "Saving Rates, n=50, p=5")

