| plot.lm {base} | R Documentation |
Four plots (choosable 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.
plot.lm(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$call), main = "",
ask = interactive() && one.fig && .Device != "postscript",
...,
id.n = 3, labels.id = names(residuals(x)), cex.id = 0.25)
x |
lm object, typically result of lm or
glm. |
which |
If a subset of the plots is required, specify a subset of
the numbers 1:4. |
caption |
Captions to appear above the plots |
panel |
Panel function. A useful alternative to
points is panel.smooth. |
sub.caption |
common title—above figures if there are multiple;
used as sub (s.title) otherwise. |
main |
title to each plot—in addition to the above
caption. |
ask |
logical; if TRUE, the user is asked before
each plot, see par(ask=.). |
... |
other parameters to be passed through to plotting functions. |
id.n |
number of points to be labelled in each plot, starting with the most extreme. |
labels.id |
vector of labels, from which the labels for extreme
points will be chosen. NULL uses observation numbers. |
cex.id |
magnification of point labels. |
sub.caption—by default the function call—is shown as
a subtitle (under the x-axis title) on each plot when plots are on
separate pages, or as a subtitle in the outer margin (if any) when
there are multiple plots per page.
The ``Scale-Location'' plot, also called ``Spread-Location'' or ``S-L'' plot, takes the square root of the absolute residuals in order to diminish skewness (sqrt{| E |} is much less skewed than | E | for Gaussian zero-mean E).
This `S-L' and the Q-Q plot use standardized residuals which
have identical variance (under the hypothesis). They are given as
R[i] / (s*sqrt(1 - h.ii))
where h.ii are the diagonal entries of the hat matrix,
lm.influence()$hat, see also hat.
John Maindonald and Martin Maechler.
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.
## 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)) ## Warnings: panel = panel.smoth, 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")