plot.lm {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

## S3 method for class 'lm'
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 \times \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")

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