R: Plot Diagnostics for an lm Object

plot.lm {stats}

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 = 0.75)

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, 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.

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 smooth 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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