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. |
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.sub.caption
by default the function callis 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
.
Cook, R. D. and S. Weisberg (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.
lm.influence
, cooks.distance
## 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")