plot.lm: Plot Diagnostics for an lm Object
In sintro: Introduction to R: sintro

Description Usage Arguments Details Author(s) References See Also Examples

Seven plots (selectable by which) are currently available: a plot of residuals against fitted values, a Scale-Location plot of sqrt(| residuals |) against fitted values, a Normal Q-Q plot, a plot of Cook's distances versus row labels, a plot of residuals against leverages, and a plot of Cook's distances against leverage/(1-leverage), a plot of studentized residuals against the ranks of the fitted values By default, the first three and 5 are provided.

## S3 method for class 'lm'
plot(x, which = c(1:3,5), 
     caption = list("Residuals vs Fitted", "Normal Q-Q",
       "Scale-Location", "Cook's distance",
       "Residuals vs Leverage",
       expression("Cook's dist vs Leverage  " * h[ii] / (1 - h[ii])),
       "Stud. Residuals vs Rk Fitted"),
     panel = if(add.smooth) panel.smooth else points,
     sub.caption = NULL, main = "",
     ask = prod(par("mfcol")) < length(which) && dev.interactive(),
     ...,
     id.n = 3, labels.id = names(residuals(x)), cex.id = 0.75,
     qqline = TRUE, cook.levels = c(0.5, 1.0),
     add.smooth = getOption("add.smooth"), label.pos = c(4,2),
     cex.caption = 1)

`x`	`lm` object, typically result of `lm` or `glm`.
`which`	if a subset of the plots is required, specify a subset of the numbers `1:7`.
`caption`	captions to appear above the plots; `character` vector or `list` of valid graphics annotations, see `as.graphicsAnnot`. Can be set to `""` or `NA` to suppress all captions.
`panel`	panel function. The useful alternative to `points`, `panel.smooth` can be chosen by `add.smooth = TRUE`.
`sub.caption`	common title—above the figures if there are more than one; used as `sub` (s.`title`) otherwise. If `NULL`, as by default, a possible abbreviated version of `deparse(x$call)` is used.
`main`	title to each plot—in addition to `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.
`qqline`	logical indicating if a `qqline()` should be added to the normal Q-Q plot.
`cook.levels`	levels of Cook's distance at which to draw contours.
`add.smooth`	logical indicating if a smoother should be added to most plots; see also `panel` above.
`label.pos`	positioning of labels, for the left half and right half of the graph respectively, for plots 1-3.
`cex.caption`	controls the size of `caption`.

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).

The ‘S-L’, the Q-Q, and the Residual-Leverage 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), and where the Residual-Leverage plot uses standardized Pearson residuals (residuals.glm(type = "pearson")) for R[i].

The Residual-Leverage plot shows contours of equal Cook's distance, for values of cook.levels (by default 0.5 and 1) and omits cases with leverage one with a warning. If the leverages are constant (as is typically the case in a balanced aov situation) the plot uses factor level combinations instead of the leverages for the x-axis. (The factor levels are ordered by mean fitted value.)

In the Cook's distance vs leverage/(1-leverage) plot, contours of standardized residuals that are equal in magnitude are lines through the origin. The contour lines are labelled with the magnitudes.

John Maindonald and Martin Maechler.

Plot 7 added by G. Sawitzki

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.

Firth, D. (1991) Generalized Linear Models. In Hinkley, D. V. and Reid, N. and Snell, E. J., eds: Pp. 55-82 in Statistical Theory and Modelling. In Honour of Sir David Cox, FRS. 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.

termplot, lm.influence, cooks.distance, hatvalues.

require(graphics)

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

## 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

## 2 plots on 1 page; studentized residuals versus rank of fitted values
par(mfrow=c(2,1))# same oma as above
plot(lm.SR, which = c(1,7))

## Was default in R <= 2.1.x:
## Cook's distances instead of Residual-Leverage plot
par(mfrow=c(2,2))# same oma as above
plot(lm.SR, which = 1:4)

## 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")