plot.lm {stats} | R Documentation |

Six 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). By default, the first
three and `5`

are provided.

## S3 method for class 'lm': plot(x, which = c(1:3,5), caption = c("Residuals vs Fitted", "Normal Q-Q", "Scale-Location", "Cook's distance", "Residuals vs Leverage", "Cook's distance vs Leverage"), 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))

`x` |
`lm` object, typically result of `lm` or
`glm` . |

`which` |
if a subset of the plots is required, specify a subset of
the numbers `1:6` . |

`caption` |
captions to appear above the plots |

`panel` |
panel function. The useful alternative to
`points` , `panel.smooth` can be chosen
by `add.smooth = TRUE` . |

`sub.caption` |
common title—above figures if there are multiple;
used as `sub` (s.`title` ) otherwise. If `NULL` ,
as by default, a possible shortened version of
`deparse(x$call)` is used. |

`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. |

`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. |

`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`

.

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. 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.

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`

.

## Analysis of the life-cycle savings data ## given in Belsley, Kuh and Welsch. 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 ## Was default in R <= 2.1.x: Cook's distances instead of Residual-Leverage plot 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")

[Package *stats* version 2.5.0 Index]