Last data update: 2014.03.03
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R: Plots to assess the goodness of fit for the linear model...
Plots to assess the goodness of fit for the linear model objects
Description
Plots to assess the goodness of fit for the linear model objects
Usage
lmplot2(
x,
which = 1:5,
caption = c("Residuals vs Fitted", "Normal Q-Q plot",
"Scale-Location plot", "Cook's distance plot"),
panel = panel.smooth,
sub.caption = deparse(x$call),
main = "",
ask = interactive() && nb.fig < length(which)
&& .Device != "postscript",
...,
id.n = 3,
labels.id = names(residuals(x)),
cex.id = 0.75,
band=TRUE,
rug=TRUE,
width=1/10,
max.n=5000
)
Arguments
x |
lm object
|
which |
Numerical values between 1 and 5, indicating which plots
to be shown. The codes are:
- 1
Fitted vs residuals
- 2
Normal Q-Q
- 3
Scale-Location
- 4
Cook's distance
- 5
Residuals vs. predictor
|
caption |
Caption for each type of plot
|
panel |
function to draw on the existing plot
|
sub.caption |
SubCaption for the plots
|
main |
Main title of the plot
|
ask |
whether interactive graphics
|
... |
parameters passed to lmplot2 .
|
id.n |
integer value, less than or equal to residuals of lm object
|
labels.id |
Names of the residuals of the lm object
|
cex.id |
Parameter to control the height of text stringsx
|
band |
logical vector indicating whether bandplot should also be plotted
|
rug |
logical vector indicating whether rug should be added to
the existing plot
|
width |
Fraction of the data to use for plot smooths
|
max.n |
Maximum number of points to display in plots
|
Note
This function replaces plot.lm2 , which has been deprecated
to avoid potential problems with S3 method dispatching.
Author(s)
Gregory R. Warnes greg@warnes.net and Nitin
Jain nitin.jain@pfizer.com
See Also
plot.lm
Examples
ctl <- rnorm(100, 4)
trt <- rnorm(100, 4.5)
group <- gl(2,100,200, labels=c("Ctl","Trt"))
weight <- c(ctl, trt)
wt.err <- rnorm(length(weight), mean=weight, sd=1/2)
x <- lm(weight ~ group + wt.err)
lmplot2(x)
lmplot2(x, which=1, width=1/3)
lmplot2(x, which=1:3, width=1/3)
Results
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