p.res.2x {sfsmisc}

Stahel's Residual Plot against 2 X's

Description

Plot Residuals, e.g., of a multiple linear regression, against two (predictor) variables, using positively and negatively oriented line segments for positive and negative residuals.

This is a (S3) generic function with a default and a formula method.

Usage

p.res.2x(x, ...)

## Default S3 method:
p.res.2x(x, y, z, restricted, size = 1, slwd = 1, scol = 2:3,
         xlab = NULL, ylab = NULL, main = NULL,
         xlim = range(x), ylim = range(y), ...)

## S3 method for class 'formula'
p.res.2x(x = ~., data, main = deparse(substitute(data)),
         xlab = NULL, ylab = NULL, ...)

Arguments

x, y

numeric vectors of the same length specifying 2 covariates. For the formula method, x is a formula.

z

numeric vector of same length as x and y, typically residuals.

restricted

positive value which truncates the size. The corresponding symbols are marked by stars.

size

the symbols are scaled so that size is the size of the largest symbol in cm.

slwd, scol

line width and color(s) for the residual segments. If scol has length 2 as per default, the two colors are used for positive and negative z values, respectively.

xlab, ylab, main

axis labels, and title see title, each with a sensible default. To suppress, use, e.g., main = "".

xlim, ylim

the basic x- and y- axis extents, see plot.default. Note that these will be slightly extended such that segments are not cut off.

...

further arguments passed to plot, or p.res.2x.default(), respectively.

data

(for the formula method:) a data frame or a fitted "lm" object.

Details

Each residual zz[i] is visualized as line segment centered at (xx_i,yy_i), i=1,\dots,n, where the lengths of the segments are proportional to the absolute values \|zz_i\|.

Positive residuals' line segments have slope +1, and negative ones slope -1, and scol is used to use different colors for negative and positive segments.

The formula interface calls p.res.2fact() when both x and y are factors.

Author(s)

Andreas Ruckstuhl in June 1991 and Martin Maechler, in 1992, '94, 2003-4.

References

Stahel, W.~A. (2008) Statistische Datenanalyse: Eine Einführung für Naturwissenschaftler, 5. Auflage, Vieweg, Wiesbaden; Paragraph 13.8.r and 13.8.v.

See Also

p.res.2fact, plot.lm, TA.plot.

Examples

xx <- rep(1:10,7)
yy <- rep(1:7, rep(10,7))
zz <- rnorm(70)
p.res.2x(xx,yy,zz, restricted = 2, main = "i.i.d.  N(0,1) random residuals")

example(lm.influence, echo = FALSE)

op <- mult.fig(2, marP=c(-1,-1,-1,0), main="p.res.2x(*,*, residuals(lm.SR))")$old.par
with(LifeCycleSavings,
     { p.res.2x(pop15, ddpi, residuals(lm.SR), scol=c("red", "blue"))
       p.res.2x(pop75, dpi,  residuals(lm.SR), scol=2:1)
     })

## with formula interface:
p.res.2x(~ pop15 + ddpi, lm.SR, scol=c("red", "blue"))
p.res.2x(~ pop75 +  dpi, lm.SR, scol=2:1)

par(op) # revert par() settings above