p.res.2x | R Documentation |
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.
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, ...)
x , y |
numeric vectors of the same length specifying 2
covariates. For the |
z |
numeric vector of same length as |
restricted |
positive value which truncates the size. The corresponding symbols are marked by stars. |
size |
the symbols are scaled so that |
slwd , scol |
line width and color(s) for the residual
|
xlab , ylab , main |
axis labels, and title see |
xlim , ylim |
the basic x- and y- axis extents, see
|
... |
further arguments passed to |
data |
(for the |
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 factor
s.
Andreas Ruckstuhl in June 1991 and Martin Maechler, in 1992, '94, 2003-4.
Stahel, W.~A. (2008) Statistische Datenanalyse: Eine Einführung für Naturwissenschaftler, 5. Auflage, Vieweg, Wiesbaden; Paragraph 13.8.r and 13.8.v.
p.res.2fact
,
plot.lm
,
TA.plot
.
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
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