| ScatterplotSmoothers {car} | R Documentation |
These smoothers are used to draw nonparametric-regression lines on scatterplots produced by
the scatterplot, scatterplotMatrix and other car functions.
The functions aren't meant to
be called directly by the user, although the user can supply options via the smoother.args argument,
the contents of which vary by the smoother (see Details below). The gamLine smoother uses the
gam function in the mgcv package, the loessLine smoother uses the
loess function in the stats package, and the quantregLine smoother uses the
rqss function in the quantreg package.
gamLine(x, y, col, log.x, log.y, spread=FALSE, smoother.args, draw=TRUE) loessLine(x, y, col, log.x, log.y, spread=FALSE, smoother.args, draw=TRUE) quantregLine(x, y, col, log.x, log.y, spread=FALSE, smoother.args, draw=TRUE)
x |
$x$ coordinates of points. |
y |
$y$ coordinates of points. |
col |
line color. |
log.x |
|
log.y |
|
spread |
the default is to plot only an estimated mean or median. If this argument is TRUE, then a measure of spread is also plotted. |
smoother.args |
additional options accapted by the smoother, in the form of a list of named values (see Details below). |
draw |
if TRUE, the default, draw the smoother on the currently active graph.
If FALSE, return a list with coordinates |
The function loessLine is a reimplementation of the loess smoother
that has been used in car prior to September 2012. The only enhancement is the ability to
set more arguments through the smoother.args argument.
The function gamLine is new and more general than the loess fitting
because it allows fitting a generalized additive model using splines. You can specify a error
distribution and link function.
The function quantregLine fits an additive model using splines with estimation
based on L1 regression and quantile regression if you ask for the spread. It is
likely to be more robust than the other smoothers.
The argument smoother.args is a list of named elements used to pass
additional arguments to the smoother.
For loessLine the default value is
smoother.args=list(lty=1, lwd=2, lty.spread=2, lwd.spread=1, span=0.5,
degree=2, family="symmetric", iterations=4).
The arguments lty and lwd are the type and width
respectively of the mean or median smooth, smooth.lty
and smooth.lwd are the type and color of the spread smooths if requested.
The arguments span, degree and family are
passed to the loess function, iterations=0 by default
specifies no robustness iterations.
For gamLine the default is
smoother.args=list(lty=1, lwd=2, lty.spread=2, lwd.spread=1,
k=-1, bs="tp", family="gaussian", link=NULL, weights=NULL)
The first for arguments are as for loessLine. The next two
arguments are passed to the gam function to control the smoothing:
k=-1 allows gam to choose the number of splines in the basis
function; bs="tp" provides the type of spline basis to be used with "tp"
for the default thin-plate splines. The last three arguments allow providing
a family, link and weights as in generalized linear models. See examples
below.
For quantregLine the default is
smoother.args=list(lty=1, lwd=2, lty.spread=2, lwd.spread=1,
lambda=IQR(x). The first four
arguments are as for loessLine. The last argument is passed to the
qss function in quantreg. It is a smoothing
parameter, here a robust estimate of the scale of the horizontal axis variable.
This is an arbitrary choice, and may not work well in all circumstances.
John Fox jfox@mcmaster.ca and Sanford Weisbergsandy@umn.edu.
scatterplot, scatterplotMatrix, gam,
loess, and rqss.
scatterplot(prestige ~ income, data=Prestige)
scatterplot(prestige ~ income, data=Prestige, smoother=gamLine)
scatterplot(prestige ~ income, data=Prestige, smoother=quantregLine)
scatterplot(prestige ~ income | type, data=Prestige)
scatterplot(prestige ~ income | type, data=Prestige, smoother=gamLine)
scatterplot(prestige ~ income | type, data=Prestige, smoother=quantregLine)
scatterplot(prestige ~ income | type, data=Prestige, smoother=NULL)
scatterplot(prestige ~ income | type, data=Prestige, spread=TRUE)
scatterplot(prestige ~ income | type, data=Prestige, smoother=gamLine, spread=TRUE)
scatterplot(prestige ~ income | type, data=Prestige, smoother=quantregLine, spread=TRUE)
scatterplot(weight ~ repwt | sex, spread=TRUE, data=Davis, smoother=loessLine)
scatterplot(weight ~ repwt | sex, spread=TRUE, data=Davis, smoother=gamLine) # messes up
scatterplot(weight ~ repwt | sex, spread=TRUE, data=Davis, smoother=quantregLine) # robust
set.seed(12345)
w <- 1 + rpois(100, 5)
x <- rnorm(100)
p <- 1/(1 + exp(-(x + 0.5*x^2)))
s <- rbinom(100, w, p)
scatterplot(s/w ~ x, smoother=gamLine,
smoother.args=list(family="binomial", weights=w))
scatterplot(s/w ~ x, smoother=gamLine,
smoother.args=list(family=binomial, link="probit", weights=w))
scatterplot(s/w ~ x, smoother=gamLine,
smoother.args=list(family=binomial, link="probit", weights=w))
scatterplot(s/w ~ x, smoother=loessLine, reg=FALSE)
y <- rbinom(100, 1, p)
scatterplot(y ~ x, smoother=gamLine, smoother.args=list(family=binomial))