This function uses basic R graphics to draw a two-dimensional scatterplot, with options to allow for plot enhancements that are often helpful with regression problems. Enhancements include adding marginal boxplots, estimated mean and variance functions using either parametric or nonparametric methods, point identification, jittering, setting characteristics of points and lines like color, size and symbol, marking points and fitting lines conditional on a grouping variable, and other enhancements.
sp is an abbreviation for scatterplot.
scatterplot(x, ...)# S3 method for formula
scatterplot(formula, data, subset, xlab, ylab, id=FALSE,
legend=TRUE, ...)
# S3 method for default
scatterplot(x, y, boxplots=if (by.groups) "" else "xy",
regLine=TRUE, legend=TRUE, id=FALSE, ellipse=FALSE, grid=TRUE,
smooth=TRUE,
groups, by.groups=!missing(groups),
xlab=deparse(substitute(x)), ylab=deparse(substitute(y)),
log="", jitter=list(), cex=par("cex"),
col=carPalette()[-1], pch=1:n.groups,
reset.par=TRUE, ...)
sp(x, ...)
vector of horizontal coordinates (or first argument of generic function).
vector of vertical coordinates.
a model formula, of the form y ~ x or, if plotting by
groups, y ~ x | z, where z evaluates to a factor
or other variable dividing the data into groups. If x is a factor, then parallel boxplots
are produced using the Boxplot function.
data frame within which to evaluate the formula.
expression defining a subset of observations.
if "x" a marginal boxplot for the horizontal x-axis is drawn below the plot;
if "y" a marginal boxplot for vertical y-axis is drawn to the left of the plot;
if "xy" both marginal boxplots are drawn; set to "" or FALSE to
suppress both boxplots.
controls adding a fitted regression line to the plot. if
regLine=FALSE, no line is drawn. If TRUE, the default, an OLS
line is fit. This argument can also be a list. The default of TRUE is
equivalent to regLine=list(method=lm, lty=1, lwd=2, col=col), which specifies
using the lm function to estimate the fitted line, to draw a solid line
(lty=1) of width 2 times the nominal width (lwd=2) in the color given by
the first element of the col argument described below.
when the plot is drawn by groups and legend=TRUE, controls placement
and properties of a
legend; if FALSE, the legend is suppressed. Can be a list of
named arguments, as follows: title for the legend; inset, giving space
as a proportion of the axes to offset the legend from the axes; coords
specifying the position of the legend in any form acceptable to the
legend function or, if not given, the legend is placed above
the plot in the upper margin; columns for the legend, determined automatically
to prefer a horizontal layout if not given explicitly; cex giving the
relative size of the legend symbols and text. TRUE (the default) is equivalent to
list(title=deparse(substitute(groups)), inset=0.02, cex=1).
controls point identification; if FALSE (the default), no points are
identified; can be a list of named arguments to the showLabels function;
TRUE is equivalent to
list(method="mahal", n=2, cex=1, col=carPalette()[-1], location="lr"),
which identifies the 2 points (in each group) with the largest Mahalanobis distances
from the center of the data. See showLabels for a description of the
other arguments. The default behavior of id is not the same in all graphics
functions in car, as the method used depends on the type of plot.
controls plotting data-concentration ellipses. If FALSE
(the default), no ellipses are plotted. Can be a list of named values giving
levels, a vector of one or more bivariate-normal probability-contour levels at
which to plot the ellipses; robust, a logical value determing whether to use
the cov.trob function in the MASS package to calculate the center
and covariance matrix for the data ellipses; and fill and fill.alpha,
which control whether the ellipse is filled and the transparency of the fill.
TRUE is equivalent to
list(levels=c(.5, .95), robust=TRUE, fill=TRUE, fill.alpha=0.2).
If TRUE, the default, a light-gray background grid is put on the graph
specifies a nonparametric estimate of the mean or median
function of the vertical axis variable given the
horizontal axis variable and optionally a nonparametric estimate of the conditional variance. If
smooth=FALSE neither function is drawn. If smooth=TRUE, then both the mean function
and variance funtions are drawn for ungrouped data, and the mean function only is drawn
for grouped
data. The default smoother is loessLine, which uses the
loess function from
the stats package. This smoother is fast and reliable. See the details below
for changing
the smoother, line type, width and color, of the added lines, and adding arguments
for the smoother.
a factor or other variable dividing the data into groups; groups are plotted with different colors, plotting characters, fits, and smooths. Using this argument is equivalent to specifying the grouping variable in the formula.
if TRUE (the default when there are groups), regression lines are fit by groups.
label for horizontal axis.
label for vertical axis.
same as the log argument to plot, to produce log axes.
a list with elements x or y or both, specifying jitter factors
for the horizontal and vertical coordinates of the points in the scatterplot. The
jitter function is used to randomly perturb the points; specifying a
factor of 1 produces the default jitter.
Fitted lines are unaffected by the jitter.
with no grouping, this specifies a color for plotted points;
with grouping, this argument should be a vector
of colors of length at least equal to the number of groups. The default is
value returned by carPalette[-1].
plotting characters for points; default is the plotting characters in
order (see par).
sets the size of plotting characters, with cex=1 the standard size. You can also
set the sizes of other elements with the arguments cex.axis, cex.lab, cex.main,
and cex.sub. See par.
if TRUE (the default) then plotting parameters are reset to their previous values
when scatterplot exits; if FALSE then the mar and mfcol parameters are
altered for the current plotting device. Set to FALSE if you want to add graphical elements
(such as lines) to the plot.
other arguments passed down and to plot. For example, the argument las sets
the style of the axis labels, and xlim and ylim set the limits on the horizontal and
vertical axes, respectively; see par.
If points are identified, their labels are returned; otherwise NULL is returned invisibly.
Many arguments to scatterplot were changed in version 3 of car to simplify use of this function.
The smooth argument is used to control adding smooth curves to the plot to estimate the conditional center of the vertical axis variable given the horizontal axis variable, and also the conditional variability. Setting smooth=FALSE omits all smoothers, while smooth=TRUE, the default, includes default smoothers. Alternatively smooth can be set to a list of subarguments that provide finer control over the smoothing.
The default behavior of smooth=TRUE is equivalent to smooth=list(smoother=loessLine, var=!by.groups, lty.var=2, lty.var=4, style="filled", alpha=0.15, border=TRUE, vertical=TRUE), specifying the default loessLine smoother for the conditional mean smooth and variance smooth. The color of the smooths is the same of the color of the points, but this can be changed with the arguments col.smooth and col.var.
Additional available smoothers are gamLine which uses the gam function and quantregLine which uses quantile regression to estimate the median and quartile functions using rqss. All of these smoothers have one or more arguments described on their help pages, and these arguments can be added to the smooth argument; for example, smooth = list(span=1/2) would use the default loessLine smoother, include the variance smooth, and change the value of the smoothing parameter to 1/2.
For loessLine and gamLine the variance smooth is estimated by separately smoothing the squared positive and negative residuals from the mean smooth, using the same type of smoother. The displayed curves are equal to the mean smooth plus the square root of the fit to the positive squared residuals, and the mean fit minus the square root of the smooth of the negative squared residuals. The lines therefore represent the comnditional variabiliity at each value on the horizontal axis. Because smoothing is done separately for positive and negative residuals, the variation shown will generally not be symmetric about the fitted mean function. For the quantregLine method, the center estimates the conditional median, and the variability estimates the lower and upper quartiles of the estimated conditional distribution.
The default depection of the variance functions is via a shaded envelope between the upper and lower estimate of variability. setting the subarguement style="lines" will display only the boundaries of this region, and style="none" suppresses variance smoothing.
For style="filled" several subarguments modify the appearance of the region: codealpha is a number between 0 and 1 that specifies opacity with defualt 0.15, border, TRUE or FALSE specifies a border for the envelope, and vertical either TRUE or FALSE, modifies the behavior of the envelope at the edges of the graph.
The sub-arguments spread, lty.spread and col.spread of the smooth argument are equivalent to the newer var, col.var and lty.var, respectively, recognizing that the spread is a measuure of conditional variability.
Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.
boxplot,
jitter, legend,
scatterplotMatrix, dataEllipse, Boxplot,
cov.trob,
showLabels, ScatterplotSmoothers.
# NOT RUN {
scatterplot(prestige ~ income, data=Prestige, ellipse=TRUE,
smooth=list(style="lines"))
scatterplot(prestige ~ income, data=Prestige,
smooth=list(smoother=quantregLine))
scatterplot(prestige ~ income, data=Prestige,
smooth=list(smoother=quantregLine, border="FALSE"))
# use quantile regression for median and quartile fits
scatterplot(prestige ~ income | type, data=Prestige,
smooth=list(smoother=quantregLine, var=TRUE, span=1, lwd=4, lwd.var=2))
scatterplot(prestige ~ income | type, data=Prestige,
legend=list(coords="topleft"))
scatterplot(vocabulary ~ education, jitter=list(x=1, y=1),
data=Vocab, smooth=FALSE, lwd=3)
scatterplot(infantMortality ~ ppgdp, log="xy", data=UN, id=list(n=5))
scatterplot(income ~ type, data=Prestige)
# }
# NOT RUN {
# remember to exit from point-identification mode
scatterplot(infantMortality ~ ppgdp, id=list(method="identify"), data=UN)
# }
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