car: scatterplot – R documentation

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scatterplot

Enhanced Scatterplots with Marginal Boxplots, Point Marking, Smoothers, and More

Description

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.

Usage

scatterplot(x, ...)

## S3 method for class 'formula'
scatterplot(formula, data, subset, xlab, ylab, id=FALSE,
    legend=TRUE, ...)

## Default S3 method:
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, ...)

Arguments

`x`	vector of horizontal coordinates (or first argument of generic function).
`y`	vector of vertical coordinates.
`formula`	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`	data frame within which to evaluate the formula.
`subset`	expression defining a subset of observations.
`boxplots`	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.
`regLine`	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.
`legend`	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)`.
`id`	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.
`ellipse`	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)`.
`grid`	If TRUE, the default, a light-gray background grid is put on the graph
`smooth`	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.
`groups`	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.
`by.groups`	if `TRUE` (the default when there are groups), regression lines are fit by groups.
`xlab`	label for horizontal axis.
`ylab`	label for vertical axis.
`log`	same as the `log` argument to `plot`, to produce log axes.
`jitter`	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.
`col`	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]`.
`pch`	plotting characters for points; default is the plotting characters in order (see `par`).
`cex`	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`.
`reset.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`.

Details

Many arguments to scatterplot were changed in version 3 of car to simplify use of this function.

The smooth argument is usually either set to TRUE or FALSE to draw, or omit, the smoother. Alternatively smooth can be set to a list of arguments. The default behavior of smooth=TRUE is equivalent to smooth=list(smoother=loessLine, var=!by.groups, lty.var=2, lty.var=4), specifying the smoother to be used, including the variance smooth, and the line widths and types for the curves. You can also specify the colors you want to use for the mean and variance smooths with the arguments col.smooth and col.var. Alternative smoothers are gamline which uses the gam function from the mgcv package, and quantregLine which uses quantile regression to estimate the median and quartile functions using rqss from the quantreg package. 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 median for each value on the horizontal axis, and the variability estimates the lower and upper quartiles of the estimated conditional distribution for each value of the horizontal axis.

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.

Value

If points are identified, their labels are returned; otherwise NULL is returned invisibly.

Author(s)

John Fox jfox@mcmaster.ca

References

Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.

Examples

scatterplot(prestige ~ income, data=Prestige, ellipse=TRUE)

scatterplot(prestige ~ income, data=Prestige, smooth=list(smoother=quantregLine))

# 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)

## End(Not run)

car

Companion to Applied Regression

v3.0-10

GPL (>= 2)

Authors

John Fox [aut, cre], Sanford Weisberg [aut], Brad Price [aut], Daniel Adler [ctb], Douglas Bates [ctb], Gabriel Baud-Bovy [ctb], Ben Bolker [ctb], Steve Ellison [ctb], David Firth [ctb], Michael Friendly [ctb], Gregor Gorjanc [ctb], Spencer Graves [ctb], Richard Heiberger [ctb], Pavel Krivitsky [ctb], Rafael Laboissiere [ctb], Martin Maechler [ctb], Georges Monette [ctb], Duncan Murdoch [ctb], Henric Nilsson [ctb], Derek Ogle [ctb], Brian Ripley [ctb], William Venables [ctb], Steve Walker [ctb], David Winsemius [ctb], Achim Zeileis [ctb], R-Core [ctb]

Initial release

2020-09-23