R: Scatterplot Matrices

scatterplotMatrix {car}

R Documentation

Scatterplot Matrices

Description

Enhanced scatterplot matrices with univariate displays down the diagonal; spm is an abbreviation for scatterplotMatrix. This function just sets up a call to pairs with custom panel functions.

Usage


scatterplotMatrix(x, ...)

## S3 method for class 'formula'
scatterplotMatrix(x, data=NULL, subset, labels, ...)

## Default S3 method:
scatterplotMatrix(x, var.labels = colnames(x), diagonal = c("density", 
    "boxplot", "histogram", "oned", "qqplot", "none"), adjust = 1, 
    nclass, plot.points = TRUE, smooth = TRUE, 
    spread = smooth && !by.groups, span = 0.5, 
    loess.threshold = 5, reg.line = lm, 
    transform = FALSE, family = c("bcPower", "yjPower"), ellipse = FALSE, 
    levels = c(0.5, 0.95), robust = TRUE, groups = NULL, by.groups = FALSE, 
    labels, id.method="mahal", id.n=0, id.cex=1, id.col=palette()[1], 
    col = if (n.groups == 1) palette()[3:1] else rep(palette(), 
        length = n.groups), 
    pch = 1:n.groups, lwd = 1, lwd.smooth = lwd, 
    lwd.spread = lwd, lty = 1, lty.smooth = lty, lty.spread = 2, 
    cex = par("cex"), cex.axis = par("cex.axis"), cex.labels = NULL, 
    cex.main = par("cex.main"), legend.plot = length(levels(groups)) > 
        1, row1attop = TRUE, ...)

spm(x, ...)

Arguments

`x`	a data matrix, numeric data frame, or a one-sided “model” formula, of the form `~ x1 + x2 + ... + xk` or `~ x1 + x2 + ... + xk \| z` where `z` evaluates to a factor or other variable to divide the data into groups.
`data`	for `scatterplotMatrix.formula`, a data frame within which to evaluate the formula.
`subset`	expression defining a subset of observations.
`labels,id.method,id.n,id.cex,id.col`	Arguments for the labelling of points. The default is `id.n=0` for labeling no points. See `showLabels` for details of these arguments. If the plot uses different colors for groups, then the `id.col` argument is ignored and label colors are determined by the `col` argument.
`var.labels`	variable labels (for the diagonal of the plot).
`diagonal`	contents of the diagonal panels of the plot.
`adjust`	relative bandwidth for density estimate, passed to `density` function.
`nclass`	number of bins for histogram, passed to `hist` function.
`plot.points`	if `TRUE` the points are plotted in each off-diagonal panel.
`smooth`	if `TRUE` a loess smooth is plotted in each off-diagonal panel.
`spread`	if `TRUE` (the default when not smoothing by groups), a smoother is applied to the root-mean-square positive and negative residuals from the loess line to display conditional spread and asymmetry.
`span`	span for loess smoother.
`loess.threshold`	suppress the loess smoother if there are fewer than `loess.threshold` unique values (default, 5) of the variable on the vertical axis.
`reg.line`	if not `FALSE` a line is plotted using the function given by this argument; e.g., using `rlm` in package `MASS` plots a robust-regression line.
`transform`	if `TRUE`, multivariate normalizing power transformations are computed with `powerTransform`, rounding the estimated powers to ‘nice’ values for plotting; if a vector of powers, one for each variable, these are applied prior to plotting. If there are `groups` and `by.groups` is `TRUE`, then the transformations are estimated conditional on the `groups` factor.
`family`	family of transformations to estimate: `"bcPower"` for the Box-Cox family or `"yjPower"` for the Yeo-Johnson family (see `powerTransform`).
`ellipse`	if `TRUE` data-concentration ellipses are plotted in the off-diagonal panels.
`levels`	levels or levels at which concentration ellipses are plotted; the default is `c(.5, .9)`.
`robust`	if `TRUE` use the `cov.trob` function in the `MASS` package to calculate the center and covariance matrix for the data ellipses.
`groups`	a factor or other variable dividing the data into groups; groups are plotted with different colors and plotting characters.
`by.groups`	if `TRUE`, regression lines are fit by groups.
`pch`	plotting characters for points; default is the plotting characters in order (see `par`).
`col`	colors for lines and points; the default is taken from the color palette, with `palette()[3]` for linear regression lines, `palette()[2]` for nonparametric regression lines, and `palette()[1]` for points if there are no groups, and successive colors for the groups if there are groups.
`lwd`	width of linear-regression lines (default `1`).
`lwd.smooth`	width for smooth regression lines (default is the same as `lwd`).
`lwd.spread`	width for lines showing spread (default is the same as `lwd`).
`lty`	type of linear-regression lines (default `1`, solid line).
`lty.smooth`	type of smooth regression lines (default is the same as `lty`).
`lty.spread`	width for lines showing spread (default is `2`, broken line).
`cex, cex.axis, cex.labels, cex.main`	set sizes of various graphical elements (see `par`).
`legend.plot`	if `TRUE` then a legend for the groups is plotted in the first diagonal cell.
`row1attop`	If `TRUE` (the default) the first row is at the top, as in a matrix, as opposed to at the bottom, as in graph (argument suggested by Richard Heiberger).
`...`	arguments to pass down.

Value

NULL. This function is used for its side effect: producing a plot.

Author(s)

John Fox jfox@mcmaster.ca

References

Fox, J. and Weisberg, S. (2011) An R Companion to Applied Regression, Second Edition, Sage.

Examples

scatterplotMatrix(~ income + education + prestige | type, data=Duncan)
scatterplotMatrix(~ income + education + prestige, 
    transform=TRUE, data=Duncan)
scatterplotMatrix(~ income + education + prestige | type, smooth=FALSE, 
	by.group=TRUE, transform=TRUE, data=Duncan)

[Package car version 2.0-12 Index]