R: Three-Dimensional Scatterplots and Point Identification

scatter3d {car}

R Documentation

Three-Dimensional Scatterplots and Point Identification

Description

The scatter3d function uses the rgl package to draw 3D scatterplots with various regression surfaces. The function identify3d allows you to label points interactively with the mouse: Press the right mouse button (on a two-button mouse) or the centre button (on a three-button mouse), drag a rectangle around the points to be identified, and release the button. Repeat this procedure for each point or set of “nearby” points to be identified. To exit from point-identification mode, click the right (or centre) button in an empty region of the plot.

Usage

scatter3d(x, ...)

## S3 method for class 'formula'
scatter3d(formula, data, subset, xlab, ylab, zlab, labels, ...)

## Default S3 method:
scatter3d(x, y, z,
	  xlab=deparse(substitute(x)), ylab=deparse(substitute(y)),
	  zlab=deparse(substitute(z)), axis.scales=TRUE, revolutions=0, 
      bg.col=c("white", "black"),
	  axis.col=if (bg.col == "white") c("darkmagenta", "black", "darkcyan")
		  else c("darkmagenta", "white", "darkcyan"),
	  surface.col=c("blue", "green", "orange", "magenta", "cyan", "red", 
      "yellow", "gray"), surface.alpha=0.5,
	  neg.res.col="red", pos.res.col="green",
	  square.col=if (bg.col == "white") "black" else "gray", point.col="yellow",
	  text.col=axis.col, grid.col=if (bg.col == "white") "black" else "gray",
	  fogtype=c("exp2", "linear", "exp", "none"),
	  residuals=(length(fit) == 1), surface=TRUE, fill=TRUE, grid=TRUE, 
      grid.lines=26, df.smooth=NULL, df.additive=NULL,
	  sphere.size=1, threshold=0.01, speed=1, fov=60, 
	  fit="linear", groups=NULL, parallel=TRUE, ellipsoid=FALSE, level=0.5,
	   ellipsoid.alpha=0.1, id.method=c("mahal", "xz", "y", "xyz", "identify", "none"), 
	  id.n=if (id.method == "identify") Inf else 0,
	  labels=as.character(seq(along=x)), offset = ((100/length(x))^(1/3)) * 0.02,
	  model.summary=FALSE, ...)
    
identify3d(x, y, z, axis.scales=TRUE, groups = NULL, labels = 1:length(x),
	col = c("blue", "green", "orange", "magenta", "cyan", "red", "yellow", "gray"),
	offset = ((100/length(x))^(1/3)) * 0.02)

Arguments

`formula`	“model” formula, of the form `y ~ x + z` or (to plot by groups) `y ~ x + z \| g`, where `g` evaluates to a factor or other variable dividing the data into groups.
`data`	data frame within which to evaluate the formula.
`subset`	expression defining a subset of observations.
`x`	variable for horizontal axis.
`y`	variable for vertical axis (response).
`z`	variable for out-of-screen axis.
`xlab, ylab, zlab`	axis labels.
`axis.scales`	if `TRUE`, label the values of the ends of the axes. Note: For `identify3d` to work properly, the value of this argument must be the same as in `scatter3d`.
`revolutions`	number of full revolutions of the display.
`bg.col`	background colour; one of `"white"`, `"black"`.
`axis.col`	colours for axes; if `axis.scales` is `FALSE`, then the second colour is used for all three axes.
`surface.col`	vector of colours for regression planes, used in the order specified by `fit`.
`surface.alpha`	transparency of regression surfaces, from `0.0` (fully transparent) to `1.0` (opaque); default is `0.5`.
`neg.res.col, pos.res.col`	colours for lines representing negative and positive residuals.
`square.col`	colour to use to plot squared residuals.
`point.col`	colour of points.
`text.col`	colour of axis labels.
`grid.col`	colour of grid lines on the regression surface(s).
`fogtype`	type of fog effect; one of `"exp2"`, `"linear"`, `"exp"`, `"none".`
`residuals`	plot residuals if `TRUE`; if `residuals="squares"`, then the squared residuals are shown as squares (using code adapted from Richard Heiberger). Residuals are available only when there is one surface plotted.
`surface`	plot surface(s) (`TRUE` or `FALSE`).
`fill`	fill the plotted surface(s) with colour (`TRUE` or `FALSE`).
`grid`	plot grid lines on the regression surface(s) (`TRUE` or `FALSE`).
`grid.lines`	number of lines (default, 26) forming the grid, in each of the x and z directions.
`df.smooth`	degrees of freedom for the two-dimensional smooth regression surface; if `NULL` (the default), the `gam` function will select the degrees of freedom for a smoothing spline by generalized cross-validation; if a positive number, a fixed regression spline will be fit with the specified degrees of freedom.
`df.additive`	degrees of freedom for each explanatory variable in an additive regression; if `NULL` (the default), the `gam` function will select degrees of freedom for the smoothing splines by generalized cross-validation; if a positive number or a vector of two positive numbers, fixed regression splines will be fit with the specified degrees of freedom for each term.
`sphere.size`	relative sizes of spheres representing points; the actual size is dependent on the number of observations.
`threshold`	if the actual size of the spheres is less than the threshold, points are plotted instead.
`speed`	relative speed of revolution of the plot.
`fov`	field of view (in degrees); controls degree of perspective.
`fit`	one or more of `"linear"`, `"quadratic"`, `"smooth"`, `"additive"`; to display fitted surface(s); partial matching is supported – e.g., `c("lin", "quad")`.
`groups`	if `NULL` (the default), no groups are defined; if a factor, a different surface or set of surfaces is plotted for each level of the factor; in this event, the colours in `plane.col` are used successively for the points, surfaces, and residuals corresponding to each level of the factor.
`parallel`	when plotting surfaces by `groups`, should the surfaces be constrained to be parallel? A logical value, with default `TRUE`.
`ellipsoid`	plot concentration ellipsoid(s) (`TRUE` or `FALSE`).
`level`	expected proportion of bivariate-normal observations included in the concentration ellipsoid(s); default is 0.5.
`ellipsoid.alpha`	transparency of ellipsoids, from `0.0` (fully transparent) to `1.0` (opaque); default is `0.1`.
`id.method`	if `"mahal"` (the default), relatively extreme points are identified automatically according to their Mahalanobis distances from the centroid (point of means); if `"identify"`, points are identified interactively by right-clicking and dragging a box around them; right-click in an empty area to exit from interactive-point-identification mode; if `"xz"`, identify extreme points in the predictor plane; if `"y"`, identify unusual values of the response; if `"xyz"` identify unusual values of an variable; if `"none"`, no point identification. See `showLabels` for more information.
`id.n`	Number of relatively extreme points to identify automatically (default, `0` unless `id.method="identify"`).
`model.summary`	print summary or summaries of the model(s) fit (`TRUE` or `FALSE`). `scatter3d` rescales the three variables internally to fit in the unit cube; this rescaling will affect regression coefficients.
`labels`	text labels for the points, one for each point; in the `default` method defaults to the observation indices, in the `formula` method to the row names of the data.
`col`	colours for the point labels, given by group. There must be at least as many colours as groups; if there are no groups, the first colour is used. Normally, the colours would correspond to the `plane.col` argument to `scatter3d`.
`offset`	vertical displacement for point labels (to avoid overplotting the points).
`...`	arguments to be passed down.

Value

scatter3d does not return a useful value; it is used for its side-effect of creating a 3D scatterplot. identify3d returns the labels of the identified points.

Note

You have to install the rgl package to produce 3D plots.

Author(s)

John Fox jfox@mcmaster.ca

References

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

Examples

    if(interactive() && require(rgl) && require(mgcv)){
scatter3d(prestige ~ income + education, data=Duncan)
Sys.sleep(5) # wait 5 seconds
scatter3d(prestige ~ income + education | type, data=Duncan)
Sys.sleep(5)
scatter3d(prestige ~ income + education | type, surface=FALSE, 
	ellipsoid=TRUE, revolutions=3, data=Duncan)
scatter3d(prestige ~ income + education, fit=c("linear", "additive"),
	data=Prestige)
	}
	## Not run: 
# drag right mouse button to identify points, click right button in open area to exit
scatter3d(prestige ~ income + education, data=Duncan, id.method="identify")
scatter3d(prestige ~ income + education | type, data=Duncan, id.method="identify")
    
## End(Not run)

[Package car version 2.0-12 Index]