scatter3d: 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, radius, xlab, ylab, zlab, labels, ...)

## S3 method for class 'default':
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, radius=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.

variable for horizontal axis.

variable for vertical axis (response).

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; for multi-group plots, the colours are used for the regression surfaces and points in the several groups.

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

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 positiv

sphere.size

general size of spheres representing points; the actual size is dependent on the number of observations.

radius

relative radii of the spheres representing the points. This is normally a vector of the same length as the variables giving the coordinates of the points, and for the formula method, that must be the case or the argument may be omit

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 surface.col are used successively for the points,

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-cli

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 surface.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.

References

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

Examples

Run this code

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)
Sys.sleep(5)
scatter3d(prestige ~ income + education | type, 
    radius=(1 + women)^(1/3), data=Prestige)
	}
	# 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")