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 scatter3d(x, ...)
## S3 method for class 'formula':
scatter3d(formula, data, subset, 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"),
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,
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)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.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."white", "black".axis.scales is FALSE, then
the second colour is used for all three axes.fit."exp2", "linear",
"exp", "none".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.TRUE or FALSE).TRUE or FALSE).TRUE or FALSE).NULL (the default), the gam function will select the degrees of freedom
for a smoothing spline by generalized NULL (the default), the gam function will select degrees of freedom
for the smoothing splines by generalized cross-validation; if a positiv"linear", "quadratic", "smooth",
"additive"; to display fitted surface(s); partial matching is supported --
e.g., c("lin", "quad").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, sgroups, should the surfaces be constrained to be
parallel? A logical value, with default TRUE.TRUE or FALSE)."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-cli0 unless
id.method="identify").TRUE or FALSE). scatter3d rescales the three variables
internally to fit in the unit cube; this rescaling will affect regression
coefficients.default method defaults to
the observation indices, in the formula method to the row names of the data.plane.col argument to scatter3dscatter3d 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.rgl-package, gamif(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)
}
# 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")Run the code above in your browser using DataLab