# scatter3d

##### Three-Dimensional Scatterplots and Point Identification

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.

- Keywords
- hplot

##### Usage

`scatter3d(x, ...)`# S3 method for formula
scatter3d(formula, data, subset, radius, xlab, ylab, zlab, id=FALSE, ...)

# S3 method for default
scatter3d(x, y, z,
xlab=deparse(substitute(x)), ylab=deparse(substitute(y)),
zlab=deparse(substitute(z)), axis.scales=TRUE, axis.ticks=FALSE,
revolutions=0,
bg.col=c("white", "black"),
axis.col=if (bg.col == "white") c("darkmagenta", "black", "darkcyan")
else c("darkmagenta", "white", "darkcyan"),
surface.col=carPalette()[-1], surface.alpha=0.5,
neg.res.col="magenta", pos.res.col="cyan",
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=FALSE,
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`

.- axis.ticks
if

`TRUE`

, print interior axis-``tick'' labels; the default is`FALSE`

. (The code for this option was provided by David Winsemius.)- 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 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
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 omitted, in which case spheres are the same size; for the`default`

method, the default for the argument,`1`

, produces spheres all of the same size. The radii are scaled so that their median is 1.- 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, 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
`FALSE`

,`TRUE`

, or a list controlling point identification, similar to`showLabels`

for 2D plots (see Details).- 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; defaults to the observation indices.

- 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.

##### Details

The `id`

argument to `scatter3d`

can be `FALSE`

, `TRUE`

(in which case 2
points will be identified according to their Mahalanobis distances from the center of the data),
or a list containing any or all of the following elements:

- 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.- n
Number of relatively extreme points to identify automatically (default,

`2`

, unless`method="identify"`

, in which case identification continues until the user exits).- 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.- offset
vertical displacement for point labels (to avoid overplotting the points).

##### 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. On a Macintosh (but not on Windows or Linux), you may also need to install the X11 windowing system. Go to https://www.xquartz.org/ and click on the link for XQuartz. Double-click on the downloaded disk-image file, and then double-click on `XQuartz.pkg`

to start the installer. You may take all of the defaults in the installation. After XQuartz is installed, you should restart your Macintosh.

##### References

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

##### See Also

##### Examples

```
# NOT RUN {
if(interactive() && require(rgl) && require(mgcv)){
scatter3d(prestige ~ income + education, data=Duncan, id=list(n=3))
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)
}
# }
# NOT RUN {
# drag right mouse button to identify points, click right button in open area to exit
scatter3d(prestige ~ income + education, data=Duncan, id=list(method="identify"))
scatter3d(prestige ~ income + education | type, data=Duncan, id=list(method="identify"))
# }
```

*Documentation reproduced from package car, version 3.0-0, License: GPL (>= 2)*