Generic functions to draw 3d scatter plots and surfaces. The
"formula" methods do most of the actual work.
cloud(x, data, ...)
wireframe(x, data, ...)# S3 method for formula
cloud(x,
data,
allow.multiple = is.null(groups) || outer,
outer = FALSE,
auto.key = lattice.getOption("default.args")$auto.key,
aspect = c(1,1),
panel.aspect = 1,
panel = lattice.getOption("panel.cloud"),
prepanel = NULL,
scales = list(),
strip = TRUE,
groups = NULL,
xlab,
ylab,
zlab,
xlim = if (is.factor(x)) levels(x) else range(x, finite = TRUE),
ylim = if (is.factor(y)) levels(y) else range(y, finite = TRUE),
zlim = if (is.factor(z)) levels(z) else range(z, finite = TRUE),
at,
drape = FALSE,
pretty = FALSE,
drop.unused.levels,
...,
lattice.options = NULL,
default.scales =
list(distance = c(1, 1, 1),
arrows = TRUE,
axs = axs.default),
default.prepanel = lattice.getOption("prepanel.default.cloud"),
colorkey,
col.regions,
alpha.regions,
cuts = 70,
subset = TRUE,
axs.default = "r")
# S3 method for data.frame
cloud(x, data = NULL, formula = data, ...)
# S3 method for formula
wireframe(x,
data,
panel = lattice.getOption("panel.wireframe"),
default.prepanel = lattice.getOption("prepanel.default.wireframe"),
...)
# S3 method for data.frame
wireframe(x, data = NULL, formula = data, ...)
# S3 method for matrix
cloud(x, data = NULL, type = "h",
zlab = deparse(substitute(x)), aspect, ...,
xlim, ylim, row.values, column.values)
# S3 method for table
cloud(x, data = NULL, groups = FALSE,
zlab = deparse(substitute(x)),
type = "h", ...)
# S3 method for matrix
wireframe(x, data = NULL,
zlab = deparse(substitute(x)), aspect, ...,
xlim, ylim, row.values, column.values)
An object of class "trellis". The
update method can be used to
update components of the object and the
print method (usually called by
default) will plot it on an appropriate plotting device.
The object on which method dispatch is carried out.
For the "formula" methods, a formula of the form z ~ x
* y | g1 * g2 * ..., where z is a numeric response, and
x, y are numeric values. g1, g2, ..., if
present, are conditioning variables used for conditioning, and must
be either factors or shingles. In the case of wireframe,
calculations are based on the assumption that the x and
y values are evaluated on a rectangular grid defined by their
unique values. The grid points need not be equally spaced.
For wireframe, x, y and z may also be
matrices (of the same dimension), in which case they are taken to
represent a 3-D surface parametrized on a 2-D grid (e.g., a sphere).
Conditioning is not possible with this feature. See details below.
Missing values are allowed, either as NA values in the
z vector, or missing rows in the data frame (note however
that in that case the X and Y grids will be determined only by the
available values). For a grouped display (producing multiple
surfaces), missing rows are not allowed, but NA-s in z
are.
Both wireframe and cloud have methods for
matrix objects, in which case x provides the
z vector described above, while its rows and columns are
interpreted as the x and y vectors respectively. This
is similar to the form used in persp.
For the "formula" methods, an optional data frame in which
variables in the formula (as well as groups and
subset, if any) are to be evaluated. data should not
be specified except when using the "formula" method.
The formula to be used for the "data.frame" methods. See
documentation for argument x for details.
Optional vectors of values that
define the grid when x is a matrix. row.values and
column.values must have the same lengths as nrow(x)
and ncol(x) respectively. By default, row and column
numbers.
These arguments are documented in the help page for
xyplot. For the cloud.table method,
groups must be a logical indicating whether the last
dimension should be used as a grouping variable as opposed to a
conditioning variable. This is only relevant if the table has more
than 2 dimensions.
type of display in cloud (see panel.3dscatter
for details). Defaults to "h" for the matrix method.
Unlike other high level functions, aspect is taken to be a
numeric vector of length 2, giving the relative aspects of the
y-size/x-size and z-size/x-size of the enclosing cube. The usual
role of the aspect argument in determining the aspect ratio
of the panel (see xyplot for details) is played by
panel.aspect, except that it can only be a numeric value.
For the matrix methods, the default y/x aspect is
ncol(x) / nrow(x) and the z/x aspect is the smaller of the
y/x aspect and 1.
panel function used to create the display. See
panel.cloud for (non-trivial) details.
Fallback prepanel function. See xyplot.
a list describing the scales. As with other high level functions
(see xyplot for details), this list can contain
parameters in name=value form. It can also contain components with
the special names x, y and z, which can be
similar lists with axis-specific values overriding the ones
specified in scales.
The most common use for this argument is to set arrows=FALSE,
which causes tick marks and labels to be used instead of arrows
being drawn (the default). Both can be suppressed by
draw=FALSE. Another special component is distance,
which specifies the relative distance of the axis label from the
bounding box. If specified as a component of scales (as
opposed to one of scales$z etc), this can be (and is recycled
if not) a vector of length 3, specifying distances for the x, y and
z labels respectively.
Other components that work in the scales argument of
xyplot etc. should also work here (as long as they make
sense), including explicit specification of tick mark locations and
labels. (Not everything is implemented yet, but if you find
something that should work but does not, feel free to bug the
maintainer.)
Note, however, that for these functions scales cannot contain
information that is specific to particular panels. If you really
need that, consider using the scales.3d argument of
panel.cloud.
Unlike 2-D display functions, cloud does not expand the
bounding box to slightly beyound the range of the data, even though
it should. This is primarily because this is the natural behaviour
in wireframe, which uses the same code. axs.default
is intended to provide a different default for cloud.
However, this feature has not yet been implemented.
Specifies a label describing the z variable in ways similar to
xlab and ylab (i.e. “grob”, character string,
expression or list) in other high level functions. Additionally, if
zlab (and xlab and ylab) is a list, it can
contain a component called rot, controlling the rotation for
the label
limits for the z-axis. Similar to xlim and ylim in
other high level functions
logical, whether the wireframe is to be draped in color. If
TRUE, the height of a facet is used to determine its color in
a manner similar to the coloring scheme used in
levelplot. Otherwise, the background color is used to
color the facets. This argument is ignored if shade = TRUE
(see panel.3dwire).
these arguments are analogous to those in
levelplot. if drape=TRUE, at gives the
vector of cutpoints where the colors change, and col.regions
the vector of colors to be used in that case. alpha.regions
determines the alpha-transparency on supporting devices. These are
passed down to the panel function, and also used in the colorkey if
appropriate. The default for col.regions and
alpha.regions is derived from the Trellis setting
"regions"
if at is unspecified, the approximate number of cutpoints if
drape=TRUE
whether automatic choice of cutpoints should be prettfied
logical indicating whether a color key should be drawn
alongside, or a list describing such a key. See
levelplot for details.
Any number of other arguments can be specified, and are passed to
the panel function. In particular, the arguments distance,
perspective, screen and R.mat are very
important in determining the 3-D display. The argument shade
can be useful for wireframe calls, and controls shading of
the rendered surface. These arguments are described in detail in
the help page for panel.cloud.
Additionally, an argument called zoom may be specified, which
should be a numeric scalar to be interpreted as a scale factor by
which the projection is magnified. This can be useful to get the
variable names into the plot. This argument is actually only used
by the default prepanel function.
Deepayan Sarkar Deepayan.Sarkar@R-project.org
These functions produce three dimensional plots in each panel (as long
as the default panel functions are used). The orientation is obtained
as follows: the data are scaled to fall within a bounding box that is
contained in the [-0.5, 0.5] cube (even smaller for non-default values
of aspect). The viewing direction is given by a sequence of
rotations specified by the screen argument, starting from the
positive Z-axis. The viewing point (camera) is located at a distance
of 1/distance from the origin. If perspective=FALSE,
distance is set to 0 (i.e., the viewing point is at an infinite
distance).
cloud draws a 3-D Scatter Plot, while wireframe draws a
3-D surface (usually evaluated on a grid). Multiple surfaces can be
drawn by wireframe using the groups argument (although
this is of limited use because the display is incorrect when the
surfaces intersect). Specifying groups with cloud
results in a panel.superpose-like effect (via
panel.3dscatter).
wireframe can optionally render the surface as being
illuminated by a light source (no shadows though). Details can be
found in the help page for panel.3dwire. Note that
although arguments controlling these are actually arguments for the
panel function, they can be supplied to cloud and
wireframe directly.
For single panel plots, wireframe can also plot parametrized
3-D surfaces (i.e., functions of the form f(u,v) = (x(u,v), y(u,v),
z(u,v)), where values of (u,v) lie on a rectangle. The simplest
example of this sort of surface is a sphere parametrized by latitude
and longitude. This can be achieved by calling wireframe with a
formula x of the form z~x*y, where x, y
and z are all matrices of the same dimension, representing the
values of x(u,v), y(u,v) and z(u,v) evaluated on a discrete
rectangular grid (the actual values of (u,v) are irrelevant).
When this feature is used, the heights used to calculate drape
colors or shading colors are no longer the z values, but the
distances of (x,y,z) from the origin.
Note that this feature does not work with groups,
subscripts, subset, etc. Conditioning variables are also
not supported in this case.
The algorithm for identifying which edges of the bounding box are
‘behind’ the points doesn't work in some extreme
situations. Also, panel.cloud tries to figure out the
optimal location of the arrows and axis labels automatically, but can
fail on occasion (especially when the view is from ‘below’ the
data). This can be manually controlled by the scpos argument in
panel.cloud.
These and all other high level Trellis functions have several other
arguments in common. These are extensively documented only in the
help page for xyplot, which should be consulted to learn
more detailed usage.
Sarkar, Deepayan (2008) Lattice: Multivariate Data Visualization with R, Springer. http://lmdvr.r-forge.r-project.org/
Lattice for an overview of the package, as well as
xyplot, levelplot,
panel.cloud.
For interaction, see panel.identify.cloud.
## volcano ## 87 x 61 matrix
wireframe(volcano, shade = TRUE,
aspect = c(61/87, 0.4),
light.source = c(10,0,10))
g <- expand.grid(x = 1:10, y = 5:15, gr = 1:2)
g$z <- log((g$x^g$gr + g$y^2) * g$gr)
wireframe(z ~ x * y, data = g, groups = gr,
scales = list(arrows = FALSE),
drape = TRUE, colorkey = TRUE,
screen = list(z = 30, x = -60))
cloud(Sepal.Length ~ Petal.Length * Petal.Width | Species, data = iris,
screen = list(x = -90, y = 70), distance = .4, zoom = .6)
## cloud.table
cloud(prop.table(Titanic, margin = 1:3),
type = c("p", "h"), strip = strip.custom(strip.names = TRUE),
scales = list(arrows = FALSE, distance = 2), panel.aspect = 0.7,
zlab = "Proportion")[, 1]
## transparent axes
par.set <-
list(axis.line = list(col = "transparent"),
clip = list(panel = "off"))
print(cloud(Sepal.Length ~ Petal.Length * Petal.Width,
data = iris, cex = .8,
groups = Species,
main = "Stereo",
screen = list(z = 20, x = -70, y = 3),
par.settings = par.set,
scales = list(col = "black")),
split = c(1,1,2,1), more = TRUE)
print(cloud(Sepal.Length ~ Petal.Length * Petal.Width,
data = iris, cex = .8,
groups = Species,
main = "Stereo",
screen = list(z = 20, x = -70, y = 0),
par.settings = par.set,
scales = list(col = "black")),
split = c(2,1,2,1))
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