levelplot {lattice} | R Documentation |
Draw Level Plots and Contour plots.
levelplot(x, data, ...) contourplot(x, data, ...) ## S3 method for class 'formula': levelplot(x, data, allow.multiple = is.null(groups) || outer, outer = TRUE, aspect = "fill", panel = lattice.getOption("panel.levelplot"), prepanel = NULL, scales = list(), strip = TRUE, groups = NULL, xlab, xlim, ylab, ylim, at, cuts = 15, pretty = FALSE, region = TRUE, drop.unused.levels = lattice.getOption("drop.unused.levels"), ..., lattice.options = NULL, default.scales = list(), colorkey = region, col.regions, alpha.regions, subset = TRUE) ## S3 method for class 'formula': contourplot(x, data, panel = lattice.getOption("panel.contourplot"), cuts = 7, labels = TRUE, contour = TRUE, pretty = TRUE, region = FALSE, ...) ## S3 method for class 'table': levelplot(x, data = NULL, aspect = "iso", ..., xlim, ylim) ## S3 method for class 'table': contourplot(x, data = NULL, aspect = "iso", ..., xlim, ylim) ## S3 method for class 'matrix': levelplot(x, data = NULL, aspect = "iso", ..., xlim, ylim, row.values = seq_len(nrow(x)), column.values = seq_len(ncol(x))) ## S3 method for class 'matrix': contourplot(x, data = NULL, aspect = "iso", ..., xlim, ylim, row.values = seq_len(nrow(x)), column.values = seq_len(ncol(x))) ## S3 method for class 'array': levelplot(x, data = NULL, ...) ## S3 method for class 'array': contourplot(x, data = NULL, ...)
x |
for the formula method, a formula of the form z ~ x * y
| g1 * g2 * ... , where z is a numeric response, and
x , y are numeric values evaluated on a rectangular
grid. g1, g2, ... are optional conditional variables, and
must be either factors or shingles if present.
Calculations are based on the assumption that all x and y values are evaluated on a grid (defined by their unique values). The function will not return an error if this is not true, but the display might not be meaningful. However, the x and y values need not be equally spaced. Both levelplot and wireframe have methods for
matrix , array , and table 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
filled.contour and image . For higher-dimensional
arrays and tables, further dimensions are used as conditioning
variables. Note that the dimnames may be duplicated; this is
handled by calling make.unique to make the names
unique (although the original labels are used for the x- and
y-axes).
|
data |
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. Usually ignored with a
warning in other cases.
|
row.values, column.values |
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. |
panel |
panel function used to create the display, as described in
xyplot
|
aspect |
For the matrix methods, the default aspect ratio is chosen to
make each cell square. The usual default is aspect="fill" ,
as described in xyplot .
|
at |
numeric vector giving breakpoints along the range of
z . Contours (if any) will be drawn at these heights, and the
regions in between would be colored using col.regions . In
the latter case, values outside the range of at will not be
drawn at all. This serves as a way to limit the range of the data
shown, similar to what a zlim argument might have been used
for. However, this also means that when supplying at
explicitly, one has to be careful to include values outside the
range of z to ensure that all the data are shown.
|
col.regions |
color vector to be used if regions is TRUE. The
general idea is that this should be a color vector of moderately
large length (longer than the number of regions. By default this is
100). It is expected that this vector would be gradually varying in
color (so that nearby colors would be similar). When the colors are
actually chosen, they are chosen to be equally spaced along this
vector. When there are more regions than colors in
col.regions , the colors are recycled. The actual color
assignment is performed by level.colors , which is
documented separately.
|
alpha.regions |
numeric, specifying alpha transparency (works only on some devices) |
colorkey |
logical specifying whether a color key is to be drawn
alongside the plot, or a list describing the color key. The list may
contain the following components:
|
contour |
logical, whether to draw contour lines. |
cuts |
number of levels the range of z would be divided into
|
labels |
typically a logical indicating whether contour lines should be
labelled, but other possibilities for more sophisticated control
exists. Details are documented in the help page for
panel.levelplot , to which this argument is passed on
unchanged. That help page also documents the label.style
argument, which affects how the labels are rendered.
|
pretty |
logical, whether to use pretty cut locations and labels |
region |
logical, whether regions between contour lines should be filled |
allow.multiple, outer, prepanel, scales, strip, groups, xlab,
xlim, ylab, ylim, drop.unused.levels, lattice.options,
default.scales, subset |
these arguments are described in the help page for
xyplot .
|
... |
other arguments. Some are processed by levelplot
or contourplot , and those unrecognized are passed on to the
panel function. |
These and all other high level Trellis functions have several
arguments in common. These are extensively documented only in the
help page for xyplot
, which should be consulted to learn more
detailed usage.
Other useful arguments are mentioned in the help page for the default
panel function panel.levelplot
(these are formally
arguments to the panel function, but can be specified in the high
level calls directly).
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.
Deepayan Sarkar Deepayan.Sarkar@R-project.org
Sarkar, Deepayan (2008) "Lattice: Multivariate Data Visualization with R", Springer. http://lmdvr.r-forge.r-project.org/
xyplot
, Lattice
,
panel.levelplot
x <- seq(pi/4, 5 * pi, length.out = 100) y <- seq(pi/4, 5 * pi, length.out = 100) r <- as.vector(sqrt(outer(x^2, y^2, "+"))) grid <- expand.grid(x=x, y=y) grid$z <- cos(r^2) * exp(-r/(pi^3)) levelplot(z~x*y, grid, cuts = 50, scales=list(log="e"), xlab="", ylab="", main="Weird Function", sub="with log scales", colorkey = FALSE, region = TRUE) #S-PLUS example require(stats) attach(environmental) ozo.m <- loess((ozone^(1/3)) ~ wind * temperature * radiation, parametric = c("radiation", "wind"), span = 1, degree = 2) w.marginal <- seq(min(wind), max(wind), length.out = 50) t.marginal <- seq(min(temperature), max(temperature), length.out = 50) r.marginal <- seq(min(radiation), max(radiation), length.out = 4) wtr.marginal <- list(wind = w.marginal, temperature = t.marginal, radiation = r.marginal) grid <- expand.grid(wtr.marginal) grid[, "fit"] <- c(predict(ozo.m, grid)) contourplot(fit ~ wind * temperature | radiation, data = grid, cuts = 10, region = TRUE, xlab = "Wind Speed (mph)", ylab = "Temperature (F)", main = "Cube Root Ozone (cube root ppb)") detach()