Estimate data depth using ddalpha::depth.()
.
stat_depth(
mapping = NULL,
data = NULL,
geom = "contour",
position = "identity",
contour = TRUE,
contour_var = "depth",
notion = "zonoid",
notion_params = list(),
n = 100L,
show.legend = NA,
inherit.aes = TRUE,
...
)
stat_depth_filled(
mapping = NULL,
data = NULL,
geom = "contour_filled",
position = "identity",
contour = TRUE,
contour_var = "depth",
notion = "zonoid",
notion_params = list(),
n = 100L,
show.legend = NA,
inherit.aes = TRUE,
...
)
Set of aesthetic mappings created by aes()
. If specified and
inherit.aes = TRUE
(the default), it is combined with the default mapping
at the top level of the plot. You must supply mapping
if there is no plot
mapping.
The data to be displayed in this layer. There are three options:
If NULL
, the default, the data is inherited from the plot
data as specified in the call to ggplot()
.
A data.frame
, or other object, will override the plot
data. All objects will be fortified to produce a data frame. See
fortify()
for which variables will be created.
A function
will be called with a single argument,
the plot data. The return value must be a data.frame
, and
will be used as the layer data. A function
can be created
from a formula
(e.g. ~ head(.x, 10)
).
The geometric object to use to display the data for this layer.
When using a stat_*()
function to construct a layer, the geom
argument
can be used to override the default coupling between stats and geoms. The
geom
argument accepts the following:
A Geom
ggproto subclass, for example GeomPoint
.
A string naming the geom. To give the geom as a string, strip the
function name of the geom_
prefix. For example, to use geom_point()
,
give the geom as "point"
.
For more information and other ways to specify the geom, see the layer geom documentation.
A position adjustment to use on the data for this layer. This
can be used in various ways, including to prevent overplotting and
improving the display. The position
argument accepts the following:
The result of calling a position function, such as position_jitter()
.
This method allows for passing extra arguments to the position.
A string naming the position adjustment. To give the position as a
string, strip the function name of the position_
prefix. For example,
to use position_jitter()
, give the position as "jitter"
.
For more information and other ways to specify the position, see the layer position documentation.
If TRUE
, contour the results of the depth estimation.
Character string identifying the variable to contour by.
Can be one of "depth"
or "ndepth"
. See the section on computed
variables for details.
Character; the name of the depth function (passed to
ddalpha::depth.()
).
List of additional parameters passed via ...
to
ddalpha::depth.()
.
Number of grid points in each direction.
logical. Should this layer be included in the legends?
NA
, the default, includes if any aesthetics are mapped.
FALSE
never includes, and TRUE
always includes.
It can also be a named logical vector to finely select the aesthetics to
display.
If FALSE
, overrides the default aesthetics,
rather than combining with them. This is most useful for helper functions
that define both data and aesthetics and shouldn't inherit behaviour from
the default plot specification, e.g. borders()
.
Arguments passed on to ggplot2::geom_contour
bins
Number of contour bins. Overridden by breaks
.
binwidth
The width of the contour bins. Overridden by bins
.
breaks
One of:
Numeric vector to set the contour breaks
A function that takes the range of the data and binwidth as input and returns breaks as output. A function can be created from a formula (e.g. ~ fullseq(.x, .y)).
Overrides binwidth
and bins
. By default, this is a vector of length
ten with pretty()
breaks.
A ggproto layer.
Depth is an extension of the univariate notion of rank to bivariate (and sometimes multivariate) data (Rousseeuw &al, 1999). It comes in several flavors and is the basis for bagplots.
stat_depth()
is adapted from ggplot2::stat_density_2d()
and returns
depth values over a grid in the same format, so it is neatly paired with
ggplot2::geom_contour()
.
ggbiplot()
uses ggplot2::fortify()
internally to produce a single data
frame with a .matrix
column distinguishing the subjects ("rows"
) and
variables ("cols"
). The stat layers stat_rows()
and stat_cols()
simply
filter the data frame to one of these two.
The geom layers geom_rows_*()
and geom_cols_*()
call the corresponding
stat in order to render plot elements for the corresponding factor matrix.
geom_dims_*()
selects a default matrix based on common practice, e.g.
points for rows and arrows for columns.
This statistical transformation is compatible with the convenience function
ord_aes()
.
Some transformations (e.g. stat_center()
) commute with projection to the
lower (1 or 2)-dimensional biplot space. If they detect aesthetics of the
form ..coord[0-9]+
, then ..coord1
and ..coord2
are converted to x
and
y
while any remaining are ignored.
Other transformations (e.g. stat_spantree()
) yield different results in a
lower-dimensional biplot when they are computed before versus after
projection. If the stat layer detects these aesthetics, then the
transformation is performed before projection, and the results in the first
two dimensions are returned as x
and y
.
A small number of transformations (stat_rule()
) are incompatible with
ordination aesthetics but will accept ord_aes()
without warning.
These are calculated during the statistical transformation and can be accessed with delayed evaluation.
stat_depth()
and stat_depth_filled()
compute different variables
depending on whether contouring is turned on or off. With contouring off
(contour = FALSE
), both stats behave the same, and the following
variables are provided:
depth
the depth estimate
ndepth
depth estimate, scaled to a maximum of 1
With contouring on (contour = TRUE
), either ggplot2::stat_contour()
or
ggplot2::stat_contour_filled()
is run after the depth estimate has been
obtained, and the computed variables are determined by these stats.
Rousseeuw PJ, Ruts I, & Tukey JW (1999) "The Bagplot: A Bivariate Boxplot". The American Statistician, 53(4): 382–387. doi:10.1080/00031305.1999.10474494
Other stat layers:
stat_bagplot()
,
stat_center()
,
stat_chull()
,
stat_cone()
,
stat_projection()
,
stat_rule()
,
stat_scale()
,
stat_spantree()
# base Motor Trends plot
b <- ggplot(mtcars, aes(wt, disp)) + geom_point()
# depth raster
b + geom_raster(stat = "depth", aes(fill = after_stat(depth)))
# depth grid
b + stat_depth(
geom = "point", contour = FALSE,
aes(size = after_stat(depth)), n = 20
)
# depth contours
b + geom_contour(stat = "depth", contour = TRUE)
# depth bands
b + geom_contour_filled(stat = "depth_filled", contour = TRUE, alpha = .75)
# contours colored by group
b + stat_depth(aes(color = factor(cyl)))
# custom depth notion
b + stat_depth(
aes(color = factor(cyl)),
notion = "halfspace", notion_params = list(exact = TRUE)
)
# contours faceted by group
b + stat_depth_filled(alpha = .75) +
facet_wrap(facets = vars(factor(cyl)))
# scaled to the unit interval
# FIXME: Some polygons are missing.
b + stat_depth_filled(contour_var = "ndepth", alpha = .75) +
facet_wrap(facets = vars(factor(cyl)))