stat_depth {gggda} | R Documentation |
Depth estimates and contours
Description
Estimate data depth using ddalpha::depth.()
.
Usage
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,
...
)
Arguments
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
geom |
The geometric object to use to display the data for this layer.
When using a
|
position |
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
|
contour |
If |
contour_var |
Character string identifying the variable to contour by.
Can be one of |
notion |
Character; the name of the depth function (passed to
|
notion_params |
List of additional parameters passed via |
n |
Number of grid points in each direction. |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
... |
Arguments passed on to
|
Details
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()
.
Value
Multidimensional position aesthetics
This statistical transformation is compatible with the convenience function
aes_coord()
.
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
these aesthetics but will accept aes_coord()
without warning.
Computed variables
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.
References
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
See Also
Other stat layers:
stat_bagplot()
,
stat_center()
,
stat_chull()
,
stat_cone()
,
stat_rule()
,
stat_scale()
,
stat_spantree()
Examples
# 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
b + stat_depth_filled(contour_var = "ndepth", alpha = .75) +
facet_wrap(facets = vars(factor(cyl)))