As used in a ggplot2 vignette, this stat
layer restricts a dataset with x
and y
variables to the points that lie
on its convex hull. The biplot extension restricts each matrix factor to
its own hull.
stat_chull(
mapping = NULL,
data = NULL,
geom = "polygon",
position = "identity",
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, either as a
ggproto
Geom
subclass or as a string naming the geom stripped of the
geom_
prefix (e.g. "point"
rather than "geom_point"
)
Position adjustment, either as a string naming the adjustment
(e.g. "jitter"
to use position_jitter
), or the result of a call to a
position adjustment function. Use the latter if you need to change the
settings of the adjustment.
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()
.
Additional arguments passed to ggplot2::layer()
.
A ggproto layer.
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.
The convenience function ord_aes()
can be used to incorporate all
coordinates of the ordination model into a statistical transformation. It
maps the coordinates to the custom aesthetics ..coord1
, ..coord2
, etc.
Some transformations, e.g. stat_center()
, are commutative with projection
to the 'x' and 'y' coordinates. 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
planar biplot when they are computer before or after projection. If such a
stat layer detects these aesthetics, then the lot of them are used in the
transformation.
In either case, the stat layer returns a data frame with position aesthetics
x
and y
.
Other stat layers:
stat_center()
,
stat_cone()
,
stat_scale()
,
stat_spantree()
# correspondence analysis of combined female and male hair and eye color data
HairEyeColor %>%
rowSums(dims = 2L) %>%
MASS::corresp(nf = 2L) %>%
as_tbl_ord() %>%
augment_ord() %>%
print() -> hec_ca
#> # A tbl_ord of class 'correspondence': (4 x 2) x (4 x 2)'
#> # 2 coordinates: Can1 and Can2
#> #
#> # Rows (standard): [ 4 x 2 | 1 ]
#> Can1 Can2 | name
#> | <chr>
#> 1 -1.10 1.44 | 1 Black
#> 2 -0.324 -0.219 | 2 Brown
#> 3 -0.283 -2.14 | 3 Red
#> 4 1.83 0.467 | 4 Blond
#> #
#> # Columns (standard): [ 4 x 2 | 1 ]
#> Can1 Can2 | name
#> | <chr>
#> 1 -1.08 0.592 | 1 Brown
#> 2 1.20 0.556 | 2 Blue
#> 3 -0.465 -1.12 | 3 Hazel
#> 4 0.354 -2.27 | 4 Green
# inertia across artificial coordinates (all singular values < 1)
get_inertia(hec_ca)
#> Can1 Can2
#> 0.20877265 0.02222661
# in row-principal biplot, row coordinates are weighted averages of columns
hec_ca %>%
confer_inertia("rows") %>%
ggbiplot(aes(color = .matrix, fill = .matrix, shape = .matrix)) +
theme_bw() +
stat_cols_chull(alpha = .1) +
geom_cols_point() +
geom_rows_point() +
ggtitle("Row-principal CA of hair & eye color")
# in column-principal biplot, column coordinates are weighted averages of rows
hec_ca %>%
confer_inertia("cols") %>%
ggbiplot(aes(color = .matrix, fill = .matrix, shape = .matrix)) +
theme_bw() +
stat_rows_chull(alpha = .1) +
geom_rows_point() +
geom_cols_point() +
ggtitle("Column-principal CA of hair & eye color")