stat_center | R Documentation |
Compute geometric centers and spreads for ordination factors
stat_center( mapping = NULL, data = NULL, geom = "point", position = "identity", show.legend = NA, inherit.aes = TRUE, ..., fun.data = NULL, fun.center = NULL, fun.min = NULL, fun.max = NULL, fun.args = list() ) stat_star( mapping = NULL, data = NULL, geom = "segment", position = "identity", show.legend = NA, inherit.aes = TRUE, ..., fun.data = NULL, fun.center = NULL, fun.args = list() )
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, either as a
|
position |
Position adjustment, either as a string naming the adjustment
(e.g. |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
... |
Additional arguments passed to |
fun.data, fun.center, fun.min, fun.max, fun.args |
Functions and arguments
treated as in |
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_chull()
,
stat_cone()
,
stat_scale()
,
stat_spantree()
# scaled PCA of Anderson iris measurements iris[, -5] %>% princomp(cor = TRUE) %>% as_tbl_ord() %>% mutate_rows(species = iris$Species) %>% print() -> iris_pca # row-principal biplot with centroid-based stars iris_pca %>% ggbiplot(aes(color = species)) + theme_bw() + scale_color_brewer(type = "qual", palette = 2) + stat_rows_star(alpha = .5, fun.center = "mean") + geom_rows_point(alpha = .5) + stat_rows_center(fun.center = "mean", size = 4, shape = 1L) + ggtitle( "Row-principal PCA biplot of Anderson iris measurements", "Segments connect each observation to its within-species centroid" )
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