biplot-stats | R Documentation |
These statistical transformations (stats) adapt
conventional ggplot2 stats to one or the other matrix factor
of a tbl_ord, in lieu of stat_rows()
or stat_cols()
. They
accept the same parameters as their corresponding conventional
stats.
stat_rows_ellipse( mapping = NULL, data = NULL, geom = "path", position = "identity", ..., type = "t", level = 0.95, segments = 51, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE ) stat_cols_ellipse( mapping = NULL, data = NULL, geom = "path", position = "identity", ..., type = "t", level = 0.95, segments = 51, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE ) stat_rows_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_cols_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_rows_star( mapping = NULL, data = NULL, geom = "segment", position = "identity", show.legend = NA, inherit.aes = TRUE, ..., fun.data = NULL, fun.center = NULL, fun.args = list() ) stat_cols_star( mapping = NULL, data = NULL, geom = "segment", position = "identity", show.legend = NA, inherit.aes = TRUE, ..., fun.data = NULL, fun.center = NULL, fun.args = list() ) stat_rows_chull( mapping = NULL, data = NULL, geom = "polygon", position = "identity", show.legend = NA, inherit.aes = TRUE, ... ) stat_cols_chull( mapping = NULL, data = NULL, geom = "polygon", position = "identity", show.legend = NA, inherit.aes = TRUE, ... ) stat_rows_cone( mapping = NULL, data = NULL, geom = "path", position = "identity", origin = FALSE, show.legend = NA, inherit.aes = TRUE, ... ) stat_cols_cone( mapping = NULL, data = NULL, geom = "path", position = "identity", origin = FALSE, show.legend = NA, inherit.aes = TRUE, ... ) stat_rows_scale( mapping = NULL, data = NULL, geom = "point", position = "identity", show.legend = NA, inherit.aes = TRUE, ..., mult = 1 ) stat_cols_scale( mapping = NULL, data = NULL, geom = "point", position = "identity", show.legend = NA, inherit.aes = TRUE, ..., mult = 1 ) stat_rows_spantree( mapping = NULL, data = NULL, geom = "segment", position = "identity", engine = "mlpack", method = "euclidean", show.legend = NA, inherit.aes = TRUE, ... ) stat_cols_spantree( mapping = NULL, data = NULL, geom = "segment", position = "identity", engine = "mlpack", method = "euclidean", show.legend = NA, inherit.aes = TRUE, ... )
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. |
... |
Additional arguments passed to |
type |
The type of ellipse.
The default |
level |
The level at which to draw an ellipse,
or, if |
segments |
The number of segments to be used in drawing the ellipse. |
na.rm |
If |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
fun.data, fun.center, fun.min, fun.max, fun.args |
Functions and arguments
treated as in |
origin |
Logical; whether to include the origin with the transformed
data. Defaults to |
mult |
Numeric value used to scale the coordinates. |
engine |
A single character string specifying the package implementation
to use; |
method |
Passed to |
A ggproto layer.
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 biplot layers:
biplot-geoms
,
stat_rows()
# compute row-principal components of scaled iris measurements iris[, -5] %>% prcomp(scale = TRUE) %>% as_tbl_ord() %>% mutate_rows(species = iris$Species) %>% print() -> iris_pca # row-principal biplot with centroids and confidence elliptical disks iris_pca %>% ggbiplot(aes(color = species)) + theme_bw() + geom_rows_point() + geom_polygon( aes(fill = species), color = NA, alpha = .25, stat = "rows_ellipse" ) + geom_cols_vector(color = "#444444") + scale_color_brewer( type = "qual", palette = 2, aesthetics = c("color", "fill") ) + ggtitle( "Row-principal PCA biplot of Anderson iris measurements", "Overlaid with 95% confidence disks" )
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