discrimintools.fviz_candisc_biplot

discrimintools.fviz_candisc_biplot(obj, axis=[0, 1], geom_ind=('point', 'text'), repel=False, point_args_ind={'shape': 'o', 'size': 1.5}, text_args_ind={'size': 8}, geom_var=('arrow', 'text'), col_var='steelblue', segment_args={'alpha': 1, 'linetype': 'solid', 'size': 0.5}, text_args_var={'size': 8}, add_group=True, geom_group=('point', 'text'), point_args_group={'shape': '^', 'size': 3}, text_args_group={'size': 11.5}, palette=None, x_lim=None, y_lim=None, x_label=None, y_label=None, title=None, add_hline=True, add_vline=True, add_grid=True, ggtheme=None)

Visualize Canonical Discriminant Analysis (CANDISC) - Biplot of individuals and variables

Canonical discriminant analysis is a dimension-reduction technique related to principal component analysis and canonical correlation. fviz_candisc_biplot provides plotnine based elegant visualization of CANDISC outputs for individuals and variables.

Parameters:

• obj (class) – An object of class CANDISC.

• **kwargs – further arguments passed to or from others functions. See fviz_candisc_ind, fviz_candisc_var.

Returns:

p – A object of class ggplot.

Return type:

class

See also

fviz_candisc

Visualize Canonical Discriminant Analysis (CANDISC).

fviz_candisc_ind

Visualize Canonical Discriminant Analysis (CANDISC) - Graph of individuals.

fviz_candisc_var

Visualize Canonical Discriminant Analysis (CANDISC) - Graph of variables.

fviz_dist

Visualize distance between barycenter.

Examples

>>> from discrimintools.datasets import load_wine
>>> from discrimintools import CANDISC, fviz_candisc_biplot
>>> D = load_wine("train") # load training data
>>> y, X = D["Quality"], D.drop(columns=["Quality"]) # split into X and y
>>> clf = CANDISC()
>>> clf.fit(X,y)
CANDISC()
>>> p = fviz_candisc_biplot(clf) # biplot of individuals and variables
>>> print(p)

Fig. 4 Biplot of individuals and variables - CANDISC