discrimintools.fviz_dica_biplot

discrimintools.fviz_dica_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=('point', 'text'), col_var='blue', point_args_var={'shape': 'o', 'size': 1.5}, 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 Discriminant Correspondence Analysis (DiCA) - Biplot of individuals and variables

Discriminant correspondence analysis (DiCA) is a canonical discriminant analysis on qualitative predictors. fviz_dica_biplot provides plotnine based elegant visualization of DiCA outputs for individuals and variables.

Parameters:

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

• **kwargs – further arguments passed to or from other methods. See fviz_dica_ind, fviz_dica_var.

Returns:

p – A object of class ggplot.

Return type:

class

See also

fviz_dica

Visualize Discriminant Correspondence Analysis (DiCA)

fviz_dica_ind

Visualize Discriminant Correspondence Analysis (DiCA) - Graph of individuals

fviz_dica_quali_var

Visualize Discriminant Correspondence Analysis (DiCA) - Graph of qualitative variables

fviz_dica_var

Visualize Discriminant Correspondence Analysis (DiCA) - graph of variables/categories

fviz_dist

Visualize distance between barycenter.

Examples

>>> from discrimintools.datasets import load_divay
>>> from discrimintools import DiCA, fviz_dica_biplot
>>> D = load_divay() # load training data
>>> y, X = D["Region"], D.drop(columns=["Region"]) # split into X and y
>>> clf = DiCA()
>>> clf.fit(X,y)
DiCA()
>>> p = fviz_dica_biplot(clf) # biplot of individuals and variables
>>> print(p)

Fig. 12 Biplot of individuals and variables