discrimintools.fviz_dica_quali_var#

discrimintools.fviz_dica_quali_var(obj, axis=[0, 1], geom_var=('point', 'text'), repel=False, col_var='blue', point_args_var={'shape': 'o', 'size': 1.5}, text_args_var={'size': 8}, x_lim=(0, 1), y_lim=(0, 1), x_label=None, y_label=None, title=None, add_hline=True, add_vline=True, add_grid=True, ggtheme=None)[source]#

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

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

Parameters:

obj (class) – An instance of class DiCA.
axis (list, defaul = [0,1]) – Dimensions to be plotted.
geom_var (str, list or tuple, default = (‘point’,’text’)) – Geometry to be used for the graph. Possible values are the combinaison of [“point”,”text”].
- ‘point’ to show only points,
- ‘text’ to show only labels,
- (‘point’,’text’) to show both types.
repel (bool, default = False) – To avoid overplotting text labels.
col_var (str, default = ‘blue’) – Color for the qualitative variables points and texts.
point_args_var (dict, default = dict(shape = “o”, size = 1.5)) – Keywords arguments for geom_point.
text_args_var (dict, default = dict(size = 8)) – Keywords arguments for geom_text.
x_lim (None, list or tuple, default = (0,1)) – The range of the plotted x values.
y_lim (None, list or tuple, default = (0,1)) – The range of the plotted y values.
x_label (None or str, default = None) – The label text of x.
y_label (None or str, default = None) – The label text of y.
title (None or str, default = None) – The title of the graph you draw.
add_hline (bool, default = True) – To add a horizontal line.
add_vline (bool, default = True) – To add a vertical line.
add_grid (bool, default = True) – To add grid customization.
ggtheme (function, default=None) – Plotnine theme name.

Returns:

p – A object of class ggplot.

Return type:

class

See also

fviz_dica: Visualize Discriminant Correspondence Analysis (DiCA)
fviz_dica_biplot: Visualize Discriminant Correspondence Analysis (DiCA) - Biplot of individuals and variables
fviz_dica_ind: Visualize Discriminant Correspondence Analysis (DiCA) - Graph of individuals
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_quali_var
>>> D = load_divay("train") # 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_quali_var(clf) # graph of qualitative variables
>>> print(p)

../../_static/fviz_dica_quali_var.png — Fig. 14 Graph of qualitative variables - DiCA#

discrimintools.fviz_dica_quali_var#

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