discrimintools.fviz_dica

discrimintools.fviz_dica(obj, element='biplot', **kwargs)

Visualize Discriminant Correspondence Analysis (DiCA)

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

Parameters:

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

• element (str, default = ‘biplot’) – The element to plot from the output, possible values:

  • ‘ind’ for the individuals graphs

  • ‘var’ for the variables graphs

  • ‘quali_var’ for qualitative variables graphs

  • ‘biplot’ for biplot of individuals and variables

  • ‘dist’ for the distance graphs

• **kwargs – further arguments passed to or from other methods

Returns:

p – A object of class ggplot.

Return type:

class

See also

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_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
>>> D = load_divay() # load training dataset
>>> y, X = D["Region"], D.drop(columns=["Region"]) # split into X and y
>>> clf = DiCA()
>>> clf.fit(X,y)
DiCA()

Graph of individuals…

>>> p = fviz_dica(clf, "ind") # graph of individuals
>>> print(p)

Fig. 7 Graph of individuals - DiCA

Graph of variables/categories …

>>> p = fviz_dica(clf, "var") # graph of variables/categories
>>> print(p)

Fig. 8 Graph of variables/categories - DiCA

Graph of qualitative variables…

>>> p = fviz_dica(clf, "quali_var") # graph of qualitative variables
>>> print(p)

Fig. 9 Graph of qualitative variables - DiCA

Biplot of individuals and variables…

>>> p = fviz_dica(clf, "biplot") # biplot of individuals and variables
>>> print(p)

Fig. 10 Biplot of individuals and variables - DiCA

Distance between class barycenter.

>>> p = fviz_dica(clf, "dist") # distance between barycenter
>>> print(p)

Fig. 11 Distance between barycenter - DiCA