"""
Created on Fri Dec 20 12:33:30 2019
Modified on Tue Jun 21 12:32:00 2022
@author: VICTOR
"""
import numpy as np
[docs]def signed_r2(class1, class2, signed=True, axis=0):
""" This function computes the basic form of the squared point biserial
correlation coefficient (r2-value).
Parameters
---------
class1: list or numpy.ndarray
Data that belongs to the first class
class1: list or numpy.ndarray
Data that belongs to the second class
signed: bool (Optional, default=True)
Controls if the sign should be mantained.
axis: int (Optional, default=0)
Dimension along which the r2-value is computed. Therefore,
if class1 and class2 has dimensions of [observations x samples]
and dim=0, the r2-value will have dimensions [1 x samples].
Returns
-------
r2: numpy.ndarray
(Signed) r2-value.
"""
# Length of each class
N1 = class1.shape[axis]
N2 = class2.shape[axis]
# Pre-computation
all_data = np.concatenate((class1,class2), axis=axis)
v = np.var(all_data, axis=axis)
m_diff = np.mean(class1, axis=axis) - np.mean(class2, axis=axis)
# Compute the sign if required
sign = 1
if signed:
sign = np.sign(m_diff)
sign[sign == 0] = 1
# Final r2 value
r2 = sign*N1*N2*np.power(m_diff, 2)/(v*np.power(N1+N2, 2))
return r2