partial_corr.py

"""
Partial Correlation in Python (clone of Matlab's partialcorr)

This uses the linear regression approach to compute the partial 
correlation (might be slow for a huge number of variables). The 
algorithm is detailed here:

    http://en.wikipedia.org/wiki/Partial_correlation#Using_linear_regression

Taking X and Y two variables of interest and Z the matrix with all the variable minus {X, Y},
the algorithm can be summarized as

    1) perform a normal linear least-squares regression with X as the target and Z as the predictor
    2) calculate the residuals in Step #1
    3) perform a normal linear least-squares regression with Y as the target and Z as the predictor
    4) calculate the residuals in Step #3
    5) calculate the correlation coefficient between the residuals from Steps #2 and #4; 

    The result is the partial correlation between X and Y while controlling for the effect of Z


Date: Nov 2014
Author: Fabian Pedregosa-Izquierdo, f@bianp.net
Testing: Valentina Borghesani, valentinaborghesani@gmail.com
"""

import numpy as np
from scipy import stats, linalg

def partial_corr(C):
    """
    Returns the sample linear partial correlation coefficients between pairs of variables in C, controlling 
    for the remaining variables in C.


    Parameters
    ----------
    C : array-like, shape (n, p)
        Array with the different variables. Each column of C is taken as a variable


    Returns
    -------
    P : array-like, shape (p, p)
        P[i, j] contains the partial correlation of C[:, i] and C[:, j] controlling
        for the remaining variables in C.
    """
    
    C = np.asarray(C)
    p = C.shape[1]
    P_corr = np.zeros((p, p), dtype=np.float)
    for i in range(p):
        P_corr[i, i] = 1
        for j in range(i+1, p):
            idx = np.ones(p, dtype=np.bool)
            idx[i] = False
            idx[j] = False
            beta_i = linalg.lstsq(C[:, idx], C[:, j])[0]
            beta_j = linalg.lstsq(C[:, idx], C[:, i])[0]

            res_j = C[:, j] - C[:, idx].dot( beta_i)
            res_i = C[:, i] - C[:, idx].dot(beta_j)
            
            corr = stats.pearsonr(res_i, res_j)[0]
            P_corr[i, j] = corr
            P_corr[j, i] = corr
        
    return P_corr