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

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, [email protected]
Testing: Valentina Borghesani, [email protected]
"""

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

@jwcarr -- I think you might need to add a column of 1s to include an intercept in the model, via np.column_stack([data, np.ones(data.shape[0])]) or similar. I'm not familiar with Matlab or R, but given your example, I would guess that R includes an intercept by default, and Matlab does not.

Sometimes R and Matlab libraries standardize your data by default. For this problem, adding the intercept column and standardizing the data have the same effect (due to removing the mean out of the data).

Regular:
>partial_corr(data) array([[ 1. , 0.25363885, 0.67638027], [ 0.25363885, 1. , 0.17226437], [ 0.67638027, 0.17226437, 1. ]])

Intercept:
data_int = np.hstack((np.ones((data.shape[0],1)), data)) partial_corr(data_int)[1:, 1:] array([[ 1. , -0.54341003, -0.14076948], [-0.54341003, 1. , -0.76207595], [-0.14076948, -0.76207595, 1. ]])

Standardized:
data_std = data.copy() data_std -= data.mean(axis=0)[np.newaxis, :] data_std /= data.std(axis=0)[np.newaxis, :] partial_corr(data_std) array([[ 1. , -0.54341003, -0.14076948], [-0.54341003, 1. , -0.76207595], [-0.14076948, -0.76207595, 1. ]])

maybe they should add this to the scipy library

there is a mistake in line 61 and 62, the subscripts of the betas are incorrect

@rubenSaro I guess it is correct since it is consistent with line 58 and 59. Although the subscripts are actually confusion.

Why not just -np.linalg.inv(np.corrcoef(C.T)) ? except if you really need the linear regression method.

In [24]: C = np.random.normal(0,1,(1000,5))

In [25]: partial_corr(C)
Out[25]:
array([[ 1.        ,  0.04377477,  0.05926928, -0.0048639 , -0.00949965],
       [ 0.04377477,  1.        , -0.02458582, -0.00286263,  0.00101031],
       [ 0.05926928, -0.02458582,  1.        ,  0.00670762, -0.04408118],
       [-0.0048639 , -0.00286263,  0.00670762,  1.        ,  0.02981604],
       [-0.00949965,  0.00101031, -0.04408118,  0.02981604,  1.        ]])

In [26]: -np.linalg.inv(np.corrcoef(C.T))
Out[26]:
array([[-1.00550451,  0.04393255,  0.05962884, -0.00486145, -0.009527  ],
       [ 0.04393255, -1.0024175 , -0.02466733, -0.00284463,  0.00102981],
       [ 0.05962884, -0.02466733, -1.0060574 ,  0.0067103 , -0.04429945],
       [-0.00486145, -0.00284463,  0.0067103 , -1.00095072,  0.0298542 ],
       [-0.009527  ,  0.00102981, -0.04429945,  0.0298542 , -1.00297644]])

Why not just -np.linalg.inv(np.corrcoef(C.T)) ? except if you really need the linear regression method.

In [24]: C = np.random.normal(0,1,(1000,5))

In [25]: partial_corr(C)
Out[25]:
array([[ 1.        ,  0.04377477,  0.05926928, -0.0048639 , -0.00949965],
       [ 0.04377477,  1.        , -0.02458582, -0.00286263,  0.00101031],
       [ 0.05926928, -0.02458582,  1.        ,  0.00670762, -0.04408118],
       [-0.0048639 , -0.00286263,  0.00670762,  1.        ,  0.02981604],
       [-0.00949965,  0.00101031, -0.04408118,  0.02981604,  1.        ]])

In [26]: -np.linalg.inv(np.corrcoef(C.T))
Out[26]:
array([[-1.00550451,  0.04393255,  0.05962884, -0.00486145, -0.009527  ],
       [ 0.04393255, -1.0024175 , -0.02466733, -0.00284463,  0.00102981],
       [ 0.05962884, -0.02466733, -1.0060574 ,  0.0067103 , -0.04429945],
       [-0.00486145, -0.00284463,  0.0067103 , -1.00095072,  0.0298542 ],
       [-0.009527  ,  0.00102981, -0.04429945,  0.0298542 , -1.00297644]])

In your opinion what do partial coefficients bigger than 1 in absolute signify?

If you do this, it works:

def pcor(data):
    X = -np.linalg.inv(np.cov(data.T))
    stdev = np.sqrt(np.abs(np.diag(X)))
    X /= stdev[:, None]
    X /= stdev[None, :]
    np.fill_diagonal(X, 1.0)
    return X

For the original data this gives:

[[ 1.         -0.54341003 -0.14076948]
 [-0.54341003  1.         -0.76207595]
 [-0.14076948 -0.76207595  1.        ]]

C = np.asarray(C)
p = C.shape[1]
P_corr = np.zeros((p, p), dtype=np.float) # sample linear partial correlation coefficients

corr = np.corrcoef(C,rowvar=False) # Pearson product-moment correlation coefficients.
corr_inv = inv(corr) # the (multiplicative) inverse of a matrix.

for i in range(p):
    P_corr[i, i] = 1
    for j in range(i+1, p):
        pcorr_ij = -corr_inv[i,j]/(np.sqrt(corr_inv[i,i]*corr_inv[j,j]))
        # 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
        P_corr[i,j]=pcorr_ij
        P_corr[j,i]=pcorr_ij
    
return P_corr

by this you can get the result same as in R 'pcor'