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// For information as to what this class does, see the Javadoc, below. //
// Copyright (C) 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, //
// 2007, 2008, 2009, 2010, 2014, 2015, 2022 by Peter Spirtes, Richard //
// Scheines, Joseph Ramsey, and Clark Glymour. //
// //
// This program is free software; you can redistribute it and/or modify //
// it under the terms of the GNU General Public License as published by //
// the Free Software Foundation; either version 2 of the License, or //
// (at your option) any later version. //
// //
// This program is distributed in the hope that it will be useful, //
// but WITHOUT ANY WARRANTY; without even the implied warranty of //
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the //
// GNU General Public License for more details. //
// //
// You should have received a copy of the GNU General Public License //
// along with this program; if not, write to the Free Software //
// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA //
///////////////////////////////////////////////////////////////////////////////
package edu.cmu.tetrad.search.utils;
import edu.cmu.tetrad.data.CorrelationMatrix;
import edu.cmu.tetrad.data.CovarianceMatrix;
import edu.cmu.tetrad.data.ICovarianceMatrix;
import edu.cmu.tetrad.graph.Node;
import edu.cmu.tetrad.util.Matrix;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import static org.apache.commons.math3.util.FastMath.sqrt;
/**
* Calculates partial correlation using the recursive method.
*
* @author josephramsey
*/
public class PartialCorrelation {
private final ICovarianceMatrix corr;
private final Map nodesMap = new HashMap<>();
/**
* Constructor.
*
* @param nodes The lsit of nodes.
* @param cov The covariance matrix, as a Matrix.
* @param sampleSize The sample size.
*/
public PartialCorrelation(List nodes, Matrix cov, int sampleSize) {
this.corr = new CorrelationMatrix(new CovarianceMatrix(nodes, cov, sampleSize));
for (int i = 0; i < nodes.size(); i++) this.nodesMap.put(nodes.get(i), i);
}
/**
* Constructor
*
* @param cov The covariance matrix, as an ICovariance object.
*/
public PartialCorrelation(ICovarianceMatrix cov) {
this.corr = new CorrelationMatrix(cov);
List nodes = this.corr.getVariables();
for (int i = 0; i < nodes.size(); i++) this.nodesMap.put(nodes.get(i), i);
}
/**
* Calculates the partial correlation of x and y conditional on the nodes in z recursively.
*
* @return this partial correlation.
*/
public double corr(Node x, Node y, List z) {
if (z.isEmpty()) return this.corr.getValue(this.nodesMap.get(x), this.nodesMap.get(y));
Node z0 = z.get(0);
List _z = new ArrayList<>(z);
_z.remove(z0);
double corr0 = corr(x, y, _z);
double corr1 = corr(x, z0, _z);
double corr2 = corr(z0, y, _z);
return (corr0 - corr1 * corr2) / sqrt(1. - corr1 * corr1) * sqrt(1. - corr2 * corr2);
}
}
