edu.cmu.tetradapp.editor.StandardizedSemImImpliedCovTable Maven / Gradle / Ivy
///////////////////////////////////////////////////////////////////////////////
// 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.tetradapp.editor;
import edu.cmu.tetrad.graph.Node;
import edu.cmu.tetrad.sem.StandardizedSemIm;
import edu.cmu.tetrad.util.Matrix;
import edu.cmu.tetrad.util.NumberFormatUtil;
import org.apache.commons.math3.util.FastMath;
import javax.swing.table.AbstractTableModel;
import java.text.NumberFormat;
import java.util.List;
/**
* Presents a covariance matrix as a table model for the SemImEditor.
*
* @author Donald Crimbchin
*/
final class StandardizedSemImImpliedCovTable extends AbstractTableModel {
/**
* The SemIm whose implied covariance matrices this model is displaying.
*/
private final StandardizedSemIm semIm;
/**
* True iff the matrices for the observed variables ony should be displayed.
*/
private final boolean measured;
/**
* True iff correlations (rather than covariances) should be displayed.
*/
private final boolean correlations;
/**
* Formats numbers so that they have 4 digits after the decimal place.
*/
private final NumberFormat nf;
/**
* The matrix being displayed. (This varies.)
*/
private final double[][] matrix;
/**
* Constructs a new table for the given covariance matrix, the nodes for which are as specified (in the order they
* appear in the matrix).
*/
public StandardizedSemImImpliedCovTable(StandardizedSemIm semIm, boolean measured,
boolean correlations) {
this.semIm = semIm;
this.measured = measured;
this.correlations = correlations;
this.nf = NumberFormatUtil.getInstance().getNumberFormat();
if (measured() && covariances()) {
this.matrix = getSemIm().getImplCovarMeas().toArray();
} else if (measured()) {
this.matrix = StandardizedSemImImpliedCovTable.corr(getSemIm().getImplCovarMeas().toArray());
} else if (covariances()) {
Matrix implCovarC = getSemIm().getImplCovar();
this.matrix = implCovarC.toArray();
} else {
Matrix implCovarC = getSemIm().getImplCovar();
this.matrix = StandardizedSemImImpliedCovTable.corr(implCovarC.toArray());
}
}
private static double[][] corr(double[][] implCovar) {
int length = implCovar.length;
double[][] corr = new double[length][length];
for (int i = 1; i < length; i++) {
for (int j = 0; j < i; j++) {
double d1 = implCovar[i][j];
double d2 = implCovar[i][i];
double d3 = implCovar[j][j];
double d4 = d1 / FastMath.pow(d2 * d3, 0.5);
if (d4 <= 1.0 || Double.isNaN(d4)) {
corr[i][j] = d4;
} else {
throw new IllegalArgumentException(
"Off-diagonal element at (" + i + ", " + j +
") cannot be converted to correlation: " +
d1 + " <= FastMath.pow(" + d2 + " * " + d3 +
", 0.5)");
}
}
}
for (int i = 0; i < length; i++) {
corr[i][i] = 1.0;
}
return corr;
}
/**
* @return the number of rows being displayed--one more than the size of the matrix, which may be different
* depending on whether only the observed variables are being displayed or all the variables are being displayed.
*/
public int getRowCount() {
if (measured()) {
return this.getSemIm().getMeasuredNodes().size() + 1;
} else {
return this.getSemIm().getVariableNodes().size() + 1;
}
}
/**
* @return the number of columns displayed--one more than the size of the matrix, which may be different depending
* on whether only the observed variables are being displayed or all the variables are being displayed.
*/
public int getColumnCount() {
if (measured()) {
return this.getSemIm().getMeasuredNodes().size() + 1;
} else {
return this.getSemIm().getVariableNodes().size() + 1;
}
}
/**
* @return the name of the column at columnIndex, which is "" for column 0 and the name of the variable for the
* other columns.
*/
public String getColumnName(int columnIndex) {
if (columnIndex == 0) {
return "";
} else {
if (measured()) {
List nodes = getSemIm().getMeasuredNodes();
Node node = ((Node) nodes.get(columnIndex - 1));
return node.getName();
} else {
List nodes = getSemIm().getVariableNodes();
Node node = ((Node) nodes.get(columnIndex - 1));
return node.getName();
}
}
}
/**
* @return the value being displayed in a cell, either a variable name or a Double.
*/
public Object getValueAt(int rowIndex, int columnIndex) {
if (rowIndex == 0) {
return getColumnName(columnIndex);
}
if (columnIndex == 0) {
return getColumnName(rowIndex);
} else if (rowIndex < columnIndex) {
return null;
} else {
return this.nf.format(this.matrix[rowIndex - 1][columnIndex - 1]);
}
}
private boolean covariances() {
return !correlations();
}
/**
* @return true iff only observed variables are displayed.
*/
private boolean measured() {
return this.measured;
}
/**
* @return true iff correlations (rather than covariances) are displayed.
*/
private boolean correlations() {
return this.correlations;
}
private StandardizedSemIm getSemIm() {
return this.semIm;
}
}
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