Examples with Node edu.cmu.tetrad.graph.Node used on opensource projects

Example 66 with Node

use of edu.cmu.tetrad.graph.Node in project tetrad by cmu-phil.

the class IndTestChiSquare method determines.

/**
 * @param z  The list of variables z1,...,zn with respect to which we want to know whether z determines x oir z.
 * @param x1 The one variable whose determination by z we want to know.
 * @return true if it is estimated that z determines x or z determines y.
 */
public boolean determines(List<Node> z, Node x1) {
    if (z == null) {
        throw new NullPointerException();
    }
    for (Node aZ : z) {
        if (aZ == null) {
            throw new NullPointerException();
        }
    }
    // For testing x, y given z1,...,zn, set up an array of length
    // n + 2 containing the indices of these variables in order.
    int[] testIndices = new int[1 + z.size()];
    testIndices[0] = variables.indexOf(x1);
    for (int i = 0; i < z.size(); i++) {
        testIndices[i + 1] = variables.indexOf(z.get(i));
    }
    // the following is lame code--need a better test
    for (int i = 0; i < testIndices.length; i++) {
        if (testIndices[i] < 0) {
            throw new IllegalArgumentException("Variable " + i + "was not used in the constructor.");
        }
    }
    // System.out.println("Testing " + x + " _||_ " + y + " | " + z);
    boolean countDetermined = chiSquareTest.isDetermined(testIndices, getDeterminationP());
    if (countDetermined) {
        StringBuilder sb = new StringBuilder();
        sb.append("Determination found: ").append(x1).append(" is determined by {");
        for (int i = 0; i < z.size(); i++) {
            sb.append(z.get(i));
            if (i < z.size() - 1) {
                sb.append(", ");
            }
        }
        sb.append("}");
        TetradLogger.getInstance().log("independencies", sb.toString());
    }
    return countDetermined;
}

Example 67 with Node

use of edu.cmu.tetrad.graph.Node in project tetrad by cmu-phil.

the class IndTestChiSquare method getVariableNames.

/**
 * @return the list of variable varNames.
 */
public List<String> getVariableNames() {
    List<Node> variables = getVariables();
    List<String> variableNames = new ArrayList<>();
    for (Node variable1 : variables) {
        variableNames.add(variable1.getName());
    }
    return variableNames;
}

Example 68 with Node

use of edu.cmu.tetrad.graph.Node in project tetrad by cmu-phil.

the class IndTestConditionalGaussianLRT method getVariableNames.

/**
 * @return the list of variable varNames.
 */
public List<String> getVariableNames() {
    List<Node> variables = getVariables();
    List<String> variableNames = new ArrayList<>();
    for (Node variable1 : variables) {
        variableNames.add(variable1.getName());
    }
    return variableNames;
}

Example 69 with Node

use of edu.cmu.tetrad.graph.Node in project tetrad by cmu-phil.

the class IndTestCramerT method indTestSubset.

// ==========================PUBLIC METHODS=============================//
/**
 * Creates a new IndTestCramerT instance for a subset of the variables.
 */
public IndependenceTest indTestSubset(List<Node> vars) {
    if (vars.isEmpty()) {
        throw new IllegalArgumentException("Subset may not be empty.");
    }
    for (Node var : vars) {
        if (!variables.contains(var)) {
            throw new IllegalArgumentException("All vars must be original vars");
        }
    }
    int[] indices = new int[vars.size()];
    for (int i = 0; i < indices.length; i++) {
        indices[i] = variables.indexOf(vars.get(i));
    }
    ICovarianceMatrix newCorrMatrix = covMatrix.getSubmatrix(indices);
    double alphaNew = getAlpha();
    return new IndTestCramerT(newCorrMatrix, alphaNew);
}

Example 70 with Node

use of edu.cmu.tetrad.graph.Node in project tetrad by cmu-phil.

the class IndTestCramerT method isIndependent.

/**
 * Determines whether variable x is independent of variable y given a list of conditioning variables z.
 *
 * @param x the one variable being compared.
 * @param y the second variable being compared.
 * @param z the list of conditioning variables.
 * @return true iff x _||_ y | z.
 * @throws RuntimeException if a matrix singularity is encountered.
 */
public boolean isIndependent(Node x, Node y, List<Node> z) {
    if (z == null) {
        throw new NullPointerException();
    }
    for (Node node : z) {
        if (node == null) {
            throw new NullPointerException();
        }
    }
    // Precondition: this.variables, this.corrMatrix properly set up.
    // 
    // Postcondition: this.storedR and this.func should be the
    // most recently calculated partial correlation and
    // partial correlation distribution function, respectively.
    // 
    // PROCEDURE:
    // calculate the partial correlation of x and y given z
    // by finding the submatrix of variables x, y, z1...zn,
    // inverting it, and examining the value at position (0, 1)
    // in the inverted matrix.  the partial correlation is
    // -1 * this value / the square root of the outerProduct of the
    // diagonal elements on the same row and column as this
    // value.
    // 
    // Design consideration:
    // Minimize object creation by reusing submatrix and condition
    // arrays and inverting submatrix in place.
    // Create index array for the given variables.
    int size = z.size() + 2;
    int[] indices = new int[size];
    indices[0] = getVariables().indexOf(x);
    indices[1] = getVariables().indexOf(y);
    for (int i = 0; i < z.size(); i++) {
        indices[i + 2] = getVariables().indexOf(z.get(i));
    }
    // Extract submatrix of correlation matrix using this index array.
    TetradMatrix submatrix = covMatrix().getMatrix().getSelection(indices, indices);
    // Check for missing values.
    if (DataUtils.containsMissingValue(submatrix)) {
        throw new IllegalArgumentException("Please remove or impute missing values first.");
    }
    // Invert submatrix.
    if (submatrix.rank() != submatrix.rows()) {
        // if (TetradAlgebra.rank(submatrix) != submatrix.rows()) {
        throw new IllegalArgumentException("Matrix singularity detected while using correlations " + "\nto check for independence; probably due to collinearity " + "\nin the data. The independence fact being checked was " + "\n" + x + " _||_ " + y + " | " + z + ".");
    }
    submatrix = submatrix.inverse();
    double a = -1.0 * submatrix.get(0, 1);
    double b = Math.sqrt(submatrix.get(0, 0) * submatrix.get(1, 1));
    // Store R so P value can be calculated.
    this.storedR = a / b;
    if (Math.abs(storedR) > 1) {
        storedR = Math.signum(storedR);
    }
    if (Double.isNaN(this.storedR)) {
        throw new IllegalArgumentException("Conditional correlation cannot be computed: " + SearchLogUtils.independenceFact(x, y, z));
    }
    // Determine whether this partial correlation is statistically
    // nondifferent from zero.
    boolean independent = isZero(this.storedR, size, getAlpha());
    double pValue = getPValue();
    if (verbose) {
        if (independent) {
            TetradLogger.getInstance().log("independencies", SearchLogUtils.independenceFactMsg(x, y, z, pValue));
        } else {
            TetradLogger.getInstance().log("dependencies", SearchLogUtils.dependenceFactMsg(x, y, z, pValue));
        }
    }
    return independent;
}