Examples with RegressionResult edu.cmu.tetrad.regression.RegressionResult used on opensource projects

Example 31 with RegressionResult

use of edu.cmu.tetrad.regression.RegressionResult in project tetrad by cmu-phil.

the class IndTestMultinomialLogisticRegression method isIndependentRegression.

private boolean isIndependentRegression(Node x, Node y, List<Node> z) {
    if (!variablesPerNode.containsKey(x)) {
        throw new IllegalArgumentException("Unrecogized node: " + x);
    }
    if (!variablesPerNode.containsKey(y)) {
        throw new IllegalArgumentException("Unrecogized node: " + y);
    }
    for (Node node : z) {
        if (!variablesPerNode.containsKey(x)) {
            throw new IllegalArgumentException("Unrecogized node: " + node);
        }
    }
    List<Node> regressors = new ArrayList<>();
    regressors.add(internalData.getVariable(y.getName()));
    for (Node _z : z) {
        regressors.addAll(variablesPerNode.get(_z));
    }
    int[] _rows = getNonMissingRows(x, y, z);
    regression.setRows(_rows);
    RegressionResult result;
    try {
        result = regression.regress(x, regressors);
    } catch (Exception e) {
        return false;
    }
    double p = result.getP()[1];
    this.lastP = p;
    boolean indep = p > alpha;
    if (verbose) {
        if (indep) {
            TetradLogger.getInstance().log("independencies", SearchLogUtils.independenceFactMsg(x, y, z, p));
        } else {
            TetradLogger.getInstance().log("dependencies", SearchLogUtils.dependenceFactMsg(x, y, z, p));
        }
    }
    return indep;
}

Example 32 with RegressionResult

use of edu.cmu.tetrad.regression.RegressionResult in project tetrad by cmu-phil.

the class IndTestRegressionAD method isIndependent.

/**
 * Determines whether variable x is independent of variable y given a list of conditioning variables z.
 *
 * @param xVar  the one variable being compared.
 * @param yVar  the second variable being compared.
 * @param zList the list of conditioning variables.
 * @return true iff x _||_ y | z.
 * @throws RuntimeException if a matrix singularity is encountered.
 */
public boolean isIndependent(Node xVar, Node yVar, List<Node> zList) {
    if (zList == null) {
        throw new NullPointerException();
    }
    for (Node node : zList) {
        if (node == null) {
            throw new NullPointerException();
        }
    }
    TetradVector v1, v2;
    try {
        List<Node> regressors = new ArrayList<>();
        regressors.add(dataSet.getVariable(yVar.getName()));
        for (Node zVar : zList) {
            regressors.add(dataSet.getVariable(zVar.getName()));
        }
        RegressionDataset regression = new RegressionDataset(dataSet);
        RegressionResult result = regression.regress(xVar, regressors);
        v1 = result.getResiduals();
        v2 = regression.getResidualsWithoutFirstRegressor();
    // regressors.remove(dataSet.getVariable(yVar.getNode()));
    // regression = new RegressionDataset(dataSet);
    // result = regression.regress(xVar, regressors);
    // v2 = result.getResiduals();
    } catch (Exception e) {
        throw e;
    }
    List<Double> d1 = new ArrayList<>();
    for (int i = 0; i < v1.size(); i++) d1.add(v1.get(i));
    List<Double> d2 = new ArrayList<>();
    double[] f2 = new double[v2.size()];
    for (int i = 0; i < v2.size(); i++) {
        d2.add(v2.get(i));
        f2[i] = v2.get(i);
    }
    double sd = StatUtils.sd(f2);
    // RealDistribution c2 = new EmpiricalCdf(d2);
    RealDistribution c2 = new NormalDistribution(0, sd);
    GeneralAndersonDarlingTest test = new GeneralAndersonDarlingTest(d1, c2);
    double aSquaredStar = test.getASquaredStar();
    System.out.println("A squared star = " + aSquaredStar + " p = " + test.getP());
    double p = test.getP();
    double aa2 = 1 - tanh(aSquaredStar);
    boolean independent = p > alpha;
    this.pvalue = aa2;
    if (verbose) {
        if (independent) {
            TetradLogger.getInstance().log("independencies", SearchLogUtils.independenceFactMsg(xVar, yVar, zList, 0.));
        } else {
            TetradLogger.getInstance().log("dependencies", SearchLogUtils.dependenceFactMsg(xVar, yVar, zList, 0.));
        }
    }
    return independent;
}

Example 33 with RegressionResult

use of edu.cmu.tetrad.regression.RegressionResult in project tetrad by cmu-phil.

the class IndTestLaggedRegression method isIndependent.

/**
 * Determines whether variable x is independent of variable y given a list of conditioning variables z.
 *
 * @param xVar  the one variable being compared.
 * @param yVar  the second variable being compared.
 * @param zList the list of conditioning variables.
 * @return true iff x _||_ y | z.
 * @throws RuntimeException if a matrix singularity is encountered.
 */
public boolean isIndependent(Node xVar, Node yVar, List<Node> zList) {
    if (zList == null) {
        throw new NullPointerException();
    }
    for (Node node : zList) {
        if (node == null) {
            throw new NullPointerException();
        }
    }
    List<Node> regressors = new ArrayList<>();
    // regressors.add(dataSet.getVariable(yVar.getNode()));
    // 
    // for (Node zVar : zList) {
    // regressors.add(dataSet.getVariable(zVar.getNode()));
    // }
    // TetradMatrix residuals = new TetradMatrix(timeLags.getNumRows(), timeSeries.getNumColumns());
    xVar = timeLags.getVariable(xVar.getName());
    regressors.add(timeLags.getVariable(yVar.getName()));
    int yIndex = timeLags.getColumn(timeLags.getVariable(yVar.getName()));
    for (int i2 = yIndex + variables.size(); i2 < timeLags.getNumColumns(); i2 += variables.size()) {
        regressors.add(timeLags.getVariable(i2));
    }
    int xIndex = timeLags.getColumn(timeLags.getVariable(xVar.getName()));
    for (int i2 = xIndex + variables.size(); i2 < timeLags.getNumColumns(); i2 += variables.size()) {
        regressors.add(timeLags.getVariable(i2));
    }
    for (Node z : zList) {
        int zIndex = timeLags.getColumn(timeLags.getVariable(z.getName()));
        for (int i2 = zIndex; i2 < timeLags.getNumColumns(); i2 += variables.size()) {
            final List<Node> variables = timeLags.getVariables();
            regressors.add(variables.get(i2));
        }
    }
    System.out.println(xVar + " conditioning on " + regressors);
    // Regression regression = new RegressionDataset(dataSet);
    RegressionResult result = null;
    try {
        result = regression.regress(xVar, regressors);
    } catch (Exception e) {
        e.printStackTrace();
        return false;
    }
    double p = result.getP()[1];
    System.out.println("p = " + p);
    boolean independent = p > alpha;
    if (verbose) {
        if (independent) {
            System.out.println(SearchLogUtils.independenceFactMsg(xVar, yVar, zList, p));
        }
    }
    return independent;
}