Examples with GeneralAndersonDarlingTest edu.cmu.tetrad.data.GeneralAndersonDarlingTest used on opensource projects

Example 1 with GeneralAndersonDarlingTest

use of edu.cmu.tetrad.data.GeneralAndersonDarlingTest in project tetrad by cmu-phil.

the class GeneralizedSemEstimator method estimate.

/**
 * Maximizes likelihood equation by equation. Assumes the equations are recursive and that
 * each has exactly one error term.
 */
public GeneralizedSemIm estimate(GeneralizedSemPm pm, DataSet data) {
    StringBuilder builder = new StringBuilder();
    GeneralizedSemIm estIm = new GeneralizedSemIm(pm);
    List<Node> nodes = pm.getGraph().getNodes();
    nodes.removeAll(pm.getErrorNodes());
    MyContext context = new MyContext();
    List<List<Double>> allResiduals = new ArrayList<>();
    List<RealDistribution> allDistributions = new ArrayList<>();
    for (int index = 0; index < nodes.size(); index++) {
        Node node = nodes.get(index);
        List<String> parameters = new ArrayList<>(pm.getReferencedParameters(node));
        Node error = pm.getErrorNode(node);
        parameters.addAll(pm.getReferencedParameters(error));
        LikelihoodFittingFunction2 likelihoodFittingfunction = new LikelihoodFittingFunction2(index, pm, parameters, nodes, data, context);
        double[] values = new double[parameters.size()];
        for (int j = 0; j < parameters.size(); j++) {
            String parameter = parameters.get(j);
            Expression parameterEstimationInitializationExpression = pm.getParameterEstimationInitializationExpression(parameter);
            values[j] = parameterEstimationInitializationExpression.evaluate(new MyContext());
        }
        double[] point = optimize(likelihoodFittingfunction, values, 1);
        for (int j = 0; j < parameters.size(); j++) {
            estIm.setParameterValue(parameters.get(j), point[j]);
        }
        List<Double> residuals = likelihoodFittingfunction.getResiduals();
        allResiduals.add(residuals);
        RealDistribution distribution = likelihoodFittingfunction.getDistribution();
        allDistributions.add(distribution);
        GeneralAndersonDarlingTest test = new GeneralAndersonDarlingTest(residuals, distribution);
        builder.append("\nEquation: ").append(node).append(" := ").append(estIm.getNodeSubstitutedString(node));
        builder.append("\n\twhere ").append(pm.getErrorNode(node)).append(" ~ ").append(estIm.getNodeSubstitutedString(pm.getErrorNode(node)));
        builder.append("\nAnderson Darling A^2* for this equation =  ").append(test.getASquaredStar()).append("\n");
    }
    List<String> parameters = new ArrayList<>();
    double[] values = new double[parameters.size()];
    for (int i = 0; i < parameters.size(); i++) {
        values[i] = estIm.getParameterValue(parameters.get(i));
    }
    LikelihoodFittingFunction likelihoodFittingFunction = new LikelihoodFittingFunction(pm, parameters, nodes, data, context);
    optimize(likelihoodFittingFunction, values, 1);
    MultiGeneralAndersonDarlingTest test = new MultiGeneralAndersonDarlingTest(allResiduals, allDistributions);
    double aSquaredStar = test.getASquaredStar();
    this.aSquaredStar = aSquaredStar;
    String builder2 = "Report:\n" + "\nModel A^2* (Anderson Darling) = " + aSquaredStar + "\n" + builder;
    this.report = builder2;
    return estIm;
}

Example 2 with GeneralAndersonDarlingTest

use of edu.cmu.tetrad.data.GeneralAndersonDarlingTest 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;
}