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

Example 1 with CovarianceMatrix

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

the class TestDeltaTetradTest method getBollenSimulationExampleData.

private CovarianceMatrix getBollenSimulationExampleData() {
    double[][] d = new double[][] { { 2.034 }, { 0.113, 1.281 }, { 0.510, 0.093, 1.572 }, { 0.105, 0.857, 0.447, 1.708 }, { 0.998, 0.228, 0.170, 0.345, 1.651 } };
    Node y1 = new ContinuousVariable("y1");
    Node y2 = new ContinuousVariable("y2");
    Node y3 = new ContinuousVariable("y3");
    Node y4 = new ContinuousVariable("y4");
    Node y5 = new ContinuousVariable("y5");
    List<Node> nodes = new ArrayList<>();
    nodes.add(y1);
    nodes.add(y2);
    nodes.add(y3);
    nodes.add(y4);
    nodes.add(y5);
    TetradMatrix matrix = new TetradMatrix(5, 5);
    for (int i = 0; i < 5; i++) {
        for (int j = 0; j <= i; j++) {
            matrix.set(i, j, d[i][j]);
            matrix.set(j, i, d[i][j]);
        }
    }
    return new CovarianceMatrix(nodes, matrix, 1000);
}

Example 2 with CovarianceMatrix

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

the class SemOptimizerRicf method optimize.

// ==============================PUBLIC METHODS========================//
/**
 * Optimizes the fitting function of the given Sem using the Powell method
 * from Numerical Recipes by adjusting the freeParameters of the Sem.
 */
public void optimize(SemIm semIm) {
    if (numRestarts < 1)
        numRestarts = 1;
    if (numRestarts != 1) {
        throw new IllegalArgumentException("Number of restarts must be 1 for this method.");
    }
    TetradMatrix sampleCovar = semIm.getSampleCovar();
    if (sampleCovar == null) {
        throw new NullPointerException("Sample covar has not been set.");
    }
    if (DataUtils.containsMissingValue(sampleCovar)) {
        throw new IllegalArgumentException("Please remove or impute missing values.");
    }
    if (DataUtils.containsMissingValue(sampleCovar)) {
        throw new IllegalArgumentException("Please remove or impute missing values.");
    }
    TetradLogger.getInstance().log("info", "Trying EM...");
    // new SemOptimizerEm().optimize(semIm);
    CovarianceMatrix cov = new CovarianceMatrix(semIm.getMeasuredNodes(), sampleCovar, semIm.getSampleSize());
    SemGraph graph = semIm.getSemPm().getGraph();
    Ricf.RicfResult result = new Ricf().ricf(graph, cov, 0.001);
    // Ricf.RicfResult result = null;
    // 
    // for (int t = 0; t < 10; t++) {
    // Graph graph = semIm.getSemPm().getGraph();
    // result = new Ricf().ricf(graph, cov, 0.001);
    // 
    // TetradMatrix bHat = result.getBhat();
    // TetradMatrix lHat = result.getLhat();
    // TetradMatrix oHat = result.getOhat();
    // TetradMatrix sHat = result.getShat();
    // 
    // for (Parameter param : semIm.getFreeParameters()) {
    // if (param.getType() == ParamType.COEF) {
    // int i = semIm.getSemPm().getVariableNodes().indexOf(param.getNodeA());
    // int j = semIm.getSemPm().getVariableNodes().indexOf(param.getNodeB());
    // semIm.setEdgeCoef(param.getNodeA(), param.getNodeB(), -bHat.get(j, i));
    // }
    // 
    // if (param.getType() == ParamType.VAR) {
    // int i = semIm.getSemPm().getVariableNodes().indexOf(param.getNodeA());
    // if (lHat.get(i, i) != 0) {
    // semIm.setErrVar(param.getNodeA(), lHat.get(i, i));
    // } else if (oHat.get(i, i) != 0) {
    // semIm.setErrVar(param.getNodeA(), oHat.get(i, i));
    // }
    // }
    // }
    // 
    // if (t < 9) {
    // for (Parameter param : semIm.getFreeParameters()) {
    // double value = semIm.getParamValue(param);
    // double max = Double.NEGATIVE_INFINITY;
    // double d;
    // 
    // for (d = value - .5; d <= value + 0.5; d += 0.001) {
    // semIm.setParamValue(param, d);
    // double fml = semIm.getFml();
    // if (fml > max) max = fml;
    // }
    // 
    // semIm.setParamValue(param, d);
    // }
    // }
    // }
    TetradMatrix bHat = new TetradMatrix(result.getBhat().toArray());
    TetradMatrix lHat = new TetradMatrix(result.getLhat().toArray());
    TetradMatrix oHat = new TetradMatrix(result.getOhat().toArray());
    for (Parameter param : semIm.getFreeParameters()) {
        if (param.getType() == ParamType.COEF) {
            int i = semIm.getSemPm().getVariableNodes().indexOf(param.getNodeA());
            int j = semIm.getSemPm().getVariableNodes().indexOf(param.getNodeB());
            semIm.setEdgeCoef(param.getNodeA(), param.getNodeB(), -bHat.get(j, i));
        }
        if (param.getType() == ParamType.VAR) {
            int i = semIm.getSemPm().getVariableNodes().indexOf(param.getNodeA());
            if (lHat.get(i, i) != 0) {
                semIm.setErrVar(param.getNodeA(), lHat.get(i, i));
            } else if (oHat.get(i, i) != 0) {
                semIm.setErrVar(param.getNodeA(), oHat.get(i, i));
            }
        }
        if (param.getType() == ParamType.COVAR) {
            int i = semIm.getSemPm().getVariableNodes().indexOf(param.getNodeA());
            int j = semIm.getSemPm().getVariableNodes().indexOf(param.getNodeB());
            if (lHat.get(i, i) != 0) {
                semIm.setErrCovar(param.getNodeA(), param.getNodeB(), lHat.get(j, i));
            } else if (oHat.get(i, i) != 0) {
                semIm.setErrCovar(param.getNodeA(), param.getNodeB(), oHat.get(j, i));
            }
        }
    }
    System.out.println(result);
    System.out.println(semIm);
}

Example 3 with CovarianceMatrix

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

the class TestDeltaTetradTest method testBollenExample1.

// Bollen and Ting, Confirmatory Tetrad Analysis, p. 164 Sympathy and Anger.
@Test
public void testBollenExample1() {
    CovarianceMatrix cov = getBollenExample1Data();
    List<Node> variables = cov.getVariables();
    Node v1 = variables.get(0);
    Node v2 = variables.get(1);
    Node v3 = variables.get(2);
    Node v4 = variables.get(3);
    Node v5 = variables.get(4);
    Node v6 = variables.get(5);
    Tetrad t1 = new Tetrad(v1, v2, v3, v4);
    Tetrad t2 = new Tetrad(v1, v2, v3, v5);
    Tetrad t3 = new Tetrad(v1, v2, v3, v6);
    Tetrad t4 = new Tetrad(v1, v4, v5, v6);
    Tetrad t5 = new Tetrad(v1, v4, v2, v3);
    Tetrad t6 = new Tetrad(v1, v5, v2, v3);
    Tetrad t7 = new Tetrad(v1, v6, v2, v3);
    Tetrad t8 = new Tetrad(v1, v6, v4, v5);
    DeltaTetradTest test = new DeltaTetradTest(cov);
    // DeltaTetradTest test = new DeltaTetradTest(new CorrelationMatrix(cov));
    double chiSq = test.calcChiSquare(t1, t2, t3, t4, t5, t6, t7, t8);
    double pValue = test.getPValue();
    // They get chi square = 6.71 p = .57 8 df but using the raw data which they don't provide here.
    // Just using the covariance matrix provided, I get chi square = 8.46, p = 0.39, df = 8.
    assertEquals(11.42, chiSq, 0.01);
    assertEquals(0.18, pValue, 0.01);
}

Example 4 with CovarianceMatrix

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

the class TestRegression method testCovariance.

/**
 * Same problem, using the covariance matrix.
 */
@Test
public void testCovariance() {
    setUp();
    RandomUtil.getInstance().setSeed(3848283L);
    ICovarianceMatrix cov = new CovarianceMatrix(data);
    List<Node> nodes = cov.getVariables();
    Node target = nodes.get(0);
    List<Node> regressors = new ArrayList<>();
    for (int i = 1; i < nodes.size(); i++) {
        regressors.add(nodes.get(i));
    }
    Regression regression = new RegressionCovariance(cov);
    RegressionResult result = regression.regress(target, regressors);
    double[] coeffs = result.getCoef();
    assertEquals(0.00, coeffs[0], 0.01);
    assertEquals(-.053, coeffs[1], 0.01);
    assertEquals(0.036, coeffs[2], 0.01);
    assertEquals(.019, coeffs[3], 0.01);
    assertEquals(.007, coeffs[4], 0.01);
}

Example 5 with CovarianceMatrix

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

the class TestSemIm method testCovariancesOfSimulated.

@Test
public void testCovariancesOfSimulated() {
    List<Node> nodes = new ArrayList<>();
    for (int i = 0; i < 5; i++) {
        nodes.add(new ContinuousVariable("X" + (i + 1)));
    }
    Graph randomGraph = new Dag(GraphUtils.randomGraph(nodes, 0, 8, 30, 15, 15, false));
    SemPm semPm1 = new SemPm(randomGraph);
    SemIm semIm1 = new SemIm(semPm1);
    TetradMatrix implCovarC = semIm1.getImplCovar(true);
    implCovarC.toArray();
    DataSet dataSet = semIm1.simulateDataRecursive(1000, false);
    new CovarianceMatrix(dataSet);
}