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

Example 16 with CovarianceMatrix

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

the class TestDeltaTetradTest method testBollenExample2.

// Bollen and Ting p. 167 (Confirmatory Tetrad Analysis). Union Sentiment.
@Test
public void testBollenExample2() {
    CovarianceMatrix cov = getBollenExample2Data();
    List<Node> variables = cov.getVariables();
    Node y1 = variables.get(0);
    Node y2 = variables.get(1);
    Node y3 = variables.get(2);
    Node x1 = variables.get(3);
    Node x2 = variables.get(4);
    Tetrad t1 = new Tetrad(y1, x1, x2, y2);
    DeltaTetradTest test = new DeltaTetradTest(cov);
    double chiSq = test.calcChiSquare(t1);
    double pValue = test.getPValue();
    assertEquals(.68, chiSq, 0.01);
    assertEquals(0.40, pValue, 0.01);
// They get chi square = .73  p = .39  df = 1
}

Example 17 with CovarianceMatrix

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

the class TestSem method constructCovMatrix2.

private ICovarianceMatrix constructCovMatrix2() {
    String[] vars = new String[] { "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8" };
    int sampleSize = 173;
    double[][] arr = { { 1.0 }, { .215, 1.0 }, { -.164, -.472, 1.0 }, { .112, .079, -.157, 1.0 }, { .034, .121, -.184, .407, 1.0 }, { .101, .197, -.190, .176, .120, 1.0 }, { .071, -.172, .206, -.049, -.084, -.291, 1.0 }, { .043, -.038, -.037, -.062, .028, .166, -.149, 1.0 } };
    double[][] m = MatrixUtils.convertLowerTriangleToSymmetric(arr);
    TetradMatrix m2 = new TetradMatrix(m);
    return new CovarianceMatrix(DataUtils.createContinuousVariables(vars), m2, sampleSize);
}

Example 18 with CovarianceMatrix

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

the class TestSem method constructCovMatrix1.

private ICovarianceMatrix constructCovMatrix1() {
    String[] vars = new String[] { "X1", "X2", "X3", "X4", "X5" };
    double[][] arr = { { 1.04408 }, { 0.80915, 1.55607 }, { 0.89296, 1.67375, 2.87584 }, { 2.23792, 2.68536, 3.94996, 7.78259 }, { 1.17516, 1.36337, 1.99039, 4.04533, 3.14922 } };
    double[][] m = MatrixUtils.convertLowerTriangleToSymmetric(arr);
    TetradMatrix m2 = new TetradMatrix(m);
    return new CovarianceMatrix(DataUtils.createContinuousVariables(vars), m2, 1000);
}

Example 19 with CovarianceMatrix

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

the class TestSemIm method test3.

@Test
public void test3() {
    Graph graph = constructGraph1();
    SemPm semPm = new SemPm(graph);
    SemIm semIm = new SemIm(semPm);
    DataSet dataSetContColumnContinuous = semIm.simulateData(500, false);
    ICovarianceMatrix covMatrix = new CovarianceMatrix(dataSetContColumnContinuous);
    SemEstimator estimator2 = new SemEstimator(covMatrix, semPm);
    estimator2.estimate();
    estimator2.getEstimatedSem();
    SemEstimator estimator3 = new SemEstimator(covMatrix, semPm);
    estimator3.estimate();
    estimator3.getEstimatedSem();
    SemPm semPm4 = new SemPm(graph);
    SemEstimator estimator4 = new SemEstimator(covMatrix, semPm4);
    estimator4.estimate();
    estimator4.getEstimatedSem();
    SemPm semPm5 = new SemPm(graph);
    SemEstimator estimator5 = new SemEstimator(covMatrix, semPm5);
    estimator5.estimate();
    estimator5.getEstimatedSem();
}

Example 20 with CovarianceMatrix

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

the class InverseCorrelation method search.

public Graph search() {
    CovarianceMatrix cov = new CovarianceMatrix(data);
    TetradMatrix _data = cov.getMatrix();
    TetradMatrix inverse = _data.inverse();
    System.out.println(inverse);
    Graph graph = new EdgeListGraph(data.getVariables());
    for (int i = 0; i < inverse.rows(); i++) {
        for (int j = i + 1; j < inverse.columns(); j++) {
            double a = inverse.get(i, j);
            double b = inverse.get(i, i);
            double c = inverse.get(j, j);
            double r = -a / Math.sqrt(b * c);
            int sampleSize = data.getNumRows();
            int z = data.getNumColumns();
            double fisherZ = Math.sqrt(sampleSize - z - 3.0) * 0.5 * (Math.log(1.0 + r) - Math.log(1.0 - r));
            double p = getPValue(fisherZ);
            if (p < threshold) {
                Node x = graph.getNodes().get(i);
                Node y = graph.getNodes().get(j);
                graph.addUndirectedEdge(x, y);
            }
        // if (abs(fisherZ) > threshold) {
        // System.out.println(fisherZ + " &&& " + p);
        // Node x = graph.getNodes().get(i);
        // Node y = graph.getNodes().get(j);
        // graph.addUndirectedEdge(x, y);
        // }
        // if (Math.abs(inverse.get(i, j)) > threshold) {
        // Node x = graph.getNodes().get(i);
        // Node y = graph.getNodes().get(j);
        // graph.addUndirectedEdge(x, y);
        // }
        }
    }
    return graph;
}