Examples with SemEstimator edu.cmu.tetrad.sem.SemEstimator used on opensource projects

Example 6 with SemEstimator

use of edu.cmu.tetrad.sem.SemEstimator in project tetrad by cmu-phil.

the class TestSemVarMeans method testMeansRecursive.

@Test
public void testMeansRecursive() {
    Graph graph = constructGraph1();
    SemPm semPm1 = new SemPm(graph);
    List<Parameter> parameters = semPm1.getParameters();
    for (Parameter p : parameters) {
        p.setInitializedRandomly(false);
    }
    SemIm semIm1 = new SemIm(semPm1);
    double[] means = { 5.0, 4.0, 3.0, 2.0, 1.0 };
    RandomUtil.getInstance().setSeed(-379467L);
    for (int i = 0; i < semIm1.getVariableNodes().size(); i++) {
        Node node = semIm1.getVariableNodes().get(i);
        semIm1.setMean(node, means[i]);
    }
    DataSet dataSet = semIm1.simulateDataRecursive(1000, false);
    SemEstimator semEst = new SemEstimator(dataSet, semPm1);
    semEst.estimate();
    SemIm estSemIm = semEst.getEstimatedSem();
    List<Node> nodes = semPm1.getVariableNodes();
    for (Node node : nodes) {
        double mean = semIm1.getMean(node);
        assertEquals(mean, estSemIm.getMean(node), 0.5);
    }
}

Example 7 with SemEstimator

use of edu.cmu.tetrad.sem.SemEstimator in project tetrad by cmu-phil.

the class TestStandardizedSem method rtest8.

public void rtest8() {
    // RandomUtil.getInstance().setSeed(2958442283L);
    SemGraph graph = new SemGraph();
    Node x = new ContinuousVariable("X");
    Node y = new ContinuousVariable("Y");
    Node z = new ContinuousVariable("Z");
    graph.addNode(x);
    graph.addNode(y);
    graph.addNode(z);
    graph.addDirectedEdge(x, y);
    graph.addBidirectedEdge(x, y);
    graph.addDirectedEdge(x, z);
    graph.addDirectedEdge(y, z);
    graph.setShowErrorTerms(true);
    SemPm semPm = new SemPm(graph);
    SemIm semIm = new SemIm(semPm);
    StandardizedSemIm sem = new StandardizedSemIm(semIm, StandardizedSemIm.Initialization.CALCULATE_FROM_SEM);
    DataSet data = semIm.simulateDataCholesky(1000, false);
    data = ColtDataSet.makeContinuousData(data.getVariables(), DataUtils.standardizeData(data.getDoubleData()));
    SemEstimator estimator = new SemEstimator(data, semPm);
    semIm = estimator.estimate();
    DataSet data2 = semIm.simulateDataReducedForm(1000, false);
    DataSet data3 = sem.simulateDataReducedForm(1000, false);
    StandardizedSemIm.ParameterRange range2 = sem.getCovarianceRange(x, y);
    double high = range2.getHigh();
    double low = range2.getLow();
    if (high == Double.POSITIVE_INFINITY)
        high = 1000;
    if (low == Double.NEGATIVE_INFINITY)
        low = -1000;
    double coef = low + RandomUtil.getInstance().nextDouble() * (high - low);
    assertTrue(sem.setErrorCovariance(x, y, coef));
    assertTrue(isStandardized(sem));
}

Example 8 with SemEstimator

use of edu.cmu.tetrad.sem.SemEstimator in project tetrad by cmu-phil.

the class TestStandardizedSem method test5.

@Test
public void test5() {
    RandomUtil.getInstance().setSeed(582374923L);
    SemGraph graph = new SemGraph();
    graph.setShowErrorTerms(true);
    Node x1 = new ContinuousVariable("X1");
    Node x2 = new ContinuousVariable("X2");
    Node x3 = new ContinuousVariable("X3");
    graph.addNode(x1);
    graph.addNode(x2);
    graph.addNode(x3);
    graph.setShowErrorTerms(true);
    Node ex1 = graph.getExogenous(x1);
    Node ex2 = graph.getExogenous(x2);
    Node ex3 = graph.getExogenous(x3);
    graph.addDirectedEdge(x1, x3);
    graph.addDirectedEdge(x2, x3);
    // graph.addDirectedEdge(x1, x2);
    // graph.addBidirectedEdge(ex1, ex2);
    SemPm pm = new SemPm(graph);
    SemIm im = new SemIm(pm);
    DataSet dataSet = im.simulateDataRecursive(1000, false);
    TetradMatrix _dataSet = dataSet.getDoubleData();
    _dataSet = DataUtils.standardizeData(_dataSet);
    DataSet dataSetStandardized = ColtDataSet.makeData(dataSet.getVariables(), _dataSet);
    SemEstimator estimator = new SemEstimator(dataSetStandardized, im.getSemPm());
    SemIm imStandardized = estimator.estimate();
    StandardizedSemIm sem = new StandardizedSemIm(im);
    // sem.setErrorCovariance(ex1, ex2, -.24);
    assertTrue(isStandardized(sem));
}

Example 9 with SemEstimator

use of edu.cmu.tetrad.sem.SemEstimator in project tetrad by cmu-phil.

the class TestStandardizedSem method test6.

@Test
public void test6() {
    // RandomUtil.getInstance().setSeed(582374923L);
    SemGraph graph = new SemGraph();
    graph.setShowErrorTerms(true);
    Node x1 = new ContinuousVariable("X1");
    Node x2 = new ContinuousVariable("X2");
    Node x3 = new ContinuousVariable("X3");
    graph.addNode(x1);
    graph.addNode(x2);
    graph.addNode(x3);
    graph.setShowErrorTerms(true);
    Node ex1 = graph.getExogenous(x1);
    Node ex2 = graph.getExogenous(x2);
    Node ex3 = graph.getExogenous(x3);
    graph.addDirectedEdge(x1, x3);
    graph.addDirectedEdge(x2, x3);
    graph.addDirectedEdge(x1, x2);
    graph.addBidirectedEdge(ex1, ex2);
    // List<List<Node>> treks = DataGraphUtils.treksIncludingBidirected(graph, x1, x3);
    // 
    // for (List<Node> trek : treks) {
    // System.out.println(trek);
    // }
    SemPm pm = new SemPm(graph);
    SemIm im = new SemIm(pm);
    DataSet dataSet = im.simulateDataRecursive(1000, false);
    TetradMatrix _dataSet = dataSet.getDoubleData();
    _dataSet = DataUtils.standardizeData(_dataSet);
    DataSet dataSetStandardized = ColtDataSet.makeData(dataSet.getVariables(), _dataSet);
    SemEstimator estimator = new SemEstimator(dataSetStandardized, im.getSemPm());
    SemIm imStandardized = estimator.estimate();
    StandardizedSemIm sem = new StandardizedSemIm(im);
    assertTrue(isStandardized(sem));
}

Example 10 with SemEstimator

use of edu.cmu.tetrad.sem.SemEstimator in project tetrad by cmu-phil.

the class PcStableLocalSearchEditor method reportIfContinuous.

private String reportIfContinuous(Graph dag, DataSet dataSet) {
    SemPm semPm = new SemPm(dag);
    SemEstimator estimator = new SemEstimator(dataSet, semPm);
    estimator.estimate();
    SemIm semIm = estimator.getEstimatedSem();
    NumberFormat nf = NumberFormat.getInstance();
    nf.setMaximumFractionDigits(4);
    StringBuilder buf = new StringBuilder();
    buf.append("\nDegrees of Freedom = ").append(semPm.getDof()).append("Chi-Square = ").append(nf.format(semIm.getChiSquare())).append("\nP Value = ").append(nf.format(semIm.getPValue())).append("\nBIC Score = ").append(nf.format(semIm.getBicScore()));
    buf.append("\n\nThe above chi square test assumes that the maximum " + "likelihood function over the measured variables has been " + "maximized. Under that assumption, the null hypothesis for " + "the test is that the population covariance matrix over all " + "of the measured variables is equal to the estimated covariance " + "matrix over all of the measured variables written as a function " + "of the free model parameters--that is, the unfixed parameters " + "for each directed edge (the linear coefficient for that edge), " + "each exogenous variable (the variance for the error term for " + "that variable), and each bidirected edge (the covariance for " + "the exogenous variables it connects).  The model is explained " + "in Bollen, Structural Equations with Latent Variable, 110. ");
    return buf.toString();
}