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

Example 41 with SemIm

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

the class PurifyScoreBased method scoreCandidate.

private double scoreCandidate() {
    SemGraph graph = updatedGraph();
    initializeGaussianEM(graph);
    SemPm semPm = new SemPm(graph);
    SemIm semIm = new SemIm(semPm, covarianceMatrix);
    gaussianMaximization(semIm);
    try {
        System.out.println("trunk ll = " + semIm.getTruncLL());
        return -semIm.getTruncLL() - 0.5 * semIm.getNumFreeParams() * Math.log(covarianceMatrix.getSampleSize());
    } catch (IllegalArgumentException e) {
        return -Double.MAX_VALUE;
    }
}

Example 42 with SemIm

use of edu.cmu.tetrad.sem.SemIm 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();
}

Example 43 with SemIm

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

the class PcGesSearchEditor method reportIfCovMatrix.

private String reportIfCovMatrix(Graph dag, ICovarianceMatrix 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();
}

Example 44 with SemIm

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

the class PcGesSearchEditor 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();
}

Example 45 with SemIm

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

the class SemThenDiscretize method simulate.

private DataSet simulate(Graph graph, Parameters parameters) {
    SemPm pm = new SemPm(graph);
    SemIm im = new SemIm(pm);
    DataSet continuousData = im.simulateData(parameters.getInt("sampleSize"), false);
    if (this.shuffledOrder == null) {
        List<Node> shuffledNodes = new ArrayList<>(continuousData.getVariables());
        Collections.shuffle(shuffledNodes);
        this.shuffledOrder = shuffledNodes;
    }
    Discretizer discretizer = new Discretizer(continuousData);
    for (int i = 0; i < shuffledOrder.size() * parameters.getDouble("percentDiscrete") * 0.01; i++) {
        discretizer.equalIntervals(continuousData.getVariable(shuffledOrder.get(i).getName()), parameters.getInt("numCategories"));
    }
    return discretizer.discretize();
}