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

Example 41 with SemPm

use of edu.cmu.tetrad.sem.SemPm 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 42 with SemPm

use of edu.cmu.tetrad.sem.SemPm 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 43 with SemPm

use of edu.cmu.tetrad.sem.SemPm 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 44 with SemPm

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

Example 45 with SemPm

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

the class TimeSeriesSemSimulation method createData.

@Override
public void createData(Parameters parameters) {
    dataSets = new ArrayList<>();
    graphs = new ArrayList<>();
    Graph graph = randomGraph.createGraph(parameters);
    graph = TimeSeriesUtils.graphToLagGraph(graph, parameters.getInt("numLags"));
    topToBottomLayout((TimeLagGraph) graph);
    this.knowledge = TimeSeriesUtils.getKnowledge(graph);
    for (int i = 0; i < parameters.getInt("numRuns"); i++) {
        if (parameters.getBoolean("differentGraphs") && i > 0) {
            graph = randomGraph.createGraph(parameters);
            graph = TimeSeriesUtils.graphToLagGraph(graph, 2);
            topToBottomLayout((TimeLagGraph) graph);
        }
        graphs.add(graph);
        SemPm pm = new SemPm(graph);
        SemIm im = new SemIm(pm, parameters);
        final int sampleSize = parameters.getInt("sampleSize");
        boolean saveLatentVars = parameters.getBoolean("saveLatentVars");
        DataSet dataSet = im.simulateData(sampleSize, saveLatentVars);
        int numLags = ((TimeLagGraph) graph).getMaxLag();
        dataSet = TimeSeriesUtils.createLagData(dataSet, numLags);
        dataSet.setName("" + (i + 1));
        dataSet.setKnowledge(knowledge.copy());
        dataSets.add(dataSet);
    // LargeScaleSimulation sim = new LargeScaleSimulation(graph);
    // if (parameters.getDouble("coefHigh") > 0.80) {
    // System.out.println("Coefficients have been set (perhaps by default) too " +
    // "high for stationary time series.");
    // System.out.println("Setting coefficient range to [0.20,0.60].");
    // sim.setCoefRange(0.20, 0.60);
    // } else sim.setCoefRange(parameters.getDouble("coefLow"), parameters.getDouble("coefHigh"));
    // boolean isStableTetradMatrix;
    // int attempt = 1;
    // int tierSize = parameters.getInt("numMeasures") + parameters.getInt("numLatents"); //params.getNumVars();
    // int[] sub = new int[tierSize];
    // int[] sub2 = new int[tierSize];
    // for (int j = 0; j < tierSize; j++) {
    // sub[j] = j;
    // sub2[j] = tierSize + j;
    // }
    // DataSet dataSet;
    // do {
    // dataSet = sim.simulateDataFisher(parameters.getInt("sampleSize")); //params.getSampleSize());
    // 
    // TetradMatrix coefMat = new TetradMatrix(sim.getCoefficientMatrix());
    // TetradMatrix B = coefMat.getSelection(sub, sub);
    // TetradMatrix Gamma1 = coefMat.getSelection(sub2, sub);
    // TetradMatrix Gamma0 = TetradMatrix.identity(tierSize).minus(B);
    // TetradMatrix A1 = Gamma0.inverse().times(Gamma1);
    // 
    // isStableTetradMatrix = TimeSeriesUtils.allEigenvaluesAreSmallerThanOneInModulus(A1);
    // attempt++;
    // } while ((!isStableTetradMatrix) && attempt <= 5);
    // if (!isStableTetradMatrix) {
    // System.out.println("%%%%%%%%%% WARNING %%%%%%%% not a stable coefficient matrix, forcing coefs to [0.15,0.3]");
    // System.out.println("Made " + (attempt - 1) + " attempts to get stable matrix.");
    // sim.setCoefRange(0.15, 0.3);
    // dataSet = sim.simulateDataFisher(parameters.getInt("sampleSize"));//params.getSampleSize());
    // } //else System.out.println("Coefficient matrix is stable.");
    // dataSet.setName("" + (i + 1));
    // dataSet.setKnowledge(knowledge.copy());
    // dataSets.add(dataSet);
    }
}