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

Example 21 with ICovarianceMatrix

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

the class FactorAnalysisAction method main.

public static void main(String[] args) {
    java.util.List<Node> nodes = new ArrayList<>();
    for (int i = 0; i < 9; i++) {
        nodes.add(new ContinuousVariable("X" + (i + 1)));
    }
    Graph graph = new Dag(GraphUtils.randomGraph(nodes, 0, 9, 30, 15, 15, false));
    SemPm pm = new SemPm(graph);
    SemIm im = new SemIm(pm);
    DataSet data = im.simulateData(500, false);
    ICovarianceMatrix cov = new CovarianceMatrix(data);
    FactorAnalysis factorAnalysis = new FactorAnalysis(cov);
    // factorAnalysis.centroidUnity();
    factorAnalysis.successiveResidual();
}

Example 22 with ICovarianceMatrix

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

the class CovCellEditor method deleteSelected.

public void deleteSelected() {
    CovMatrixTable model = (CovMatrixTable) getModel();
    ICovarianceMatrix cov = model.getCovMatrix();
    java.util.List<String> selected = cov.getSelectedVariableNames();
    java.util.List<String> remaining = cov.getVariableNames();
    remaining.removeAll(selected);
    cov.removeVariables(remaining);
    firePropertyChange("modelChanged", null, null);
    model.fireTableDataChanged();
}

Example 23 with ICovarianceMatrix

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

the class SemEstimatorWrapper method setParams.

private boolean setParams(SemPmWrapper semPmWrapper, DataModelList dataModel) {
    for (DataModel model : dataModel) {
        if (model instanceof DataSet) {
            DataSet dataSet = (DataSet) model;
            SemPm semPm = semPmWrapper.getSemPm();
            this.semPm = semPm;
            SemEstimator estimator = new SemEstimator(dataSet, semPm, getOptimizer());
            estimator.setNumRestarts(getParams().getInt("numRestarts", 1));
            estimator.setScoreType((ScoreType) getParams().get("scoreType", ScoreType.Fgls));
            if (!degreesOfFreedomCheck(semPm)) {
                return true;
            }
            estimator.estimate();
            getMultipleResultList().add(estimator);
        } else if (model instanceof ICovarianceMatrix) {
            ICovarianceMatrix covMatrix = new CovarianceMatrix((ICovarianceMatrix) model);
            SemPm semPm = semPmWrapper.getSemPm();
            this.semPm = semPm;
            SemEstimator estimator = new SemEstimator(covMatrix, semPm, getOptimizer());
            estimator.setNumRestarts(getParams().getInt("numRestarts", 1));
            estimator.setScoreType((ScoreType) getParams().get("scoreType", ScoreType.Fgls));
            if (!degreesOfFreedomCheck(semPm)) {
                return true;
            }
            estimator.estimate();
            getMultipleResultList().add(estimator);
        } else {
            throw new IllegalArgumentException("Data must consist of continuous data sets or covariance matrices.");
        }
    }
    return false;
}

Example 24 with ICovarianceMatrix

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

the class CovMatrixAverageWrapper method calcAverage.

private void calcAverage(List<DataWrapper> wrappers) {
    List<TetradMatrix> cov = new ArrayList<>();
    for (int i = 0; i < wrappers.size(); i++) {
        DataModel selectedDataModel = wrappers.get(i).getSelectedDataModel();
        if (!(selectedDataModel instanceof ICovarianceMatrix)) {
            throw new IllegalArgumentException("Sorry, this is an average only over covariance matrices.");
        }
        cov.add(((ICovarianceMatrix) selectedDataModel).getMatrix());
    }
    TetradMatrix cov3 = new TetradMatrix(cov.get(0).rows(), cov.get(0).rows());
    for (int i = 0; i < cov.get(0).rows(); i++) {
        for (int j = 0; j < cov.get(0).rows(); j++) {
            double c = 0.0;
            for (int k = 0; k < cov.size(); k++) {
                c += cov.get(k).get(i, j);
            }
            c /= cov.size();
            cov3.set(i, j, c);
            cov3.set(j, i, c);
        }
    }
    DataModel m = wrappers.get(0).getSelectedDataModel();
    ICovarianceMatrix _cov = (ICovarianceMatrix) m;
    List<Node> nodes = _cov.getVariables();
    int n = _cov.getSampleSize();
    ICovarianceMatrix covWrapper = new CovarianceMatrix(nodes, cov3, n);
    setDataModel(covWrapper);
}

Example 25 with ICovarianceMatrix

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

the class TestSem method testEstimation.

/**
 * The point of this test is to try to detect if the function of the
 * estimation ever changes for perhaps extraneous reasons.
 */
@Test
public void testEstimation() {
    Graph graph = constructGraph1();
    SemPm semPm = new SemPm(graph);
    ICovarianceMatrix covMatrix = constructCovMatrix1();
    SemEstimator estimator = new SemEstimator(covMatrix, semPm);
    estimator.estimate();
    SemIm semIm2 = estimator.getEstimatedSem();
    double[][] edgeCoef = semIm2.getEdgeCoef().toArray();
    double[][] _edgeCoef = { { 0.0000, 0.7750, 0.0000, 1.3192, 0.0000 }, { 0.0000, 0.0000, 1.0756, 0.0000, 0.0000 }, { 0.0000, 0.0000, 0.0000, 0.9639, 0.0000 }, { 0.0000, 0.0000, 0.0000, 0.0000, 0.5198 }, { 0.0000, 0.0000, 0.0000, 0.0000, 0.0000 } };
    for (int i = 0; i < edgeCoef.length; i++) {
        for (int j = 0; j < edgeCoef[i].length; j++) {
            assertEquals(edgeCoef[i][j], _edgeCoef[i][j], .001);
        }
    }
    double[][] errCovar = semIm2.getErrCovar().toArray();
    double[][] _errCovar = { { 1.0439, 0.0000, 0.0000, 0.0000, 0.0000 }, { 0.0000, 0.9293, 0.0000, 0.0000, 0.0000 }, { 0.0000, 0.0000, 1.0756, 0.0000, 0.0000 }, { 0.0000, 0.0000, 0.0000, 1.0233, 0.0000 }, { 0.0000, 0.0000, 0.0000, 0.0000, 1.0465 } };
    for (int i = 0; i < edgeCoef.length; i++) {
        for (int j = 0; j < edgeCoef[i].length; j++) {
            assertEquals(errCovar[i][j], _errCovar[i][j], .001);
        }
    }
}