Examples with RegressionResult edu.cmu.tetrad.regression.RegressionResult used on opensource projects

Example 6 with RegressionResult

use of edu.cmu.tetrad.regression.RegressionResult in project tetrad by cmu-phil.

the class TimeSeriesUtils method ar2.

public static DataSet ar2(DataSet timeSeries, int numLags) {
    List<Node> missingVariables = new ArrayList<>();
    for (Node node : timeSeries.getVariables()) {
        int index = timeSeries.getVariables().indexOf(node);
        boolean missing = true;
        for (int i = 0; i < timeSeries.getNumRows(); i++) {
            if (!Double.isNaN(timeSeries.getDouble(i, index))) {
                missing = false;
                break;
            }
        }
        if (missing) {
            missingVariables.add(node);
        }
    }
    DataSet timeLags = createLagData(timeSeries, numLags);
    Regression regression = new RegressionDataset(timeLags);
    TetradMatrix residuals = new TetradMatrix(timeLags.getNumRows(), timeSeries.getNumColumns());
    for (int i = 0; i < timeSeries.getNumColumns(); i++) {
        Node target = timeLags.getVariable(i);
        int index = timeSeries.getVariables().indexOf(target);
        if (missingVariables.contains(target)) {
            for (int i2 = 0; i2 < residuals.rows(); i2++) {
                residuals.set(i2, index, Double.NaN);
            }
            continue;
        }
        List<Node> regressors = new ArrayList<>();
        for (int i2 = timeSeries.getNumColumns(); i2 < timeLags.getNumColumns(); i2++) {
            int varIndex = i2 % timeSeries.getNumColumns();
            Node var = timeSeries.getVariable(varIndex);
            if (missingVariables.contains(var)) {
                continue;
            }
            regressors.add(timeLags.getVariable(i2));
        }
        RegressionResult result = regression.regress(target, regressors);
        TetradVector residualsColumn = result.getResiduals();
        residuals.assignColumn(i, residualsColumn);
    }
    return ColtDataSet.makeContinuousData(timeSeries.getVariables(), residuals);
}

Example 7 with RegressionResult

use of edu.cmu.tetrad.regression.RegressionResult in project tetrad by cmu-phil.

the class IndTestMultinomialLogisticRegressionWald method isIndependentRegression.

private boolean isIndependentRegression(Node x, Node y, List<Node> z) {
    if (!variablesPerNode.containsKey(x)) {
        throw new IllegalArgumentException("Unrecogized node: " + x);
    }
    if (!variablesPerNode.containsKey(y)) {
        throw new IllegalArgumentException("Unrecogized node: " + y);
    }
    for (Node node : z) {
        if (!variablesPerNode.containsKey(node)) {
            throw new IllegalArgumentException("Unrecogized node: " + node);
        }
    }
    List<Node> regressors = new ArrayList<>();
    if (y instanceof ContinuousVariable) {
        regressors.add(internalData.getVariable(y.getName()));
    } else {
        regressors.addAll(variablesPerNode.get(y));
    }
    for (Node _z : z) {
        regressors.addAll(variablesPerNode.get(_z));
    }
    int[] _rows = getNonMissingRows(x, y, z);
    regression.setRows(_rows);
    RegressionResult result;
    try {
        result = regression.regress(x, regressors);
    } catch (Exception e) {
        return false;
    }
    double p = 1;
    if (y instanceof ContinuousVariable) {
        p = result.getP()[1];
    } else {
        for (int i = 0; i < variablesPerNode.get(y).size(); i++) {
            double val = result.getP()[1 + i];
            if (val < p)
                p = val;
        }
    }
    this.lastP = p;
    boolean indep = p > alpha;
    if (indep) {
        TetradLogger.getInstance().log("independencies", SearchLogUtils.independenceFactMsg(x, y, z, p));
    } else {
        TetradLogger.getInstance().log("dependencies", SearchLogUtils.dependenceFactMsg(x, y, z, p));
    }
    return indep;
}

Example 8 with RegressionResult

use of edu.cmu.tetrad.regression.RegressionResult in project tetrad by cmu-phil.

the class TestRegression method testCovariance.

/**
 * Same problem, using the covariance matrix.
 */
@Test
public void testCovariance() {
    setUp();
    RandomUtil.getInstance().setSeed(3848283L);
    ICovarianceMatrix cov = new CovarianceMatrix(data);
    List<Node> nodes = cov.getVariables();
    Node target = nodes.get(0);
    List<Node> regressors = new ArrayList<>();
    for (int i = 1; i < nodes.size(); i++) {
        regressors.add(nodes.get(i));
    }
    Regression regression = new RegressionCovariance(cov);
    RegressionResult result = regression.regress(target, regressors);
    double[] coeffs = result.getCoef();
    assertEquals(0.00, coeffs[0], 0.01);
    assertEquals(-.053, coeffs[1], 0.01);
    assertEquals(0.036, coeffs[2], 0.01);
    assertEquals(.019, coeffs[3], 0.01);
    assertEquals(.007, coeffs[4], 0.01);
}

Example 9 with RegressionResult

use of edu.cmu.tetrad.regression.RegressionResult in project tetrad by cmu-phil.

the class LingamPattern2 method getScore1.

// =============================PRIVATE METHODS=========================//
private Score getScore1(Graph dag, List<TetradMatrix> data, List<Node> variables) {
    // System.out.println("Scoring DAG: " + dag);
    List<Regression> regressions = new ArrayList<>();
    for (TetradMatrix _data : data) {
        regressions.add(new RegressionDataset(_data, variables));
    }
    int totalSampleSize = 0;
    for (TetradMatrix _data : data) {
        totalSampleSize += _data.rows();
    }
    int numCols = data.get(0).columns();
    List<Node> nodes = dag.getNodes();
    double score = 0.0;
    double[] pValues = new double[nodes.size()];
    TetradMatrix absoluteStandardizedResiduals = new TetradMatrix(totalSampleSize, numCols);
    for (int i = 0; i < nodes.size(); i++) {
        List<Double> _absoluteStandardizedResiduals = new ArrayList<>();
        for (int j = 0; j < data.size(); j++) {
            Node _target = nodes.get(i);
            List<Node> _regressors = dag.getParents(_target);
            Node target = getVariable(variables, _target.getName());
            List<Node> regressors = new ArrayList<>();
            for (Node _regressor : _regressors) {
                Node variable = getVariable(variables, _regressor.getName());
                regressors.add(variable);
            }
            RegressionResult result = regressions.get(j).regress(target, regressors);
            TetradVector residualsColumn = result.getResiduals();
            DoubleArrayList _absoluteStandardizedResidualsColumn = new DoubleArrayList(residualsColumn.toArray());
            double mean = Descriptive.mean(_absoluteStandardizedResidualsColumn);
            double std = Descriptive.standardDeviation(Descriptive.variance(_absoluteStandardizedResidualsColumn.size(), Descriptive.sum(_absoluteStandardizedResidualsColumn), Descriptive.sumOfSquares(_absoluteStandardizedResidualsColumn)));
            for (int i2 = 0; i2 < _absoluteStandardizedResidualsColumn.size(); i2++) {
                _absoluteStandardizedResidualsColumn.set(i2, (_absoluteStandardizedResidualsColumn.get(i2) - mean) / std);
                _absoluteStandardizedResidualsColumn.set(i2, Math.abs(_absoluteStandardizedResidualsColumn.get(i2)));
            }
            for (int k = 0; k < _absoluteStandardizedResidualsColumn.size(); k++) {
                _absoluteStandardizedResiduals.add(_absoluteStandardizedResidualsColumn.get(k));
            }
        }
        DoubleArrayList absoluteStandardResidualsList = new DoubleArrayList(absoluteStandardizedResiduals.getColumn(i).toArray());
        for (int k = 0; k < _absoluteStandardizedResiduals.size(); k++) {
            absoluteStandardizedResiduals.set(k, i, _absoluteStandardizedResiduals.get(k));
        }
        double _mean = Descriptive.mean(absoluteStandardResidualsList);
        double diff = _mean - Math.sqrt(2.0 / Math.PI);
        score += diff * diff;
    }
    for (int j = 0; j < absoluteStandardizedResiduals.columns(); j++) {
        double[] x = absoluteStandardizedResiduals.getColumn(j).toArray();
        double p = new AndersonDarlingTest(x).getP();
        pValues[j] = p;
    }
    return new Score(score, pValues);
}

Example 10 with RegressionResult

use of edu.cmu.tetrad.regression.RegressionResult in project tetrad by cmu-phil.

the class RegressionEditor method runRegression.

// ========================= Private Methods ========================//
/**
 * Runs the regression, resetting the text output and graph output.
 */
private void runRegression() {
    runner.execute();
    Graph graph = runner.getOutGraph();
    GraphUtils.circleLayout(graph, 200, 200, 150);
    GraphUtils.fruchtermanReingoldLayout(graph);
    workbench.setGraph(graph);
    RegressionResult report = runner.getResult();
    reportText.setText(report.toString());
    textWithTable.removeAll();
    textWithTable.setLayout(new BorderLayout());
    textWithTable.add(TextWithTable.component(report.getPreamble(), report.getResultsTable()));
    textWithTable.revalidate();
    textWithTable.repaint();
}