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

Example 21 with RegressionResult

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

the class LingamPattern2 method getScore2.

private Score getScore2(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 residuals = new TetradMatrix(totalSampleSize, numCols);
    for (int j = 0; j < nodes.size(); j++) {
        List<Double> _residuals = new ArrayList<>();
        Node _target = nodes.get(j);
        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);
        }
        for (int m = 0; m < data.size(); m++) {
            RegressionResult result = regressions.get(m).regress(target, regressors);
            TetradVector residualsSingleDataset = result.getResiduals();
            DoubleArrayList _residualsSingleDataset = new DoubleArrayList(residualsSingleDataset.toArray());
            double mean = Descriptive.mean(_residualsSingleDataset);
            double std = Descriptive.standardDeviation(Descriptive.variance(_residualsSingleDataset.size(), Descriptive.sum(_residualsSingleDataset), Descriptive.sumOfSquares(_residualsSingleDataset)));
            for (int i2 = 0; i2 < _residualsSingleDataset.size(); i2++) {
                _residualsSingleDataset.set(i2, (_residualsSingleDataset.get(i2) - mean) / std);
            }
            for (int k = 0; k < _residualsSingleDataset.size(); k++) {
                _residuals.add(_residualsSingleDataset.get(k));
            }
        }
        for (int k = 0; k < _residuals.size(); k++) {
            residuals.set(k, j, _residuals.get(k));
        }
    }
    for (int i = 0; i < nodes.size(); i++) {
        DoubleArrayList f = new DoubleArrayList(residuals.getColumn(i).toArray());
        for (int j = 0; j < f.size(); j++) {
            f.set(j, Math.abs(f.get(j)));
        }
        double _mean = Descriptive.mean(f);
        double diff = _mean - Math.sqrt(2.0 / Math.PI);
        score += diff * diff;
    }
    for (int j = 0; j < residuals.columns(); j++) {
        double[] x = residuals.getColumn(j).toArray();
        double p = new AndersonDarlingTest(x).getP();
        pValues[j] = p;
    }
    return new Score(score, pValues);
}

Example 22 with RegressionResult

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

the class Lofs2 method regressionCoef.

private double regressionCoef(double[] xValues, double[] yValues) {
    List<Node> v = new ArrayList<>();
    v.add(new GraphNode("x"));
    v.add(new GraphNode("y"));
    TetradMatrix bothData = new TetradMatrix(xValues.length, 2);
    for (int i = 0; i < xValues.length; i++) {
        bothData.set(i, 0, xValues[i]);
        bothData.set(i, 1, yValues[i]);
    }
    Regression regression2 = new RegressionDataset(bothData, v);
    RegressionResult result;
    try {
        result = regression2.regress(v.get(0), v.get(1));
    } catch (Exception e) {
        return Double.NaN;
    }
    return result.getCoef()[1];
}

Example 23 with RegressionResult

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

the class TimeSeriesUtils method getSelfLoopCoefs.

public static double[] getSelfLoopCoefs(DataSet timeSeries) {
    DataSet timeLags = createLagData(timeSeries, 1);
    double[] coefs = new double[timeSeries.getNumColumns()];
    for (int j = 0; j < timeSeries.getNumColumns(); j++) {
        Node target = timeLags.getVariable(j);
        Node selfLoop = timeLags.getVariable(j + timeSeries.getNumColumns());
        List<Node> regressors = Collections.singletonList(selfLoop);
        Regression regression = new RegressionDataset(timeLags);
        RegressionResult result = regression.regress(target, regressors);
        coefs[j] = result.getCoef()[1];
    }
    return coefs;
}

Example 24 with RegressionResult

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

the class TimeSeriesUtils method ar.

/**
 * @return the VAR residuals of the given time series with the given number
 * of lags. That is, every variable at the model lag is regressed onto every
 * variable at previous lags, up to the given number of lags, and the
 * residuals of these regressions for each variable are returned.
 */
public static DataSet ar(DataSet timeSeries, int numLags) {
    DataSet timeLags = createLagData(timeSeries, numLags);
    List<Node> regressors = new ArrayList<>();
    for (int i = timeSeries.getNumColumns(); i < timeLags.getNumColumns(); i++) {
        regressors.add(timeLags.getVariable(i));
    }
    Regression regression = new RegressionDataset(timeLags);
    // Regression regression = new RegressionDatasetGeneralized(timeLags);
    TetradMatrix residuals = new TetradMatrix(timeLags.getNumRows(), timeSeries.getNumColumns());
    for (int i = 0; i < timeSeries.getNumColumns(); i++) {
        Node target = timeLags.getVariable(i);
        RegressionResult result = regression.regress(target, regressors);
        TetradVector residualsColumn = result.getResiduals();
        // residuals.viewColumn(i).assign(residualsColumn);
        residuals.assignColumn(i, residualsColumn);
    }
    return ColtDataSet.makeContinuousData(timeSeries.getVariables(), residuals);
}

Example 25 with RegressionResult

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

the class TimeSeriesUtils method structuralVar.

public static VarResult structuralVar(DataSet timeSeries, int numLags) {
    DataSet timeLags = TimeSeriesUtils.createLagData(timeSeries, numLags);
    IKnowledge knowledge = timeLags.getKnowledge().copy();
    for (int i = 0; i <= numLags; i++) {
        knowledge.setTierForbiddenWithin(i, true);
    }
    Score score;
    if (timeLags.isDiscrete()) {
        score = new BDeuScore(timeLags);
    } else if (timeLags.isContinuous()) {
        SemBicScore semBicScore = new SemBicScore(new CovarianceMatrixOnTheFly(timeLags));
        semBicScore.setPenaltyDiscount(2.0);
        score = semBicScore;
    } else {
        throw new IllegalArgumentException("Mixed data set");
    }
    Fges search = new Fges(score);
    search.setKnowledge(knowledge);
    Graph graph = search.search();
    // want to collapse graph here...
    Graph collapsedVarGraph = new EdgeListGraph(timeSeries.getVariables());
    for (Edge edge : graph.getEdges()) {
        String node1_before = edge.getNode1().getName();
        String node2_before = edge.getNode2().getName();
        String node1_after = node1_before.substring(0, node1_before.indexOf("."));
        String node2_after = node2_before.substring(0, node2_before.indexOf("."));
        Node node1 = collapsedVarGraph.getNode(node1_after);
        Node node2 = collapsedVarGraph.getNode(node2_after);
        Edge _edge = new Edge(node1, node2, edge.getEndpoint1(), edge.getEndpoint2());
        if (!collapsedVarGraph.containsEdge(_edge)) {
            collapsedVarGraph.addEdge(_edge);
        }
    }
    TetradMatrix residuals = new TetradMatrix(timeLags.getNumRows(), timeSeries.getNumColumns());
    Regression regression = new RegressionDataset(timeLags);
    for (int i = 0; i < timeSeries.getNumColumns(); i++) {
        Node target = timeLags.getVariable(i);
        List<Node> regressors = new ArrayList<>();
        // timelags.getVariable(i).
        for (int j = 0; j <= 0; /*numLags*/
        j++) {
            Node variable = timeLags.getVariable(i + j * timeSeries.getNumColumns());
            regressors.addAll(graph.getParents(variable));
        }
        RegressionResult result = regression.regress(target, regressors);
        TetradVector residualsColumn = result.getResiduals();
        // residuals.viewColumn(i).assign(residualsColumn);
        residuals.assignColumn(i, residualsColumn);
    }
    return new VarResult(ColtDataSet.makeContinuousData(timeSeries.getVariables(), residuals), collapsedVarGraph);
}