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

Example 11 with Regression

use of edu.cmu.tetrad.regression.Regression 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 12 with Regression

use of edu.cmu.tetrad.regression.Regression 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 13 with Regression

use of edu.cmu.tetrad.regression.Regression 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 14 with Regression

use of edu.cmu.tetrad.regression.Regression 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);
}

Example 15 with Regression

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

the class SemIm method getStandardError.

public double getStandardError(Parameter parameter, int maxFreeParams) {
    TetradMatrix sampleCovar = getSampleCovar();
    if (sampleCovar == null) {
        return Double.NaN;
    }
    if (getFreeParameters().contains(parameter)) {
        if (getNumFreeParams() <= maxFreeParams) {
            if (parameter.getNodeA() != parameter.getNodeB()) {
                Node nodeA = parameter.getNodeA();
                Node nodeB = parameter.getNodeB();
                Node parent;
                Node child;
                Graph graph = getSemPm().getGraph();
                if (graph.isParentOf(nodeA, nodeB)) {
                    parent = nodeA;
                    child = nodeB;
                } else {
                    parent = nodeB;
                    child = nodeA;
                }
                if (child.getName().startsWith("E_")) {
                    return Double.NaN;
                }
                CovarianceMatrix cov = new CovarianceMatrix(measuredNodes, sampleCovar, sampleSize);
                Regression regression = new RegressionCovariance(cov);
                List<Node> parents = graph.getParents(child);
                for (Node node : new ArrayList<>(parents)) {
                    if (node.getName().startsWith("E_")) {
                        parents.remove(node);
                    }
                }
                if (!(child.getNodeType() == NodeType.LATENT) && !containsLatent(parents)) {
                    RegressionResult result = regression.regress(child, parents);
                    double[] se = result.getSe();
                    return se[parents.indexOf(parent) + 1];
                }
            }
            if (this.sampleCovarC == null) {
                this.standardErrors = null;
                return Double.NaN;
            }
            int index = getFreeParameters().indexOf(parameter);
            double[] doubles = standardErrors();
            if (doubles == null) {
                return Double.NaN;
            }
            return doubles[index];
        } else {
            return Double.NaN;
        }
    } else if (getFixedParameters().contains(parameter)) {
        return 0.0;
    }
    throw new IllegalArgumentException("That is not a parameter of this model: " + parameter);
}