Examples with ChiSquaredDistribution org.apache.commons.math3.distribution.ChiSquaredDistribution used on opensource projects

Example 16 with ChiSquaredDistribution

use of org.apache.commons.math3.distribution.ChiSquaredDistribution in project tetrad by cmu-phil.

the class MimbuildTrek method getCov.

private TetradMatrix getCov(ICovarianceMatrix _measurescov, List<Node> latents, Node[][] indicators) {
    if (latents.size() != indicators.length) {
        throw new IllegalArgumentException();
    }
    TetradMatrix measurescov = _measurescov.getMatrix();
    TetradMatrix latentscov = new TetradMatrix(latents.size(), latents.size());
    for (int i = 0; i < latentscov.rows(); i++) {
        for (int j = i; j < latentscov.columns(); j++) {
            if (i == j)
                latentscov.set(i, j, 1.0);
            else {
                double v = .5;
                latentscov.set(i, j, v);
                latentscov.set(j, i, v);
            }
        }
    }
    double[][] loadings = new double[indicators.length][];
    for (int i = 0; i < indicators.length; i++) {
        loadings[i] = new double[indicators[i].length];
    }
    for (int i = 0; i < indicators.length; i++) {
        loadings[i] = new double[indicators[i].length];
        for (int j = 0; j < indicators[i].length; j++) {
            loadings[i][j] = .5;
        }
    }
    int[][] indicatorIndices = new int[indicators.length][];
    List<Node> measures = _measurescov.getVariables();
    for (int i = 0; i < indicators.length; i++) {
        indicatorIndices[i] = new int[indicators[i].length];
        for (int j = 0; j < indicators[i].length; j++) {
            indicatorIndices[i][j] = measures.indexOf(indicators[i][j]);
        }
    }
    // Variances of the measures.
    double[] delta = new double[measurescov.rows()];
    for (int i = 0; i < delta.length; i++) {
        delta[i] = 1;
    }
    int numNonMeasureVarianceParams = 0;
    for (int i = 0; i < latentscov.rows(); i++) {
        for (int j = i; j < latentscov.columns(); j++) {
            numNonMeasureVarianceParams++;
        }
    }
    for (int i = 0; i < indicators.length; i++) {
        numNonMeasureVarianceParams += indicators[i].length;
    }
    double[] allParams1 = getAllParams(indicators, latentscov, loadings, delta);
    optimizeNonMeasureVariancesQuick(indicators, measurescov, latentscov, loadings, indicatorIndices);
    // for (int i = 0; i < 10; i++) {
    // optimizeNonMeasureVariancesConditionally(indicators, measurescov, latentscov, loadings, indicatorIndices, delta);
    // optimizeMeasureVariancesConditionally(measurescov, latentscov, loadings, indicatorIndices, delta);
    // 
    // double[] allParams2 = getAllParams(indicators, latentscov, loadings, delta);
    // if (distance(allParams1, allParams2) < epsilon) break;
    // allParams1 = allParams2;
    // }
    this.numParams = allParams1.length;
    // // Very slow but could be done alone.
    optimizeAllParamsSimultaneously(indicators, measurescov, latentscov, loadings, indicatorIndices, delta);
    double N = _measurescov.getSampleSize();
    int p = _measurescov.getDimension();
    int df = (p) * (p + 1) / 2 - (numParams);
    double x = (N - 1) * minimum;
    this.pValue = 1.0 - new ChiSquaredDistribution(df).cumulativeProbability(x);
    return latentscov;
}

Example 17 with ChiSquaredDistribution

use of org.apache.commons.math3.distribution.ChiSquaredDistribution in project tetrad by cmu-phil.

the class SemIm method getPValue.

/**
 * @return the p-value for the model.
 */
public double getPValue() {
    double chiSquare = getChiSquare();
    int dof = semPm.getDof();
    if (dof <= 0) {
        return Double.NaN;
    } else if (chiSquare < 0) {
        return Double.NaN;
    } else {
        return 1.0 - new ChiSquaredDistribution(dof).cumulativeProbability(chiSquare);
    }
}

Example 18 with ChiSquaredDistribution

use of org.apache.commons.math3.distribution.ChiSquaredDistribution in project tetrad by cmu-phil.

the class TestStatUtils method testChiSqCdf.

@Test
public void testChiSqCdf() {
    ChiSquaredDistribution dist = new ChiSquaredDistribution(1);
    double log = Math.log(1000);
    double p = 1.0 - dist.cumulativeProbability(log);
    assertEquals(0.008, p, 0.001);
}

Example 19 with ChiSquaredDistribution

use of org.apache.commons.math3.distribution.ChiSquaredDistribution in project tetrad by cmu-phil.

the class IndTestMNLRLRT method isIndependent.

/**
 * @return true if the given independence question is judged true, false if not. The independence question is of the
 * form x _||_ y | z, z = <z1,...,zn>, where x, y, z1,...,zn are searchVariables in the list returned by
 * getVariableNames().
 */
public boolean isIndependent(Node x, Node y, List<Node> z) {
    int _x = nodesHash.get(x);
    int _y = nodesHash.get(y);
    int[] list0 = new int[z.size() + 1];
    int[] list1 = new int[z.size() + 1];
    int[] list2 = new int[z.size()];
    list0[0] = _x;
    list1[0] = _y;
    for (int i = 0; i < z.size(); i++) {
        int _z = nodesHash.get(z.get(i));
        list0[i + 1] = _z;
        list1[i + 1] = _z;
        list2[i] = _z;
    }
    double lik_0;
    double dof_0;
    double lik_1;
    double dof_1;
    lik_0 = likelihood.getLik(_y, list0) - likelihood.getLik(_y, list2);
    dof_0 = likelihood.getLik(_y, list0) - likelihood.getLik(_y, list2);
    lik_1 = likelihood.getLik(_x, list1) - likelihood.getLik(_x, list2);
    dof_1 = likelihood.getLik(_x, list1) - likelihood.getLik(_x, list2);
    if (dof_0 <= 0) {
        dof_0 = 1;
    }
    if (dof_1 <= 0) {
        dof_1 = 1;
    }
    double p_0 = 0;
    double p_1 = 0;
    try {
        p_0 = 1.0 - new ChiSquaredDistribution(dof_0).cumulativeProbability(2.0 * lik_0);
    } catch (Exception e) {
        e.printStackTrace();
    }
    try {
        p_1 = 1.0 - new ChiSquaredDistribution(dof_1).cumulativeProbability(2.0 * lik_1);
    } catch (Exception e) {
        e.printStackTrace();
    }
    this.pValue = Math.min(p_0, p_1);
    return this.pValue > alpha;
}

Example 20 with ChiSquaredDistribution

use of org.apache.commons.math3.distribution.ChiSquaredDistribution in project tetrad by cmu-phil.

the class IndTestMixedMultipleTTest method dependencePvalsLogit.

private double[] dependencePvalsLogit(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<Double> pValues = new ArrayList<>();
    int[] _rows = getNonMissingRows(x, y, z);
    logisticRegression.setRows(_rows);
    List<Node> yzDumList = new ArrayList<>();
    List<Node> yzList = new ArrayList<>();
    yzList.add(y);
    yzList.addAll(z);
    // List<Node> zList = new ArrayList<>();
    yzDumList.addAll(variablesPerNode.get(y));
    for (Node _z : z) {
        yzDumList.addAll(variablesPerNode.get(_z));
    // zList.addAll(variablesPerNode.get(_z));
    }
    // double[][] coeffsDep = new double[variablesPerNode.get(x).size()][];
    // DoubleMatrix2D coeffsNull = DoubleFactory2D.dense.make(zList.size(), variablesPerNode.get(x).size());
    // DoubleMatrix2D coeffsDep = DoubleFactory2D.dense.make(yzDumList.size()+1, variablesPerNode.get(x).size());
    double[] sumLnP = new double[yzList.size()];
    for (int i = 0; i < sumLnP.length; i++) sumLnP[i] = 0.0;
    for (int i = 0; i < variablesPerNode.get(x).size(); i++) {
        Node _x = variablesPerNode.get(x).get(i);
        LogisticRegression.Result result1 = logisticRegression.regress((DiscreteVariable) _x, yzDumList);
        int n = originalData.getNumRows();
        int k = yzDumList.size();
        // skip intercept at index 0
        int coefIndex = 1;
        for (int j = 0; j < yzList.size(); j++) {
            for (int dum = 0; dum < variablesPerNode.get(yzList.get(j)).size(); dum++) {
                double wald = Math.abs(result1.getCoefs()[coefIndex] / result1.getStdErrs()[coefIndex]);
                // double val = (1.0 - new NormalDistribution(0,1).cumulativeProbability(wald))*2;//two-tailed test
                // double val = 1-result1.getProbs()[i+1];
                // this is exactly the same test as the linear case
                double val = (1.0 - ProbUtils.tCdf(wald, n - k)) * 2;
                // System.out.println(_x.getName() + "\t" + yzDumList.get(coefIndex-1).getName() + "\t" + val + "\t" + (n-k));
                // if(val <= 0) System.out.println("Zero p-val t-test: p " + val + " stat " + wald + " k " + k + " n " + n);
                sumLnP[j] += Math.log(val);
                coefIndex++;
            }
        }
    }
    double[] pVec = new double[sumLnP.length];
    for (int i = 0; i < pVec.length; i++) {
        if (sumLnP[i] == Double.NEGATIVE_INFINITY)
            pVec[i] = 0.0;
        else {
            int df = 2 * variablesPerNode.get(x).size() * variablesPerNode.get(yzList.get(i)).size();
            pVec[i] = 1.0 - new ChiSquaredDistribution(df).cumulativeProbability(-2 * sumLnP[i]);
        }
    }
    return pVec;
}