Examples with Ricf edu.cmu.tetrad.sem.Ricf used on opensource projects

Example 6 with Ricf

use of edu.cmu.tetrad.sem.Ricf in project tetrad by cmu-phil.

the class TestRicf method test4.

@Test
public void test4() {
    List<Node> nodes1 = new ArrayList<>();
    for (int i1 = 0; i1 < 5; i1++) {
        nodes1.add(new ContinuousVariable("X" + (i1 + 1)));
    }
    Graph g1 = GraphUtils.randomGraph(nodes1, 0, 5, 0, 0, 0, false);
    List<Node> nodes = new ArrayList<>();
    for (int i = 0; i < 5; i++) {
        nodes.add(new ContinuousVariable("X" + (i + 1)));
    }
    Graph g2 = GraphUtils.randomGraph(nodes, 0, 5, 0, 0, 0, false);
    SemPm pm = new SemPm(g1);
    SemIm im = new SemIm(pm);
    DataSet dataset = im.simulateData(1000, false);
    ICovarianceMatrix cov = new CovarianceMatrix(dataset);
    new Ricf().ricf(new SemGraph(g1), cov, 0.001);
    new Ricf().ricf(new SemGraph(g2), cov, 0.001);
}

Example 7 with Ricf

use of edu.cmu.tetrad.sem.Ricf in project tetrad by cmu-phil.

the class TestRicf method testRicf1.

/**
 * <pre>
 * > ## A covariance matrix
 *
 * > "S" <- structure(c(2.93, -1.7, 0.76, -0.06,
 * +                   -1.7, 1.64, -0.78, 0.1,
 * +                    0.76, -0.78, 1.66, -0.78,
 * +                   -0.06, 0.1, -0.78, 0.81), .Dim = c(4,4),
 * +                  .Dimnames = list(c("y", "x", "z", "u"), c("y", "x",
 * "z", "u")))
 *
 * > ## The following should give the same fit.
 *
 * > ## Fit an ancestral graph y -> x <-> z <- u
 *
 * > fitAncestralGraph(ag1 <- makeAG(dag=DAG(x~y,z~u), bg = UG(~x*z)), S,
 * n=100)
 * $Shat
 * y          x          z          u
 * y  2.930000 -1.4344254  0.0000000  0.0000000
 * x -1.434425  1.3799680 -0.3430373  0.0000000
 * z  0.000000 -0.3430373  1.5943070 -0.7442518
 * u  0.000000  0.0000000 -0.7442518  0.8100000
 *
 * $Lhat
 * y x z    u
 * y 2.93 0 0 0.00
 * x 0.00 0 0 0.00
 * z 0.00 0 0 0.00
 * u 0.00 0 0 0.81
 *
 * $Bhat
 * y x z         u
 * y 1.000000 0 0 0.0000000
 * x 0.489565 1 0 0.0000000
 * z 0.000000 0 1 0.9188294
 * u 0.000000 0 0 1.0000000
 *
 * $Ohat
 * y          x          z u
 * y 0  0.0000000  0.0000000 0
 * x 0  0.6777235 -0.3430373 0
 * z 0 -0.3430373  0.9104666 0
 * u 0  0.0000000  0.0000000 0
 *
 * $dev
 * [1] 21.57711
 *
 * $df
 * [1] 3
 *
 * $it
 * [1] 4
 *
 * </pre>
 */
@Test
public void testRicf1() {
    String[] varNames = new String[] { "y", "x", "z", "u" };
    int numVars = varNames.length;
    double[] values = { 2.93, -1.7, 0.76, -0.06, -1.7, 1.64, -0.78, 0.1, 0.76, -0.78, 1.66, -0.78, -0.06, 0.1, -0.78, 0.81 };
    TetradMatrix m = matrix(values, numVars, numVars);
    ICovarianceMatrix s = new CovarianceMatrix(DataUtils.createContinuousVariables(varNames), m, 30);
    Graph mag = new EdgeListGraph();
    Node x = new ContinuousVariable("x");
    Node y = new ContinuousVariable("y");
    Node z = new ContinuousVariable("z");
    Node u = new ContinuousVariable("u");
    mag.addNode(x);
    mag.addNode(y);
    mag.addNode(z);
    mag.addNode(u);
    mag.addDirectedEdge(y, x);
    // mag.addDirectedEdge(u, x);
    mag.addBidirectedEdge(x, z);
    mag.addDirectedEdge(u, z);
    // int n = 100;
    double tol = 1e-06;
    Ricf ricf = new Ricf();
    Ricf.RicfResult ricfResult = ricf.ricf(new SemGraph(mag), s, tol);
    // Test shat at least.
    double[] shatValues = { 2.93, -1.434425, 0, 0, -1.434425, 1.379968, -0.343037, 0, 0, -0.343037, 1.594307, -0.744252, 0, 0, -0.744252, 0.81 };
    double norm = normdiff(ricfResult, shatValues, numVars, numVars);
    assertTrue(norm < 0.0001);
    // sHat should be the same for the bidirected model.
    mag.removeEdges(mag.getEdges());
    mag.addBidirectedEdge(y, x);
    // mag.addDirectedEdge(u, x);
    mag.addBidirectedEdge(x, z);
    mag.addBidirectedEdge(u, z);
    ricf.ricf(new SemGraph(mag), s, tol);
    norm = normdiff(ricfResult, shatValues, numVars, numVars);
    assertTrue(norm < 0.0001);
}