use of edu.pitt.csb.mgm.IndTestMultinomialLogisticRegressionWald in project tetrad by cmu-phil.
the class TestIndTestWaldLR method testIsIndependent.
@Test
public void testIsIndependent() {
RandomUtil.getInstance().setSeed(1450705713157L);
int numPassed = 0;
for (int i = 0; i < 10; i++) {
List<Node> nodes = new ArrayList<>();
for (int i1 = 0; i1 < 10; i1++) {
nodes.add(new ContinuousVariable("X" + (i1 + 1)));
}
Graph graph = GraphUtils.randomGraph(nodes, 0, 10, 3, 3, 3, false);
SemPm pm = new SemPm(graph);
SemIm im = new SemIm(pm);
DataSet data = im.simulateData(1000, false);
Discretizer discretizer = new Discretizer(data);
discretizer.setVariablesCopied(true);
discretizer.equalCounts(data.getVariable(0), 2);
discretizer.equalCounts(data.getVariable(3), 2);
data = discretizer.discretize();
Node x1 = data.getVariable("X1");
Node x2 = data.getVariable("X2");
Node x3 = data.getVariable("X3");
Node x4 = data.getVariable("X4");
Node x5 = data.getVariable("X5");
List<Node> cond = new ArrayList<>();
cond.add(x3);
cond.add(x4);
cond.add(x5);
Node x1Graph = graph.getNode(x1.getName());
Node x2Graph = graph.getNode(x2.getName());
List<Node> condGraph = new ArrayList<>();
for (Node node : cond) {
condGraph.add(graph.getNode(node.getName()));
}
// Using the Wald LR test since it's most up to date.
IndependenceTest test = new IndTestMultinomialLogisticRegressionWald(data, 0.05, false);
IndTestDSep dsep = new IndTestDSep(graph);
boolean correct = test.isIndependent(x2, x1, cond) == dsep.isIndependent(x2Graph, x1Graph, condGraph);
if (correct) {
numPassed++;
}
}
// System.out.println(RandomUtil.getInstance().getSeed());
// Do not always get all 10.
assertEquals(10, numPassed);
}
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