@Test
public void test7() {
RandomUtil.getInstance().setSeed(29999483L);
List<Node> nodes = new ArrayList<>();
int numVars = 10;
for (int i = 0; i < numVars; i++) nodes.add(new ContinuousVariable("X" + (i + 1)));
Graph graph =
GraphUtils.randomGraphRandomForwardEdges(nodes, 0, numVars, 30, 15, 15, false, true);
GeneralizedSemPm pm = new GeneralizedSemPm(graph);
GeneralizedSemIm im = new GeneralizedSemIm(pm);
print(im);
DataSet data = im.simulateDataRecursive(1000, false);
GeneralizedSemEstimator estimator = new GeneralizedSemEstimator();
GeneralizedSemIm estIm = estimator.estimate(pm, data);
print(estIm);
print(estimator.getReport());
double aSquaredStar = estimator.getaSquaredStar();
assertEquals(0.67, aSquaredStar, 0.01);
}
@Test
public void test6() {
RandomUtil.getInstance().setSeed(29999483L);
int numVars = 5;
List<Node> nodes = new ArrayList<>();
for (int i = 0; i < numVars; i++) nodes.add(new ContinuousVariable("X" + (i + 1)));
Graph graph =
GraphUtils.randomGraphRandomForwardEdges(nodes, 0, numVars, 30, 15, 15, false, true);
SemPm spm = new SemPm(graph);
SemImInitializationParams params = new SemImInitializationParams();
params.setCoefRange(0.5, 1.5);
params.setVarRange(1, 3);
SemIm sim = new SemIm(spm, params);
GeneralizedSemPm pm = new GeneralizedSemPm(spm);
GeneralizedSemIm im = new GeneralizedSemIm(pm, sim);
DataSet data = im.simulateData(1000, false);
print(im);
GeneralizedSemEstimator estimator = new GeneralizedSemEstimator();
GeneralizedSemIm estIm = estimator.estimate(pm, data);
print(estIm);
print(estimator.getReport());
double aSquaredStar = estimator.getaSquaredStar();
assertEquals(0.59, aSquaredStar, 0.01);
}