private double getPMulticluster(List<List<Integer>> clusters, int numRestarts) {
if (false) {
Graph g = new EdgeListGraph();
List<Node> latents = new ArrayList<Node>();
for (int i = 0; i < clusters.size(); i++) {
GraphNode latent = new GraphNode("L" + i);
latent.setNodeType(NodeType.LATENT);
latents.add(latent);
g.addNode(latent);
List<Node> cluster = variablesForIndices(clusters.get(i));
for (int j = 0; j < cluster.size(); j++) {
g.addNode(cluster.get(j));
g.addDirectedEdge(latent, cluster.get(j));
}
}
SemPm pm = new SemPm(g);
// pm.fixOneLoadingPerLatent();
SemOptimizerPowell semOptimizer = new SemOptimizerPowell();
semOptimizer.setNumRestarts(numRestarts);
SemEstimator est = new SemEstimator(cov, pm, semOptimizer);
est.setScoreType(SemIm.ScoreType.Fgls);
est.estimate();
return est.getEstimatedSem().getPValue();
} else {
double max = Double.NEGATIVE_INFINITY;
for (int i = 0; i < numRestarts; i++) {
Mimbuild2 mimbuild = new Mimbuild2();
List<List<Node>> _clusters = new ArrayList<List<Node>>();
for (List<Integer> _cluster : clusters) {
_clusters.add(variablesForIndices(_cluster));
}
List<String> names = new ArrayList<String>();
for (int j = 0; j < clusters.size(); j++) {
names.add("L" + j);
}
mimbuild.search(_clusters, names, cov);
double c = mimbuild.getpValue();
if (c > max) max = c;
}
return max;
}
}
private double getP(List<Integer> cluster, int numRestarts) {
if (true) {
Node latent = new GraphNode("L");
latent.setNodeType(NodeType.LATENT);
Graph g = new EdgeListGraph();
g.addNode(latent);
List<Node> measures = variablesForIndices(cluster);
for (Node node : measures) {
g.addNode(node);
g.addDirectedEdge(latent, node);
}
SemPm pm = new SemPm(g);
// pm.fixOneLoadingPerLatent();
SemOptimizerPowell semOptimizer = new SemOptimizerPowell();
semOptimizer.setNumRestarts(numRestarts);
SemEstimator est = new SemEstimator(cov, pm, semOptimizer);
est.setScoreType(SemIm.ScoreType.Fgls);
est.estimate();
return est.getEstimatedSem().getPValue();
} else {
double max = Double.NEGATIVE_INFINITY;
for (int i = 0; i < numRestarts; i++) {
Mimbuild2 mimbuild = new Mimbuild2();
List<List<Node>> clusters1 = new ArrayList<List<Node>>();
clusters1.add(variablesForIndices(new ArrayList<Integer>(cluster)));
List<String> names = new ArrayList<String>();
names.add("L");
mimbuild.search(clusters1, names, cov);
double c = mimbuild.getpValue();
if (c > max) max = c;
}
return max;
}
}
private Graph convertSearchGraphNodes(Set<Set<Node>> clusters) {
Graph graph = new EdgeListGraph(variables);
List<Node> latents = new ArrayList<Node>();
for (int i = 0; i < clusters.size(); i++) {
Node latent = new GraphNode(MimBuild.LATENT_PREFIX + (i + 1));
latent.setNodeType(NodeType.LATENT);
latents.add(latent);
graph.addNode(latent);
}
List<Set<Node>> _clusters = new ArrayList<Set<Node>>(clusters);
for (int i = 0; i < latents.size(); i++) {
for (Node node : _clusters.get(i)) {
if (!graph.containsNode(node)) graph.addNode(node);
graph.addDirectedEdge(latents.get(i), node);
}
}
return graph;
}
private double getClusterP2(List<Node> c) {
Graph g = new EdgeListGraph(c);
Node l = new GraphNode("L");
l.setNodeType(NodeType.LATENT);
g.addNode(l);
for (Node n : c) {
g.addDirectedEdge(l, n);
}
SemPm pm = new SemPm(g);
SemEstimator est;
if (dataModel instanceof DataSet) {
est = new SemEstimator((DataSet) dataModel, pm, new SemOptimizerEm());
} else {
est = new SemEstimator((CovarianceMatrix) dataModel, pm, new SemOptimizerEm());
}
SemIm estIm = est.estimate();
double pValue = estIm.getPValue();
return pValue == 1 ? Double.NaN : pValue;
}
@Test
public void test15() {
RandomUtil.getInstance().setSeed(29999483L);
try {
Node x1 = new GraphNode("X1");
Node x2 = new GraphNode("X2");
Node x3 = new GraphNode("X3");
Node x4 = new GraphNode("X4");
Graph g = new EdgeListGraphSingleConnections();
g.addNode(x1);
g.addNode(x2);
g.addNode(x3);
g.addNode(x4);
g.addDirectedEdge(x1, x2);
g.addDirectedEdge(x2, x3);
g.addDirectedEdge(x3, x4);
g.addDirectedEdge(x1, x4);
GeneralizedSemPm pm = new GeneralizedSemPm(g);
pm.setNodeExpression(x1, "E_X1");
pm.setNodeExpression(x2, "a1 * X1 + E_X2");
pm.setNodeExpression(x3, "a2 * X2 + E_X3");
pm.setNodeExpression(x4, "a3 * X1 + a4 * X3 ^ 2 + E_X4");
pm.setNodeExpression(pm.getErrorNode(x1), "Gamma(c1, c2)");
pm.setNodeExpression(pm.getErrorNode(x2), "ChiSquare(c3)");
pm.setNodeExpression(pm.getErrorNode(x3), "ChiSquare(c4)");
pm.setNodeExpression(pm.getErrorNode(x4), "ChiSquare(c5)");
pm.setParameterExpression("c1", "5");
pm.setParameterExpression("c2", "2");
pm.setParameterExpression("c3", "10");
pm.setParameterExpression("c4", "10");
pm.setParameterExpression("c5", "10");
pm.setParameterEstimationInitializationExpression("c1", "U(1, 5)");
pm.setParameterEstimationInitializationExpression("c2", "U(1, 5)");
pm.setParameterEstimationInitializationExpression("c3", "U(1, 5)");
pm.setParameterEstimationInitializationExpression("c4", "U(1, 5)");
pm.setParameterEstimationInitializationExpression("c5", "U(1, 5)");
GeneralizedSemIm im = new GeneralizedSemIm(pm);
print("True model: ");
print(im);
DataSet data = im.simulateDataRecursive(1000, false);
GeneralizedSemEstimator estimator = new GeneralizedSemEstimator();
GeneralizedSemIm estIm = estimator.estimate(pm, data);
print("\n\n\nEstimated model: ");
print(estIm);
print(estimator.getReport());
double aSquaredStar = estimator.getaSquaredStar();
assertEquals(.79, aSquaredStar, 0.01);
} catch (ParseException e) {
e.printStackTrace();
}
}
@Test
public void test14() {
RandomUtil.getInstance().setSeed(29999483L);
try {
Node x1 = new GraphNode("X1");
Node x2 = new GraphNode("X2");
Node x3 = new GraphNode("X3");
Node x4 = new GraphNode("X4");
Graph g = new EdgeListGraphSingleConnections();
g.addNode(x1);
g.addNode(x2);
g.addNode(x3);
g.addNode(x4);
g.addDirectedEdge(x1, x2);
g.addDirectedEdge(x2, x3);
g.addDirectedEdge(x3, x4);
g.addDirectedEdge(x1, x4);
GeneralizedSemPm pm = new GeneralizedSemPm(g);
pm.setNodeExpression(x1, "E_X1");
pm.setNodeExpression(x2, "a1 * tan(X1) + E_X2");
pm.setNodeExpression(x3, "a2 * tan(X2) + E_X3");
pm.setNodeExpression(x4, "a3 * tan(X1) + a4 * tan(X3) ^ 2 + E_X4");
pm.setNodeExpression(pm.getErrorNode(x1), "N(0, c1)");
pm.setNodeExpression(pm.getErrorNode(x2), "N(0, c2)");
pm.setNodeExpression(pm.getErrorNode(x3), "N(0, c3)");
pm.setNodeExpression(pm.getErrorNode(x4), "N(0, c4)");
pm.setParameterExpression("a1", "1");
pm.setParameterExpression("a2", "1");
pm.setParameterExpression("a3", "1");
pm.setParameterExpression("a4", "1");
pm.setParameterExpression("c1", "4");
pm.setParameterExpression("c2", "4");
pm.setParameterExpression("c3", "4");
pm.setParameterExpression("c4", "4");
GeneralizedSemIm im = new GeneralizedSemIm(pm);
print("True model: ");
print(im);
DataSet data = im.simulateDataRecursive(1000, false);
GeneralizedSemIm imInit = new GeneralizedSemIm(pm);
imInit.setParameterValue("c1", 8);
imInit.setParameterValue("c2", 8);
imInit.setParameterValue("c3", 8);
imInit.setParameterValue("c4", 8);
GeneralizedSemEstimator estimator = new GeneralizedSemEstimator();
GeneralizedSemIm estIm = estimator.estimate(pm, data);
print("\n\n\nEstimated model: ");
print(estIm);
print(estimator.getReport());
double aSquaredStar = estimator.getaSquaredStar();
assertEquals(71.25, aSquaredStar, 0.01);
} catch (ParseException e) {
e.printStackTrace();
}
}