private boolean isIndependentMultinomialLogisticRegression(Node x, Node y, List<Node> z) {
double p = dependencePvalsLogit(x, y, z)[0];
boolean indep = p > alpha;
// 0 corresponds to y
this.lastP = p;
if (indep) {
TetradLogger.getInstance()
.log("independencies", SearchLogUtils.independenceFactMsg(x, y, z, p));
} else {
TetradLogger.getInstance().log("dependencies", SearchLogUtils.dependenceFactMsg(x, y, z, p));
}
return indep;
}
private boolean isIndependentRegression(Node x, Node y, List<Node> z) {
double p = dependencePvalsLinear(x, y, z)[0];
// result.getP()[1];
this.lastP = p;
boolean indep = p > alpha;
if (verbose) {
if (indep) {
TetradLogger.getInstance()
.log("independencies", SearchLogUtils.independenceFactMsg(x, y, z, p));
} else {
TetradLogger.getInstance()
.log("dependencies", SearchLogUtils.dependenceFactMsg(x, y, z, p));
}
}
return indep;
}
/** Tests to see if d separation facts are symmetric. */
public void testDSeparation() {
EdgeListGraphSingleConnections graph =
new EdgeListGraphSingleConnections(
new Dag(GraphUtils.randomGraph(7, 0, 7, 30, 15, 15, true)));
System.out.println(graph);
List<Node> nodes = graph.getNodes();
int depth = -1;
for (int i = 0; i < nodes.size(); i++) {
for (int j = i + 1; j < nodes.size(); j++) {
Node x = nodes.get(i);
Node y = nodes.get(j);
List<Node> theRest = new ArrayList<Node>(nodes);
theRest.remove(x);
theRest.remove(y);
DepthChoiceGenerator gen = new DepthChoiceGenerator(theRest.size(), depth);
int[] choice;
while ((choice = gen.next()) != null) {
List<Node> z = new LinkedList<Node>();
for (int k = 0; k < choice.length; k++) {
z.add(theRest.get(choice[k]));
}
if (graph.isDSeparatedFrom(x, y, z) != graph.isDSeparatedFrom(y, x, z)) {
fail(
SearchLogUtils.independenceFact(x, y, z)
+ " should have same d-sep result as "
+ SearchLogUtils.independenceFact(y, x, z));
}
}
}
}
}