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 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;
}
/**
* Constructs a new standardized SEM IM from the freeParameters in the given SEM IM.
*
* @param im Stop asking me for these things! The given SEM IM!!!
* @param initialization CALCULATE_FROM_SEM if the initial values will be calculated from the
* given SEM IM; INITIALIZE_FROM_DATA if data will be simulated from the given SEM,
* standardized, and estimated.
*/
public StandardizedSemIm(SemIm im, Initialization initialization) {
this.semPm = new SemPm(im.getSemPm());
this.semGraph = new SemGraph(semPm.getGraph());
semGraph.setShowErrorTerms(true);
if (semGraph.existsDirectedCycle()) {
throw new IllegalArgumentException("The cyclic case is not handled.");
}
if (initialization == Initialization.CALCULATE_FROM_SEM) {
// This code calculates the new coefficients directly from the old ones.
edgeParameters = new HashMap<Edge, Double>();
List<Node> nodes = im.getVariableNodes();
TetradMatrix impliedCovar = im.getImplCovar(true);
for (Parameter parameter : im.getSemPm().getParameters()) {
if (parameter.getType() == ParamType.COEF) {
Node a = parameter.getNodeA();
Node b = parameter.getNodeB();
int aindex = nodes.indexOf(a);
int bindex = nodes.indexOf(b);
double vara = impliedCovar.get(aindex, aindex);
double stda = Math.sqrt(vara);
double varb = impliedCovar.get(bindex, bindex);
double stdb = Math.sqrt(varb);
double oldCoef = im.getEdgeCoef(a, b);
double newCoef = (stda / stdb) * oldCoef;
edgeParameters.put(Edges.directedEdge(a, b), newCoef);
} else if (parameter.getType() == ParamType.COVAR) {
Node a = parameter.getNodeA();
Node b = parameter.getNodeB();
Node exoa = semGraph.getExogenous(a);
Node exob = semGraph.getExogenous(b);
double covar = im.getErrCovar(a, b) / Math.sqrt(im.getErrVar(a) * im.getErrVar(b));
edgeParameters.put(Edges.bidirectedEdge(exoa, exob), covar);
}
}
} else {
// This code estimates the new coefficients from simulated data from the old model.
DataSet dataSet = im.simulateData(1000, false);
TetradMatrix _dataSet = dataSet.getDoubleData();
_dataSet = DataUtils.standardizeData(_dataSet);
DataSet dataSetStandardized = ColtDataSet.makeData(dataSet.getVariables(), _dataSet);
SemEstimator estimator = new SemEstimator(dataSetStandardized, im.getSemPm());
SemIm imStandardized = estimator.estimate();
edgeParameters = new HashMap<Edge, Double>();
for (Parameter parameter : imStandardized.getSemPm().getParameters()) {
if (parameter.getType() == ParamType.COEF) {
Node a = parameter.getNodeA();
Node b = parameter.getNodeB();
double coef = imStandardized.getEdgeCoef(a, b);
edgeParameters.put(Edges.directedEdge(a, b), coef);
} else if (parameter.getType() == ParamType.COVAR) {
Node a = parameter.getNodeA();
Node b = parameter.getNodeB();
Node exoa = semGraph.getExogenous(a);
Node exob = semGraph.getExogenous(b);
double covar = -im.getErrCovar(a, b) / Math.sqrt(im.getErrVar(a) * im.getErrVar(b));
edgeParameters.put(Edges.bidirectedEdge(exoa, exob), covar);
}
}
}
this.measuredNodes = Collections.unmodifiableList(semPm.getMeasuredNodes());
}