private void resetSemImEditor() {
java.util.List<SemEstimator> semEstimators = wrapper.getMultipleResultList();
if (semEstimators.size() == 1) {
SemEstimator estimatedSem = semEstimators.get(0);
SemImEditor editor = new SemImEditor(new SemImWrapper(estimatedSem.getEstimatedSem()));
panel.removeAll();
panel.add(editor, BorderLayout.CENTER);
panel.revalidate();
panel.repaint();
} else {
JTabbedPane tabs = new JTabbedPane();
for (int i = 0; i < semEstimators.size(); i++) {
SemEstimator estimatedSem = semEstimators.get(i);
SemImEditor editor = new SemImEditor(new SemImWrapper(estimatedSem.getEstimatedSem()));
JPanel _panel = new JPanel();
_panel.setLayout(new BorderLayout());
_panel.add(editor, BorderLayout.CENTER);
tabs.addTab(estimatedSem.getDataSet().getName(), _panel);
}
panel.removeAll();
panel.add(tabs);
panel.validate();
}
}
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;
}
}
public ImpliedCovarianceDataAllWrapper(SemEstimatorWrapper wrapper) {
// int sampleSize = params.getSampleSize();
// boolean latentDataSaved = params.isIncludeLatents();
SemEstimator semEstimator = wrapper.getSemEstimator();
SemIm semIm1 = semEstimator.getEstimatedSem();
if (semIm1 != null) {
TetradMatrix matrix2D = semIm1.getImplCovar(true);
int sampleSize = semIm1.getSampleSize();
List<Node> variables =
wrapper.getSemEstimator().getEstimatedSem().getSemPm().getVariableNodes();
CovarianceMatrix cov = new CovarianceMatrix(variables, matrix2D, sampleSize);
setDataModel(cov);
setSourceGraph(wrapper.getSemEstimator().getEstimatedSem().getSemPm().getGraph());
this.semIm = wrapper.getEstimatedSemIm();
}
LogDataUtils.logDataModelList(
"Data simulated from a linear structural equation model.", getDataModelList());
}
private String reportIfContinuous(Graph dag, DataSet dataSet) {
SemPm semPm = new SemPm(dag);
SemEstimator estimator = new SemEstimator(dataSet, semPm);
estimator.estimate();
SemIm semIm = estimator.getEstimatedSem();
NumberFormat nf = NumberFormat.getInstance();
nf.setMaximumFractionDigits(4);
StringBuilder buf = new StringBuilder();
buf.append("\nDegrees of Freedom = ")
.append(semPm.getDof())
.append("Chi-Square = ")
.append(nf.format(semIm.getChiSquare()))
.append("\nP Value = ")
.append(nf.format(semIm.getPValue()))
.append("\nBIC Score = ")
.append(nf.format(semIm.getBicScore()));
buf.append(
"\n\nThe above chi square test assumes that the maximum "
+ "likelihood function over the measured variables has been "
+ "maximized. Under that assumption, the null hypothesis for "
+ "the test is that the population covariance matrix over all "
+ "of the measured variables is equal to the estimated covariance "
+ "matrix over all of the measured variables written as a function "
+ "of the free model parameters--that is, the unfixed parameters "
+ "for each directed edge (the linear coefficient for that edge), "
+ "each exogenous variable (the variance for the error term for "
+ "that variable), and each bidirected edge (the covariance for "
+ "the exogenous variables it connects). The model is explained "
+ "in Bollen, Structural Equations with Latent Variable, 110. ");
return buf.toString();
}
private String compileReport() {
StringBuilder builder = new StringBuilder();
builder.append("Datset\tFrom\tTo\tType\tValue\tSE\tT\tP");
java.util.List<SemEstimator> estimators = wrapper.getMultipleResultList();
for (int i = 0; i < estimators.size(); i++) {
SemEstimator estimator = estimators.get(i);
SemIm estSem = estimator.getEstimatedSem();
String dataName = estimator.getDataSet().getName();
for (Parameter parameter : estSem.getFreeParameters()) {
builder.append("\n");
builder.append(dataName + "\t");
builder.append(parameter.getNodeA() + "\t");
builder.append(parameter.getNodeB() + "\t");
builder.append(typeString(parameter) + "\t");
builder.append(asString(paramValue(estSem, parameter)) + "\t");
/*
Maximum number of free parameters for which statistics will be
calculated. (Calculating standard errors is high complexity.) Set this to
zero to turn off statistics calculations (which can be problematic
sometimes).
*/
int maxFreeParamsForStatistics = 200;
builder.append(
asString(estSem.getStandardError(parameter, maxFreeParamsForStatistics)) + "\t");
builder.append(asString(estSem.getTValue(parameter, maxFreeParamsForStatistics)) + "\t");
builder.append(asString(estSem.getPValue(parameter, maxFreeParamsForStatistics)) + "\t");
}
List<Node> nodes = estSem.getVariableNodes();
for (int j = 0; j < nodes.size(); j++) {
Node node = nodes.get(j);
int n = estSem.getSampleSize();
int df = n - 1;
double mean = estSem.getMean(node);
double stdDev = estSem.getMeanStdDev(node);
double stdErr = stdDev / Math.sqrt(n);
double tValue = mean / stdErr;
double p = 2.0 * (1.0 - ProbUtils.tCdf(Math.abs(tValue), df));
builder.append("\n");
builder.append(dataName + "\t");
builder.append(nodes.get(j) + "\t");
builder.append(nodes.get(j) + "\t");
builder.append("Mean" + "\t");
builder.append(asString(mean) + "\t");
builder.append(asString(stdErr) + "\t");
builder.append(asString(tValue) + "\t");
builder.append(asString(p) + "\t");
}
}
return builder.toString();
}
