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 void reestimate() {
SemOptimizer optimizer;
String type = wrapper.getSemOptimizerType();
if ("Regression".equals(type)) {
optimizer = new SemOptimizerRegression();
} else if ("EM".equals(type)) {
optimizer = new SemOptimizerEm();
} else if ("Powell".equals(type)) {
optimizer = new SemOptimizerPowell();
} else if ("Random Search".equals(type)) {
optimizer = new SemOptimizerScattershot();
} else if ("RICF".equals(type)) {
optimizer = new SemOptimizerRicf();
} else if ("Powell".equals(type)) {
optimizer = new SemOptimizerPowell();
} else {
throw new IllegalArgumentException("Unexpected optimizer " + "type: " + type);
}
int numRestarts = wrapper.getNumRestarts();
optimizer.setNumRestarts(numRestarts);
java.util.List<SemEstimator> estimators = wrapper.getMultipleResultList();
java.util.List<SemEstimator> newEstimators = new ArrayList<>();
for (SemEstimator estimator : estimators) {
SemPm semPm = estimator.getSemPm();
DataSet dataSet = estimator.getDataSet();
ICovarianceMatrix covMatrix = estimator.getCovMatrix();
SemEstimator newEstimator;
if (dataSet != null) {
newEstimator = new SemEstimator(dataSet, semPm, optimizer);
newEstimator.setNumRestarts(numRestarts);
newEstimator.setScoreType(wrapper.getScoreType());
} else if (covMatrix != null) {
newEstimator = new SemEstimator(covMatrix, semPm, optimizer);
newEstimator.setNumRestarts(numRestarts);
newEstimator.setScoreType(wrapper.getScoreType());
} else {
throw new IllegalStateException(
"Only continuous "
+ "rectangular data sets and covariance matrices "
+ "can be processed.");
}
newEstimator.estimate();
newEstimators.add(newEstimator);
}
wrapper.setSemEstimator(newEstimators.get(0));
wrapper.setMultipleResultList(newEstimators);
resetSemImEditor();
}
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();
}
