private static Element makeVariables(SemIm semIm) {
Element variablesElement = new Element(SemXmlConstants.SEM_VARIABLES);
Element variable;
Node measuredNode, latentNode;
for (Node node1 : semIm.getSemPm().getMeasuredNodes()) {
measuredNode = node1;
variable = new Element(SemXmlConstants.CONTINUOUS_VARIABLE);
variable.addAttribute(new Attribute(SemXmlConstants.NAME, measuredNode.getName()));
variable.addAttribute(new Attribute(SemXmlConstants.IS_LATENT, "no"));
variable.addAttribute(
new Attribute(SemXmlConstants.MEAN, Double.toString(semIm.getMean(measuredNode))));
variable.addAttribute(
new Attribute(SemXmlConstants.X, Integer.toString(measuredNode.getCenterX())));
variable.addAttribute(
new Attribute(SemXmlConstants.Y, Integer.toString(measuredNode.getCenterY())));
variablesElement.appendChild(variable);
}
for (Node node : semIm.getSemPm().getLatentNodes()) {
latentNode = node;
variable = new Element(SemXmlConstants.CONTINUOUS_VARIABLE);
variable.addAttribute(new Attribute(SemXmlConstants.NAME, latentNode.getName()));
variable.addAttribute(new Attribute(SemXmlConstants.IS_LATENT, "yes"));
variable.addAttribute(
new Attribute(SemXmlConstants.MEAN, Double.toString(semIm.getMean(latentNode))));
variable.addAttribute(
new Attribute(SemXmlConstants.X, Integer.toString(latentNode.getCenterX())));
variable.addAttribute(
new Attribute(SemXmlConstants.Y, Integer.toString(latentNode.getCenterY())));
variablesElement.appendChild(variable);
}
return variablesElement;
}
@Test
public void test2() {
RandomUtil.getInstance().setSeed(2999983L);
int sampleSize = 1000;
List<Node> variableNodes = new ArrayList<>();
ContinuousVariable x1 = new ContinuousVariable("X1");
ContinuousVariable x2 = new ContinuousVariable("X2");
ContinuousVariable x3 = new ContinuousVariable("X3");
ContinuousVariable x4 = new ContinuousVariable("X4");
ContinuousVariable x5 = new ContinuousVariable("X5");
variableNodes.add(x1);
variableNodes.add(x2);
variableNodes.add(x3);
variableNodes.add(x4);
variableNodes.add(x5);
Graph _graph = new EdgeListGraph(variableNodes);
SemGraph graph = new SemGraph(_graph);
graph.addDirectedEdge(x1, x3);
graph.addDirectedEdge(x2, x3);
graph.addDirectedEdge(x3, x4);
graph.addDirectedEdge(x2, x4);
graph.addDirectedEdge(x4, x5);
graph.addDirectedEdge(x2, x5);
SemPm semPm = new SemPm(graph);
SemIm semIm = new SemIm(semPm);
DataSet dataSet = semIm.simulateData(sampleSize, false);
print(semPm);
GeneralizedSemPm _semPm = new GeneralizedSemPm(semPm);
GeneralizedSemIm _semIm = new GeneralizedSemIm(_semPm, semIm);
DataSet _dataSet = _semIm.simulateDataMinimizeSurface(sampleSize, false);
print(_semPm);
// System.out.println(_dataSet);
for (int j = 0; j < dataSet.getNumColumns(); j++) {
double[] col = dataSet.getDoubleData().getColumn(j).toArray();
double[] _col = _dataSet.getDoubleData().getColumn(j).toArray();
double mean = StatUtils.mean(col);
double _mean = StatUtils.mean(_col);
double variance = StatUtils.variance(col);
double _variance = StatUtils.variance(_col);
assertEquals(mean, _mean, 0.3);
assertEquals(1.0, variance / _variance, .2);
}
}
public int hashCode() {
int hashCode = 37;
hashCode = 19 * hashCode + semIm.hashCode();
hashCode = 19 * hashCode + proposition.hashCode();
hashCode = 19 * hashCode + manipulation.hashCode();
return hashCode;
}
@Test
public void test8() {
RandomUtil.getInstance().setSeed(29999483L);
Node x = new GraphNode("X");
Node y = new GraphNode("Y");
List<Node> nodes = new ArrayList<>();
nodes.add(x);
nodes.add(y);
Graph graph = new EdgeListGraphSingleConnections(nodes);
graph.addDirectedEdge(x, y);
SemPm spm = new SemPm(graph);
SemIm sim = new SemIm(spm);
sim.setEdgeCoef(x, y, 20);
sim.setErrVar(x, 1);
sim.setErrVar(y, 1);
GeneralizedSemPm pm = new GeneralizedSemPm(spm);
GeneralizedSemIm im = new GeneralizedSemIm(pm, sim);
print(im);
try {
pm.setParameterEstimationInitializationExpression("b1", "U(10, 30)");
pm.setParameterEstimationInitializationExpression("T1", "U(.1, 3)");
pm.setParameterEstimationInitializationExpression("T2", "U(.1, 3)");
} catch (ParseException e) {
e.printStackTrace();
}
DataSet data = im.simulateDataRecursive(1000, false);
GeneralizedSemEstimator estimator = new GeneralizedSemEstimator();
GeneralizedSemIm estIm = estimator.estimate(pm, data);
print(estIm);
// System.out.println(estimator.getReport());
double aSquaredStar = estimator.getaSquaredStar();
assertEquals(0.69, aSquaredStar, 0.01);
}
private static Element makeMarginalErrorDistribution(SemIm semIm) {
Element marginalErrorElement = new Element(SemXmlConstants.MARGINAL_ERROR_DISTRIBUTION);
Element normal;
SemGraph semGraph = semIm.getSemPm().getGraph();
semGraph.setShowErrorTerms(true);
for (Node node : getExogenousNodes(semGraph)) {
normal = new Element(SemXmlConstants.NORMAL);
normal.addAttribute(new Attribute(SemXmlConstants.VARIABLE, node.getName()));
normal.addAttribute(new Attribute(SemXmlConstants.MEAN, "0.0"));
normal.addAttribute(
new Attribute(
SemXmlConstants.VARIANCE, Double.toString(semIm.getParamValue(node, node))));
marginalErrorElement.appendChild(normal);
}
return marginalErrorElement;
}
private double paramValue(SemIm im, Parameter parameter) {
double paramValue = im.getParamValue(parameter);
if (parameter.getType() == ParamType.VAR) {
paramValue = Math.sqrt(paramValue);
}
return paramValue;
}
private static Element makeEdges(SemIm semIm) {
Element edgesElement = new Element(SemXmlConstants.EDGES);
Parameter param;
Element edge;
for (Parameter parameter : semIm.getSemPm().getParameters()) {
param = parameter;
if (param.getType() == ParamType.COEF) {
edge = new Element(SemXmlConstants.EDGE);
edge.addAttribute(new Attribute(SemXmlConstants.CAUSE_NODE, param.getNodeA().getName()));
edge.addAttribute(new Attribute(SemXmlConstants.EFFECT_NODE, param.getNodeB().getName()));
edge.addAttribute(
new Attribute(SemXmlConstants.VALUE, Double.toString(semIm.getParamValue(param))));
edge.addAttribute(
new Attribute(SemXmlConstants.FIXED, Boolean.valueOf(param.isFixed()).toString()));
edgesElement.appendChild(edge);
}
}
return edgesElement;
}
/** @return the variable for which there is evidence. */
public List<Node> getNodesInEvidence() {
List<Node> nodes = semIm.getVariableNodes();
List<Node> nodesInEvidence = new ArrayList<Node>();
for (int i = 0; i < nodes.size(); i++) {
if (!Double.isNaN(proposition.getValue(i))) {
nodesInEvidence.add(nodes.get(i));
}
}
return nodesInEvidence;
}
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;
}
public int getNodeIndex(Node node) {
List<Node> nodes = semIm.getSemPm().getVariableNodes();
for (int i = 0; i < nodes.size(); i++) {
Node _node = nodes.get(i);
if (node == _node) {
return i;
}
}
return -1;
}
public int getNodeIndex(String nodeName) {
List<Node> nodes = semIm.getSemPm().getVariableNodes();
for (int i = 0; i < nodes.size(); i++) {
Node node = nodes.get(i);
if (node.getName().equals(nodeName)) {
return i;
}
}
return -1;
}
@Test
public void test5() {
RandomUtil.getInstance().setSeed(29999483L);
List<Node> nodes = new ArrayList<>();
for (int i1 = 0; i1 < 5; i1++) {
nodes.add(new ContinuousVariable("X" + (i1 + 1)));
}
Graph graph = new Dag(GraphUtils.randomGraph(nodes, 0, 5, 30, 15, 15, false));
SemPm semPm = new SemPm(graph);
SemIm semIm = new SemIm(semPm);
semIm.simulateDataReducedForm(1000, false);
GeneralizedSemPm pm = new GeneralizedSemPm(semPm);
GeneralizedSemIm im = new GeneralizedSemIm(pm, semIm);
TetradVector e = new TetradVector(5);
for (int i = 0; i < e.size(); i++) {
e.set(i, RandomUtil.getInstance().nextNormal(0, 1));
}
TetradVector record1 = semIm.simulateOneRecord(e);
TetradVector record2 = im.simulateOneRecord(e);
print("XXX1" + e);
print("XXX2" + record1);
print("XXX3" + record2);
for (int i = 0; i < record1.size(); i++) {
assertEquals(record1.get(i), record2.get(i), 1e-10);
}
}
public String toString() {
List<Node> nodes = semIm.getVariableNodes();
StringBuilder buf = new StringBuilder();
buf.append("\nEvidence: ");
for (int i = 0; i < nodes.size(); i++) {
Node node = nodes.get(i);
buf.append("\n").append(node).append(" = ").append(proposition.getValue(i));
if (isManipulated(i)) {
buf.append("\tManipulated");
}
}
return buf.toString();
}
private static Element makeJointErrorDistribution(SemIm semIm) {
Element jointErrorElement = new Element(SemXmlConstants.JOINT_ERROR_DISTRIBUTION);
Element normal;
Parameter param;
for (Parameter parameter : semIm.getSemPm().getParameters()) {
param = parameter;
if (param.getType() == ParamType.COVAR) {
normal = new Element(SemXmlConstants.NORMAL);
normal.addAttribute(new Attribute(SemXmlConstants.NODE_1, param.getNodeA().getName()));
normal.addAttribute(new Attribute(SemXmlConstants.NODE_2, param.getNodeB().getName()));
normal.addAttribute(
new Attribute(SemXmlConstants.COVARIANCE, Double.toString(param.getStartingValue())));
jointErrorElement.appendChild(normal);
}
}
return jointErrorElement;
}
public Node getNode(int nodeIndex) {
return semIm.getVariableNodes().get(nodeIndex);
}
public int getNumNodes() {
return semIm.getVariableNodes().size();
}
/** Generates a simple exemplar of this class to test serialization. */
public static SemEvidence serializableInstance() {
return new SemEvidence(SemIm.serializableInstance());
}
protected SemIm estimateCoeffs(SemIm semIm) {
// System.out.print("\n****************\nCalling 2SLS... ");
SemGraph semGraph = semIm.getSemPm().getGraph();
// Get list of fixed measurements that will be kept fixed, and the
// respective latent variables that are their parents.
// "X" variables are exogenous, while "Y" variables are endogenous.
List<Node> ly = new LinkedList<Node>();
List<Node> lx = new LinkedList<Node>();
List<Node> my1 = new LinkedList<Node>();
List<Node> mx1 = new LinkedList<Node>();
List<Node> observed = new LinkedList<Node>();
for (Node nodeA : semGraph.getNodes()) {
if (nodeA.getNodeType() == NodeType.ERROR) {
continue;
}
if (nodeA.getNodeType() == NodeType.LATENT) {
if (semGraph.getParents(nodeA).size() == 0) {
lx.add(nodeA);
} else {
ly.add(nodeA);
}
} else {
observed.add(nodeA);
}
}
setFixedNodes(semGraph, mx1, my1);
// ------------------------------------------------------------------
// Estimate freeParameters for the latent/latent edges
for (Node current : ly) {
if (nodeName != null && !nodeName.equals(current.getName())) {
continue;
}
// Build Z, the matrix containing the data for the fixed measurements
// associated with the parents of the getModel (endogenous) latent node
List<Node> endo_parents_m = new LinkedList<Node>();
List<Node> exo_parents_m = new LinkedList<Node>();
List<Node> endo_parents = new LinkedList<Node>();
List<Node> exo_parents = new LinkedList<Node>();
Iterator<Node> it_p = semGraph.getParents(current).iterator();
lNames = new String[lx.size() + ly.size()];
while (it_p.hasNext()) {
Node node = it_p.next();
if (node.getNodeType() == NodeType.ERROR) {
continue;
}
if (lx.contains(node)) {
int position = lx.indexOf(node);
exo_parents_m.add(mx1.get(position));
exo_parents.add(node);
} else {
int position = ly.indexOf(node);
endo_parents_m.add(my1.get(position));
endo_parents.add(node);
}
}
Object endp_a_m[] = endo_parents_m.toArray();
Object exop_a_m[] = exo_parents_m.toArray();
Object endp_a[] = endo_parents.toArray();
Object exop_a[] = exo_parents.toArray();
int n = dataSet.getNumRows(), c = endp_a_m.length + exop_a_m.length;
if (c == 0) {
continue;
}
double Z[][] = new double[n][c];
int count = 0;
for (int i = 0; i < endp_a_m.length; i++) {
Node node = (Node) endp_a_m[i];
String name = node.getName();
Node variable = dataSet.getVariable(name);
int colIndex = dataSet.getVariables().indexOf(variable);
// Column column = dataSet.getColumnObject(variable);
// double column_data[] = (double[]) column.getRawData();
for (int j = 0; j < n; j++) {
// Z[j][i] = column_data[j];
Z[j][i] = dataSet.getDouble(j, colIndex);
}
lNames[count++] = (endo_parents.get(i)).getName();
}
for (int i = 0; i < exop_a_m.length; i++) {
Node node = (Node) exop_a_m[i];
String name = node.getName();
Node variable = dataSet.getVariable(name);
int colIndex = dataSet.getVariables().indexOf(variable);
// Column column = dataSet.getColumnObject(variable);
// double column_data[] = (double[]) column.getRawData();
for (int j = 0; j < n; j++) {
// Z[j][endp_a_m.length + i] = column_data[j];
Z[j][endp_a_m.length + i] = dataSet.getDouble(j, colIndex);
}
lNames[count++] = exo_parents.get(i).getName();
}
// Build V, the matrix containing the data for the nonfixed measurements
// associated with the parents of the getModel (endogenous) latent node
endo_parents_m = new LinkedList<Node>();
exo_parents_m = new LinkedList<Node>();
it_p = semGraph.getParents(current).iterator();
while (it_p.hasNext()) {
Node node = it_p.next();
if (node.getNodeType() == NodeType.ERROR) {
continue;
}
List<Node> other_measures = new LinkedList<Node>();
for (Node next : semGraph.getChildren(node)) {
if (next.getNodeType() == NodeType.MEASURED) {
other_measures.add(next);
}
}
if (lx.contains(node)) {
int position = lx.indexOf(node);
other_measures.remove(mx1.get(position));
exo_parents_m.addAll(other_measures);
} else {
int position = ly.indexOf(node);
other_measures.remove(my1.get(position));
endo_parents_m.addAll(other_measures);
}
}
endp_a_m = endo_parents_m.toArray();
exop_a_m = exo_parents_m.toArray();
n = dataSet.getNumRows();
c = endp_a_m.length + exop_a_m.length;
double V[][] = new double[n][c];
if (c == 0) {
continue;
}
for (int i = 0; i < endp_a_m.length; i++) {
Node node = ((Node) endp_a_m[i]);
String name = node.getName();
Node variable = dataSet.getVariable(name);
int colIndex = dataSet.getVariables().indexOf(variable);
// Column column = dataSet.getColumnObject(variable);
// double column_data[] = (double[]) column.getRawData();
for (int j = 0; j < n; j++) {
// V[j][i] = column_data[j];
V[j][i] = dataSet.getDouble(j, colIndex);
}
}
for (int i = 0; i < exop_a_m.length; i++) {
Node node = (Node) exop_a_m[i];
String name = node.getName();
Node variable = dataSet.getVariable(name);
int colIndex = dataSet.getVariables().indexOf(variable);
// Column column = dataSet.getColumnObject(variable);
// double column_data[] = (double[]) column.getRawData();
for (int j = 0; j < n; j++) {
// V[j][endp_a_m.length + i] = column_data[j];
V[j][endp_a_m.length + i] = dataSet.getDouble(j, colIndex);
}
}
double yi[] = new double[n];
if (lx.contains(current)) {
int position = lx.indexOf(current);
Node node = mx1.get(position);
String name = node.getName();
Node variable = dataSet.getVariable(name);
int colIndex = dataSet.getVariables().indexOf(variable);
// Column column = dataSet.getColumnObject(variable);
//
// System.arraycopy(column.getRawData(), 0, yi, 0, n);
for (int i = 0; i < n; i++) {
yi[i] = dataSet.getDouble(i, colIndex);
}
} else {
int position = ly.indexOf(current);
Node node = my1.get(position);
String name = node.getName();
Node variable = dataSet.getVariable(name);
int colIndex = dataSet.getVariables().indexOf(variable);
// System.arraycopy(dataSet.getColumnObject(variable).getRawData(), 0, yi, 0,
// n);
for (int i = 0; i < n; i++) {
yi[i] = dataSet.getDouble(i, colIndex);
}
}
// Build Z_hat
double Z_hat[][] =
MatrixUtils.product(
V,
MatrixUtils.product(
MatrixUtils.inverse(MatrixUtils.product(MatrixUtils.transpose(V), V)),
MatrixUtils.product(MatrixUtils.transpose(V), Z)));
A_hat =
MatrixUtils.product(
MatrixUtils.inverse(MatrixUtils.product(MatrixUtils.transpose(Z_hat), Z_hat)),
MatrixUtils.product(MatrixUtils.transpose(Z_hat), yi));
// Set the edge for the fixed measurement
int position = ly.indexOf(current);
semIm.setParamValue(current, my1.get(position), 1.);
// Set the edge for the latents
for (int i = 0; i < endp_a.length; i++) {
semIm.setParamValue((Node) endp_a[i], current, A_hat[i]);
}
for (int i = 0; i < exop_a.length; i++) {
semIm.setParamValue((Node) exop_a[i], current, A_hat[endp_a.length + i]);
}
if (nodeName != null && nodeName.equals(current.getName())) {
computeAsymptLatentCovar(yi, A_hat, Z, Z_hat, dataSet.getNumRows());
break;
}
}
// ------------------------------------------------------------------
// Estimate freeParameters of the measurement model
// Set the edges of the fixed measurements of exogenous
for (Node current : lx) {
int position = lx.indexOf(current);
semIm.setParamValue(current, mx1.get(position), 1.);
}
for (Node current : observed) {
if (nodeName != null && !nodeName.equals(current.getName())) {
continue;
}
if (mx1.contains(current) || my1.contains(current)) {
continue;
}
// First, get the parent of this observed
Node current_latent = null;
for (Node node : semGraph.getParents(current)) {
if (node.getNodeType() == NodeType.ERROR) {
continue;
}
current_latent = node;
}
Iterator<Node> children = semGraph.getChildren(current_latent).iterator();
List<Node> other_measures = new LinkedList<Node>();
Node fixed_measurement;
while (children.hasNext()) {
Node next = children.next();
if ((next.getNodeType() == NodeType.MEASURED) && next != current) {
other_measures.add(next);
}
}
if (lx.contains(current_latent)) {
int position = lx.indexOf(current_latent);
other_measures.remove(mx1.get(position));
fixed_measurement = mx1.get(position);
} else {
int position = ly.indexOf(current_latent);
other_measures.remove(my1.get(position));
fixed_measurement = my1.get(position);
}
// Regress other_measures over the fixed measurement x1 (y1) correspondent
// to the measurement variable that is being evaluated
int n = dataSet.getNumRows(), c = other_measures.size();
if (c == 0) {
continue;
}
double Z[][] = new double[n][c];
for (int i = 0; i < c; i++) {
Node variable = dataSet.getVariable((other_measures.get(i)).getName());
int varIndex = dataSet.getVariables().indexOf(variable);
// Column column = dataSet.getColumnObject(variable);
// double column_data[] = (double[]) column.getRawData();
for (int j = 0; j < n; j++) {
// Z[j][i] = column_data[j];
Z[j][i] = dataSet.getDouble(varIndex, j);
}
}
// Build C, the column matrix containing the data for the fixed
// measurement associated with the only latent parent of the getModel
// observed node (as assumed by the structure of our measurement model).
Node variable = dataSet.getVariable(fixed_measurement.getName());
int colIndex = dataSet.getVariables().indexOf(variable);
// Column column = dataSet.getColumnObject(variable);
// double C[] = (double[]) column.getRawData();
double[] C = new double[dataSet.getNumRows()];
for (int i = 0; i < dataSet.getNumRows(); i++) {
C[i] = dataSet.getDouble(colIndex, i);
}
// Build V, the matrix containing the data for the other measurements
// associated with the parents of the (latent) parent of getModel
// observed node. The only difference with respect to the estimation
// of the within-latent coefficients is that here we only include
// the other measurements attached to the parent of the getModel node,
// assuming that the error term of the getModel node is independent
// of the error term of the others and that each measurement is
// taken with respect to only one latent.
n = dataSet.getNumRows();
c = other_measures.size();
double V[][] = new double[n][c];
for (int i = 0; i < c; i++) {
Node variable2 = dataSet.getVariable((other_measures.get(i)).getName());
int var2index = dataSet.getVariables().indexOf(variable2);
// Column column = dataSet.getColumnObject(variable2);
// double column_data[] = (double[]) column.getRawData();
for (int j = 0; j < n; j++) {
// V[j][i] = column_data[j];
V[j][i] = dataSet.getDouble(j, var2index);
}
}
double yi[] = new double[n];
Node variable3 = dataSet.getVariable((current).getName());
int var3Index = dataSet.getVariables().indexOf(variable3);
for (int i = 0; i < n; i++) {
yi[i] = dataSet.getDouble(i, var3Index);
}
// Object rawData = dataSet.getColumnObject(variable3).getRawData();
// System.arraycopy(rawData, 0, yi, 0, n);
double C_hat[] =
MatrixUtils.product(
V,
MatrixUtils.product(
MatrixUtils.inverse(MatrixUtils.product(MatrixUtils.transpose(V), V)),
MatrixUtils.product(MatrixUtils.transpose(V), C)));
double A_hat =
MatrixUtils.innerProduct(
MatrixUtils.scalarProduct(1. / MatrixUtils.innerProduct(C_hat, C_hat), C_hat), yi);
// Set the edge for the getModel measurement
semIm.setParamValue(current_latent, current, A_hat);
}
return semIm;
}
/**
* 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());
}
/** Generates a simple exemplar of this class to test serialization. */
public static StandardizedSemIm serializableInstance() {
return new StandardizedSemIm(SemIm.serializableInstance());
}
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();
}
