private double localScoreB(Node node, List<Node> parents) {
double score = 0.0;
double maxScore = Double.NEGATIVE_INFINITY;
Node _target = node;
List<Node> _regressors = parents;
Node target = getVariable(variables, _target.getName());
List<Node> regressors = new ArrayList<Node>();
for (Node _regressor : _regressors) {
Node variable = getVariable(variables, _regressor.getName());
regressors.add(variable);
}
DATASET:
for (int m = 0; m < dataSets.size(); m++) {
RegressionResult result = regressions.get(m).regress(target, regressors);
TetradVector residualsSingleDataset = result.getResiduals();
DoubleArrayList _residualsSingleDataset =
new DoubleArrayList(residualsSingleDataset.toArray());
for (int h = 0; h < residualsSingleDataset.size(); h++) {
if (Double.isNaN(residualsSingleDataset.get(h))) {
continue DATASET;
}
}
double mean = Descriptive.mean(_residualsSingleDataset);
double std =
Descriptive.standardDeviation(
Descriptive.variance(
_residualsSingleDataset.size(),
Descriptive.sum(_residualsSingleDataset),
Descriptive.sumOfSquares(_residualsSingleDataset)));
for (int i2 = 0; i2 < _residualsSingleDataset.size(); i2++) {
_residualsSingleDataset.set(i2, (_residualsSingleDataset.get(i2) - mean) / std);
}
double[] _f = new double[_residualsSingleDataset.size()];
for (int k = 0; k < _residualsSingleDataset.size(); k++) {
_f[k] = _residualsSingleDataset.get(k);
}
DoubleArrayList f = new DoubleArrayList(_f);
for (int k = 0; k < f.size(); k++) {
f.set(k, Math.abs(f.get(k)));
}
double _mean = Descriptive.mean(f);
double diff = _mean - Math.sqrt(2.0 / Math.PI);
score += diff * diff;
if (score > maxScore) {
maxScore = score;
}
}
double avg = score / dataSets.size();
return avg;
}
/**
* 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());
}