private boolean paramInBounds(Edge edge, double newValue) {
edgeParameters.put(edge, newValue);
Map<Node, Double> errorVariances = new HashMap<Node, Double>();
for (Node node : semPm.getVariableNodes()) {
Node error = semGraph.getExogenous(node);
double d2 = calculateErrorVarianceFromParams(error);
if (Double.isNaN(d2)) {
return false;
}
errorVariances.put(error, d2);
}
if (!MatrixUtils.isPositiveDefinite(errCovar(errorVariances))) {
return false;
}
return true;
}
/**
* Computes the implied covariance matrices of the Sem. There are two: <code>implCovar </code>
* contains the covariances of all the variables and <code>implCovarMeas</code> contains
* covariance for the measured variables only.
*/
private void computeImpliedCovar() {
TetradMatrix edgeCoefT = edgeCoef().transpose();
// Note. Since the sizes of the temp matrices in this calculation
// never change, we ought to be able to reuse them.
this.implCovar = MatrixUtils.impliedCovar(edgeCoefT, errCovar(errorVariances()));
// Submatrix of implied covar for measured vars only.
int size = getMeasuredNodes().size();
this.implCovarMeas = new TetradMatrix(size, size);
for (int i = 0; i < size; i++) {
for (int j = 0; j < size; j++) {
Node iNode = getMeasuredNodes().get(i);
Node jNode = getMeasuredNodes().get(j);
int _i = getVariableNodes().indexOf(iNode);
int _j = getVariableNodes().indexOf(jNode);
this.implCovarMeas.set(i, j, this.implCovar.get(_i, _j));
}
}
}
/**
* @param sampleSize The sample size of the desired data set.
* @param latentDataSaved True if latent variables should be included in the data set.
* @return This returns a standardized data set simulated from the model, using the reduced form
* method.
*/
public DataSet simulateDataReducedForm(int sampleSize, boolean latentDataSaved) {
int numVars = getVariableNodes().size();
// Calculate inv(I - edgeCoef)
TetradMatrix edgeCoef = edgeCoef().copy().transpose();
// TetradMatrix iMinusB = TetradAlgebra.identity(edgeCoef.rows());
// iMinusB.assign(edgeCoef, Functions.minus);
TetradMatrix iMinusB = TetradAlgebra.identity(edgeCoef.rows()).minus(edgeCoef);
TetradMatrix inv = iMinusB.inverse();
// Pick error values e, for each calculate inv * e.
TetradMatrix sim = new TetradMatrix(sampleSize, numVars);
// Generate error data with the right variances and covariances, then override this
// with error data for varaibles that have special distributions defined. Not ideal,
// but not sure what else to do at the moment. It's better than not taking covariances
// into account!
TetradMatrix cholesky = MatrixUtils.choleskyC(errCovar(errorVariances()));
for (int i = 0; i < sampleSize; i++) {
TetradVector e = new TetradVector(exogenousData(cholesky, RandomUtil.getInstance()));
TetradVector ePrime = inv.times(e);
sim.assignRow(i, ePrime); // sim.viewRow(i).assign(ePrime);
}
DataSet fullDataSet = ColtDataSet.makeContinuousData(getVariableNodes(), sim);
if (latentDataSaved) {
return fullDataSet;
} else {
return DataUtils.restrictToMeasured(fullDataSet);
}
}
