/**
* Calculates the sample likelihood and BIC score for i given its parents in a simple SEM model.
*/
private double localSemScore(int i, int[] parents) {
try {
ICovarianceMatrix cov = getCovMatrix();
double varianceY = cov.getValue(i, i);
double residualVariance = varianceY;
int n = sampleSize();
int p = parents.length;
int k = (p * (p + 1)) / 2 + p;
// int k = (p + 1) * (p + 1);
// int k = p + 1;
TetradMatrix covxx = cov.getSelection(parents, parents);
TetradMatrix covxxInv = covxx.inverse();
TetradVector covxy = cov.getSelection(parents, new int[] {i}).getColumn(0);
TetradVector b = covxxInv.times(covxy);
residualVariance -= covxy.dotProduct(b);
if (residualVariance <= 0 && verbose) {
out.println(
"Nonpositive residual varianceY: resVar / varianceY = "
+ (residualVariance / varianceY));
return Double.NaN;
}
double c = getPenaltyDiscount();
// return -n * log(residualVariance) - 2 * k; //AIC
return -n * Math.log(residualVariance) - c * k * Math.log(n);
// return -n * log(residualVariance) - c * k * (log(n) - log(2 * PI));
} catch (Exception e) {
e.printStackTrace();
throw new RuntimeException(e);
// throwMinimalLinearDependentSet(parents, cov);
}
}
/**
* @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);
}
}
