/** * @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); } }