public IndTestFisherZPercentIndependent(List<DataSet> dataSets, double alpha) {
this.dataSets = dataSets;
this.variables = dataSets.get(0).getVariables();
data = new ArrayList<TetradMatrix>();
for (DataSet dataSet : dataSets) {
dataSet = DataUtils.center(dataSet);
TetradMatrix _data = dataSet.getDoubleData();
data.add(_data);
}
ncov = new ArrayList<TetradMatrix>();
for (TetradMatrix d : this.data) ncov.add(d.transpose().times(d).scalarMult(1.0 / d.rows()));
setAlpha(alpha);
rows = new int[dataSets.get(0).getNumRows()];
for (int i = 0; i < getRows().length; i++) getRows()[i] = i;
variablesMap = new HashMap<Node, Integer>();
for (int i = 0; i < variables.size(); i++) {
variablesMap.put(variables.get(i), i);
}
this.recursivePartialCorrelation = new ArrayList<RecursivePartialCorrelation>();
for (TetradMatrix covMatrix : ncov) {
recursivePartialCorrelation.add(new RecursivePartialCorrelation(getVariables(), covMatrix));
}
}
/**
* @return Returns the error covariance matrix of the model. i.e. [a][b] is the covariance of E_a
* and E_b, with [a][a] of course being the variance of E_a. THESE ARE NOT PARAMETERS OF THE
* MODEL; THEY ARE CALCULATED. Note that elements of this matrix may be Double.NaN; this
* indicates that these elements cannot be calculated.
*/
private TetradMatrix errCovar(Map<Node, Double> errorVariances) {
List<Node> variableNodes = getVariableNodes();
List<Node> errorNodes = new ArrayList<Node>();
for (Node node : variableNodes) {
errorNodes.add(semGraph.getExogenous(node));
}
TetradMatrix errorCovar = new TetradMatrix(errorVariances.size(), errorVariances.size());
for (int index = 0; index < errorNodes.size(); index++) {
Node error = errorNodes.get(index);
double variance = getErrorVariance(error);
errorCovar.set(index, index, variance);
}
for (int index1 = 0; index1 < errorNodes.size(); index1++) {
for (int index2 = 0; index2 < errorNodes.size(); index2++) {
Node error1 = errorNodes.get(index1);
Node error2 = errorNodes.get(index2);
Edge edge = semGraph.getEdge(error1, error2);
if (edge != null && Edges.isBidirectedEdge(edge)) {
double covariance = getErrorCovariance(error1, error2);
errorCovar.set(index1, index2, covariance);
}
}
}
return errorCovar;
}
/**
* 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);
}
}
private TetradMatrix subMatrix(Node x, Node y, List<Node> z) {
int dim = z.size() + 2;
int[] indices = new int[dim];
indices[0] = variables.indexOf(x);
indices[1] = variables.indexOf(y);
for (int k = 0; k < z.size(); k++) {
indices[k + 2] = variables.indexOf(z.get(k));
}
TetradMatrix submatrix = new TetradMatrix(dim, dim);
for (int i = 0; i < dim; i++) {
for (int j = 0; j < dim; j++) {
int i1 = indices[i];
int i2 = indices[j];
submatrix.set(i, j, covMatrix.getDouble(i1, i2));
}
}
return submatrix;
}
public boolean isIndependent(Node x, Node y, List<Node> z) {
int[] all = new int[z.size() + 2];
all[0] = variablesMap.get(x);
all[1] = variablesMap.get(y);
for (int i = 0; i < z.size(); i++) {
all[i + 2] = variablesMap.get(z.get(i));
}
int sampleSize = data.get(0).rows();
List<Double> pValues = new ArrayList<Double>();
for (int m = 0; m < ncov.size(); m++) {
TetradMatrix _ncov = ncov.get(m).getSelection(all, all);
TetradMatrix inv = _ncov.inverse();
double r = -inv.get(0, 1) / sqrt(inv.get(0, 0) * inv.get(1, 1));
double fisherZ =
sqrt(sampleSize - z.size() - 3.0) * 0.5 * (Math.log(1.0 + r) - Math.log(1.0 - r));
double pValue;
if (Double.isInfinite(fisherZ)) {
pValue = 0;
} else {
pValue = 2.0 * (1.0 - RandomUtil.getInstance().normalCdf(0, 1, abs(fisherZ)));
}
pValues.add(pValue);
}
double _cutoff = alpha;
if (fdr) {
_cutoff = StatUtils.fdrCutoff(alpha, pValues, false);
}
Collections.sort(pValues);
int index = (int) round((1.0 - percent) * pValues.size());
this.pValue = pValues.get(index);
// if (this.pValue == 0) {
// System.out.println("Zero pvalue "+ SearchLogUtils.independenceFactMsg(x, y, z,
// getPValue()));
// }
boolean independent = this.pValue > _cutoff;
if (verbose) {
if (independent) {
TetradLogger.getInstance()
.log("independencies", SearchLogUtils.independenceFactMsg(x, y, z, getPValue()));
// System.out.println(SearchLogUtils.independenceFactMsg(x, y, z, getPValue()));
} else {
TetradLogger.getInstance()
.log("dependencies", SearchLogUtils.dependenceFactMsg(x, y, z, getPValue()));
}
}
return independent;
}
/**
* @return The edge coefficient matrix of the model, a la SemIm. Note that this will normally need
* to be transposed, since [a][b] is the edge coefficient for a-->b, not b-->a. Sorry.
* History. THESE ARE PARAMETERS OF THE MODEL--THE ONLY PARAMETERS.
*/
public TetradMatrix edgeCoef() {
List<Node> variableNodes = getVariableNodes();
TetradMatrix edgeCoef = new TetradMatrix(variableNodes.size(), variableNodes.size());
for (Edge edge : edgeParameters.keySet()) {
if (Edges.isBidirectedEdge(edge)) {
continue;
}
Node a = edge.getNode1();
Node b = edge.getNode2();
int aindex = variableNodes.indexOf(a);
int bindex = variableNodes.indexOf(b);
double coef = edgeParameters.get(edge);
edgeCoef.set(aindex, bindex, coef);
}
return edgeCoef;
}
/**
* Takes a Cholesky decomposition from the Cholesky.cholesky method and a set of data simulated
* using the information in that matrix. Written by Don Crimbchin. Modified June 8, Matt
* Easterday: added a random # seed so that data can be recalculated with the same result in
* Causality lab
*
* @param cholesky the result from cholesky above.
* @param randomUtil a random number generator, if null the method will make a new generator for
* each random number needed
* @return an array the same length as the width or length (cholesky should have the same width
* and length) containing a randomly generate data set.
*/
private double[] exogenousData(TetradMatrix cholesky, RandomUtil randomUtil) {
// Step 1. Generate normal samples.
double exoData[] = new double[cholesky.rows()];
for (int i = 0; i < exoData.length; i++) {
exoData[i] = randomUtil.nextNormal(0, 1);
}
// Step 2. Multiply by cholesky to get correct covariance.
double point[] = new double[exoData.length];
for (int i = 0; i < exoData.length; i++) {
double sum = 0.0;
for (int j = 0; j <= i; j++) {
sum += cholesky.get(i, j) * exoData[j];
}
point[i] = sum;
}
return point;
}
// Prints a smallest subset of parents that causes a singular matrix exception.
private void printMinimalLinearlyDependentSet(int[] parents, ICovarianceMatrix cov) {
List<Node> _parents = new ArrayList<>();
for (int p : parents) _parents.add(variables.get(p));
DepthChoiceGenerator gen = new DepthChoiceGenerator(_parents.size(), _parents.size());
int[] choice;
while ((choice = gen.next()) != null) {
int[] sel = new int[choice.length];
List<Node> _sel = new ArrayList<>();
for (int m = 0; m < choice.length; m++) {
sel[m] = parents[m];
_sel.add(variables.get(sel[m]));
}
TetradMatrix m = cov.getSelection(sel, sel);
try {
m.inverse();
} catch (Exception e2) {
out.println("### Linear dependence among variables: " + _sel);
}
}
}
/**
* @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);
}
}
/**
* This method computes the information matrix or Hessian matrix of second order partial
* derivatives of the fitting function (4B_2 on page 135 of Bollen) with respect to the free
* freeParameters of the estimated SEM. It then computes the inverse of the the information matrix
* and calculates the standard errors of the freeParameters as the square roots of the diagonal
* elements of that matrix.
*
* @param estSem the estimated SEM.
*/
public void computeStdErrors(ISemIm estSem) {
// if (!unmeasuredLatents(estSem.getSemPm()).isEmpty()) {
// int n = estSem.getFreeParameters().size();
// stdErrs = new double[n];
//
// for (int i = 0; i < n; i++) {
// stdErrs[i] = Double.NaN;
// }
//
// return;
// }
// this.semIm = estSem;
estSem.setParameterBoundsEnforced(false);
double[] paramsOriginal = estSem.getFreeParamValues();
double delta;
FittingFunction fcn = new SemFittingFunction(estSem);
boolean ridder = false; // Ridder is more accurate but a lot slower.
int n = fcn.getNumParameters();
// Store the free freeParameters of the SemIm so that they can be reset to these
// values. The differentiation methods change them.
double[] params = new double[n];
System.arraycopy(paramsOriginal, 0, params, 0, n);
// If the Ridder method (secondPartialDerivativeRidr) is used to search for
// the best delta it is initially set to 0.1. Otherwise the delta is set to
// 0.005. That value has worked well for those fitting functions tested to
// date.
if (ridder) {
delta = 0.1;
} else {
delta = 0.005;
}
// The Hessian matrix of second order partial derivatives is called the
// information matrix.
TetradMatrix hess = new TetradMatrix(n, n);
List<Parameter> freeParameters = estSem.getFreeParameters();
boolean containsCovararianceParameter = false;
for (Parameter p : freeParameters) {
if (p.getType() == ParamType.COVAR) {
containsCovararianceParameter = true;
break;
}
}
for (int i = 0; i < n; i++) {
for (int j = i; j < n; j++) {
Parameter pi = freeParameters.get(i);
Parameter pj = freeParameters.get(j);
if (!containsCovararianceParameter) {
// Restrict off-diagonal to just collider edge freeParameters.
if (i != j && (pi.getType() != ParamType.COEF || pj.getType() != ParamType.COEF)) {
continue;
}
if (pi.getNodeB() != pj.getNodeB()) {
continue;
}
}
double v;
if (ridder) {
v = secondPartialDerivativeRidr(fcn, i, j, params, delta);
} else {
v = secondPartialDerivative(fcn, i, j, params, delta);
}
if (Math.abs(v) < 1e-7) {
v = 0;
}
// if (Double.isNaN(v)) {
// v = 0;
// }
hess.set(i, j, v);
hess.set(j, i, v);
}
}
ROWS:
for (int i = 0; i < hess.rows(); i++) {
for (int j = 0; j < hess.columns(); j++) {
if (hess.get(i, j) != 0) {
continue ROWS;
}
}
// System.out.println("Zero row for " + freeParameters.get(i));
}
// The diagonal elements of the inverse of the information matrix are the
// squares of the standard errors of the freeParameters. Their order is the
// same as in the array of free parameter values stored in paramsOriginal.
try {
TetradMatrix hessInv = hess.inverse();
// TetradMatrix hessInv = hess.ginverse();
// System.out.println("Inverse: " + hessInv);
// for (int i = 0; i < freeParameters.size(); i++) {
// System.out.println(i + " = " + freeParameters.get(i));
// }
stdErrs = new double[n];
// Hence the standard errors of the freeParameters are the square roots of the
// diagonal elements of the inverse of the information matrix.
for (int i = 0; i < n; i++) {
double v = Math.sqrt((2.0 / (estSem.getSampleSize() - 1)) * hessInv.get(i, i));
if (v == 0) {
System.out.println("v = " + v + " hessInv(i, i) = " + hessInv.get(i, i));
}
if (v == 0) {
stdErrs[i] = Double.NaN;
} else {
stdErrs[i] = v;
}
}
} catch (Exception e) {
e.printStackTrace();
stdErrs = new double[n];
for (int i = 0; i < n; i++) {
stdErrs[i] = Double.NaN;
}
}
// Restore the freeParameters of the estimated SEM to their original values.
estSem.setFreeParamValues(paramsOriginal);
estSem.setParameterBoundsEnforced(true);
}
/**
* Takes a list of tetrads for the given data set and returns the chi square value for the test.
* We assume that the tetrads are non-redundant; if not, a matrix exception will be thrown.
*
* <p>Calculates the T statistic (Bollen and Ting, p. 161). This is significant if tests as
* significant using the Chi Square distribution with degrees of freedom equal to the number of
* nonredundant tetrads tested.
*/
@Override
// public double calcChiSquare1(Sextad... sextads) {
// this.storedSextads = sextads;
//
// this.df = sextads.length;
//
// // Need a list of symbolic covariances--i.e. covariances that appear in tetrads.
// Set<Sigma> boldSigmaSet = new LinkedHashSet<Sigma>();
// List<Sigma> boldSigma = new ArrayList<Sigma>();
//
// for (Sextad sextad : sextads) {
// boldSigmaSet.add(new Sigma(sextad.getI(), sextad.getL()));
// boldSigmaSet.add(new Sigma(sextad.getI(), sextad.getM()));
// boldSigmaSet.add(new Sigma(sextad.getI(), sextad.getN()));
//
// boldSigmaSet.add(new Sigma(sextad.getJ(), sextad.getL()));
// boldSigmaSet.add(new Sigma(sextad.getJ(), sextad.getM()));
// boldSigmaSet.add(new Sigma(sextad.getJ(), sextad.getN()));
//
// boldSigmaSet.add(new Sigma(sextad.getK(), sextad.getL()));
// boldSigmaSet.add(new Sigma(sextad.getK(), sextad.getM()));
// boldSigmaSet.add(new Sigma(sextad.getK(), sextad.getN()));
// }
//
// for (Sigma sigma : boldSigmaSet) {
// boldSigma.add(sigma);
// }
//
// // Need a matrix of variances and covariances of sample covariances.
// TetradMatrix sigma_ss = TetradMatrix.instance(boldSigma.size(), boldSigma.size());
//
// for (int i = 0; i < boldSigma.size(); i++) {
// for (int j = 0; j < boldSigma.size(); j++) {
// Sigma sigmaef = boldSigma.get(i);
// Sigma sigmagh = boldSigma.get(j);
//
// Node e = sigmaef.getA();
// Node f = sigmaef.getB();
// Node g = sigmagh.getA();
// Node h = sigmagh.getB();
//
// if (cov != null && cov instanceof CorrelationMatrix) {
//
//// Assumes multinormality. Using formula 23. (Not implementing formula 22 because
// that case
//// does not come up.)
// double rr = 0.5 * (r(e, f) * r(g, h))
// * (r(e, g) * r(e, g) + r(e, h) * r(e, h) + r(f, g) * r(f, g) + r(f,
// h) * r(f, h))
// + r(e, g) * r(f, h) + r(e, h) * r(f, g)
// - r(e, f) * (r(f, g) * r(f, h) + r(e, g) * r(e, h))
// - r(g, h) * (r(f, g) * r(e, g) + r(f, h) * r(e, h));
//
// sigma_ss.set(i, j, rr);
// } else if (cov != null && dataSet == null) {
//
// // Assumes multinormality--see p. 160.
// double _ss = r(e, g) * r(f, h) + r(e, h) * r(f, g); // + or -? Different
// advise. + in the code.
// sigma_ss.set(i, j, _ss);
// } else {
// double _ss = sxyzw(e, f, g, h) - r(e, f) * r(g, h);
// sigma_ss.set(i, j, _ss);
// }
// }
// }
//
// // Need a matrix of of population estimates of partial derivatives of tetrads
// // with respect to covariances in boldSigma.
// TetradMatrix del = TetradMatrix.instance(boldSigma.size(), sextads.length);
//
// for (int i = 0; i < boldSigma.size(); i++) {
// for (int j = 0; j < sextads.length; j++) {
// Sigma sigma = boldSigma.get(i);
// Sextad sextad = sextads[j];
//
// Node m1 = sextad.getI();
// Node m2 = sextad.getJ();
// Node m3 = sextad.getK();
// Node m4 = sextad.getL();
// Node m5 = sextad.getM();
// Node m6 = sextad.getN();
//
// double derivative = getDerivative(m1, m2, m3, m4, m5, m6, sigma.getA(),
// sigma.getB());
// del.set(i, j, derivative);
// }
// }
//
// // Need a vector of population estimates of the sextads.
// TetradMatrix t = TetradMatrix.instance(sextads.length, 1);
//
// for (int i = 0; i < sextads.length; i++) {
// Sextad sextad = sextads[i];
//
// List<Node> nodes = new ArrayList<Node>();
//
// nodes.add(sextad.getI());
// nodes.add(sextad.getJ());
// nodes.add(sextad.getK());
// nodes.add(sextad.getL());
// nodes.add(sextad.getM());
// nodes.add(sextad.getN());
//
// TetradMatrix m = TetradMatrix.instance(3, 3);
//
// for (int k1 = 0; k1 < 3; k1++) {
// for (int k2 = 0; k2 < 3; k2++) {
// m.set(k1, k2, r(nodes.get(k1), nodes.get(3+k2)));
// }
// }
//
// double value = TetradAlgebra.det(m);
// t.set(i, 0, value);
// this.storedValue = value;
// }
//
// // Now multiply to get Sigma_tt
// TetradMatrix w1 = TetradAlgebra.times(del.transpose(), sigma_ss);
// TetradMatrix sigma_tt = TetradAlgebra.times(w1, del);
//
// // And now invert and multiply to get T.
// TetradMatrix v0 = TetradAlgebra.inverse(sigma_tt);
// TetradMatrix v1 = TetradAlgebra.times(t.transpose(), v0);
// TetradMatrix v2 = TetradAlgebra.times(v1, t);
// double chisq = N * v2.get(0, 0);
//
// this.chisq = chisq;
// return chisq;
// }
public double calcChiSquare(Sextad... sextads) {
this.storedSextads = sextads;
this.df = 4; // sextads.length;
List<Sigma> boldSigma = new ArrayList<Sigma>();
List<Node> _nodes = new ArrayList<Node>(sextads[0].getNodes());
for (int i = 0; i < _nodes.size(); i++) {
for (int j = i + 1; j < _nodes.size(); j++) {
boldSigma.add(new Sigma(_nodes.get(i), _nodes.get(j)));
}
}
// Need a matrix of variances and covariances of sample covariances.
TetradMatrix sigma_ss = new TetradMatrix(boldSigma.size(), boldSigma.size());
for (int i = 0; i < boldSigma.size(); i++) {
for (int j = i; j < boldSigma.size(); j++) {
Sigma sigmaef = boldSigma.get(i);
Sigma sigmagh = boldSigma.get(j);
Node e = sigmaef.getA();
Node f = sigmaef.getB();
Node g = sigmagh.getA();
Node h = sigmagh.getB();
if (cov != null && cov instanceof CorrelationMatrix) {
// Assumes multinormality. Using formula 23. (Not implementing formula 22
// because that case
// does not come up.)
double rr =
0.5
* (r(e, f) * r(g, h))
* (r(e, g) * r(e, g)
+ r(e, h) * r(e, h)
+ r(f, g) * r(f, g)
+ r(f, h) * r(f, h))
+ r(e, g) * r(f, h)
+ r(e, h) * r(f, g)
- r(e, f) * (r(f, g) * r(f, h) + r(e, g) * r(e, h))
- r(g, h) * (r(f, g) * r(e, g) + r(f, h) * r(e, h));
sigma_ss.set(i, j, rr);
sigma_ss.set(j, i, rr);
} else if (cov != null && dataSet == null) {
// Assumes multinormality--see p. 160.
double _ss =
r(e, g) * r(f, h) + r(e, h) * r(f, g); // + or -? Different advise. + in the code.
// double _ss = r(e, g) * r(f, h) - r(e, h) * r(g, f); // shouldn't
// this be a tetrad?
sigma_ss.set(i, j, _ss);
sigma_ss.set(j, i, _ss);
} else {
double _ss = sxyzw(e, f, g, h) - r(e, f) * r(g, h);
sigma_ss.set(i, j, _ss);
sigma_ss.set(j, i, _ss);
}
}
}
// Need a matrix of of population estimates of partial derivatives of tetrads
// with respect to covariances in boldSigma.
TetradMatrix del = new TetradMatrix(boldSigma.size(), sextads.length);
for (int j = 0; j < sextads.length; j++) {
Sextad sextad = sextads[j];
for (int i = 0; i < boldSigma.size(); i++) {
Sigma sigma = boldSigma.get(i);
double derivative = getDerivative(sextad, sigma);
del.set(i, j, derivative);
}
}
// Need a vector of population estimates of the sextads.
TetradMatrix t = new TetradMatrix(sextads.length, 1);
for (int i = 0; i < sextads.length; i++) {
Sextad sextad = sextads[i];
List<Node> nodes = sextad.getNodes();
TetradMatrix m = new TetradMatrix(3, 3);
for (int k1 = 0; k1 < 3; k1++) {
for (int k2 = 0; k2 < 3; k2++) {
m.set(k1, k2, r(nodes.get(k1), nodes.get(3 + k2)));
}
}
double det = m.det();
t.set(i, 0, det);
this.storedValue = det; // ?
}
// for (int i = 0; i < sextads.length; i++) {
// Sextad sextad = sextads[i];
//
// List<Node> nodes = new ArrayList<Node>();
//
// nodes.add(sextad.getI());
// nodes.add(sextad.getJ());
// nodes.add(sextad.getK());
// nodes.add(sextad.getL());
// nodes.add(sextad.getM());
// nodes.add(sextad.getN());
//
// TetradMatrix m = TetradMatrix.instance(3, 3);
//
// for (int k1 = 0; k1 < 3; k1++) {
// for (int k2 = 0; k2 < 3; k2++) {
// m.set(k1, k2, r(nodes.get(k1), nodes.get(3+k2)));
// }
// }
//
// double value = TetradAlgebra.det(m);
// t.set(i, 0, value);
// this.storedValue = value;
// }
TetradMatrix sigma_tt = del.transpose().times(sigma_ss).times(del);
try {
this.chisq = N * t.transpose().times(sigma_tt.inverse()).times(t).get(0, 0);
return chisq;
} catch (Exception e) {
return Double.NaN;
}
}
/**
* 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());
}
public DataSet simulateDataCholesky(
int sampleSize, TetradMatrix covar, List<Node> variableNodes) {
List<Node> variables = new LinkedList<Node>();
for (Node node : variableNodes) {
variables.add(node);
}
List<Node> newVariables = new ArrayList<Node>();
for (Node node : variables) {
ContinuousVariable continuousVariable = new ContinuousVariable(node.getName());
continuousVariable.setNodeType(node.getNodeType());
newVariables.add(continuousVariable);
}
TetradMatrix impliedCovar = covar;
DataSet fullDataSet = new ColtDataSet(sampleSize, newVariables);
TetradMatrix cholesky = MatrixUtils.choleskyC(impliedCovar);
// Simulate the data by repeatedly calling the Cholesky.exogenousData
// method. Store only the data for the measured variables.
ROW:
for (int row = 0; row < sampleSize; row++) {
// Step 1. Generate normal samples.
double exoData[] = new double[cholesky.rows()];
for (int i = 0; i < exoData.length; i++) {
exoData[i] = RandomUtil.getInstance().nextNormal(0, 1);
// exoData[i] = randomUtil.nextUniform(-1, 1);
}
// Step 2. Multiply by cholesky to get correct covariance.
double point[] = new double[exoData.length];
for (int i = 0; i < exoData.length; i++) {
double sum = 0.0;
for (int j = 0; j <= i; j++) {
sum += cholesky.get(i, j) * exoData[j];
}
point[i] = sum;
}
double rowData[] = point;
for (int col = 0; col < variables.size(); col++) {
int index = variableNodes.indexOf(variables.get(col));
double value = rowData[index];
if (Double.isNaN(value) || Double.isInfinite(value)) {
throw new IllegalArgumentException("Value out of range: " + value);
}
fullDataSet.setDouble(row, col, value);
}
}
return DataUtils.restrictToMeasured(fullDataSet);
}