/**
   * Constructs a new Fisher Z independence test with the listed arguments.
   *
   * @param data A 2D continuous data set with no missing values.
   * @param variables A list of variables, a subset of the variables of <code>data</code>.
   * @param alpha The significance cutoff level. p values less than alpha will be reported as
   *     dependent.
   */
  public IndTestFisherZShortTriangular(TetradMatrix data, List<Node> variables, double alpha) {
    DataSet dataSet = ColtDataSet.makeContinuousData(variables, data);
    this.covMatrix = new ShortTriangularMatrix(dataSet.getNumColumns());
    this.covMatrix.becomeCorrelationMatrix(dataSet);
    this.variables = dataSet.getVariables();
    setAlpha(alpha);

    this.deterministicTest = new IndTestFisherZGeneralizedInverse(dataSet, alpha);
  }
Beispiel #2
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  /**
   * @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);
    }
  }
Beispiel #3
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 /** Generates a simple exemplar of this class to test serialization. */
 public static IDeltaSextadTest serializableInstance() {
   return new DeltaSextadTest(ColtDataSet.serializableInstance());
 }
Beispiel #4
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  /**
   * 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());
  }