Beispiel #1
0
  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;
  }
Beispiel #2
0
  public GeneralizedSemPm(SemPm semPm) {
    this(semPm.getGraph());

    // Write down equations.
    try {
      List<Node> variableNodes = getVariableNodes();

      for (int i = 0; i < variableNodes.size(); i++) {
        Node node = variableNodes.get(i);
        List<Node> parents = getVariableParents(node);

        StringBuilder buf = new StringBuilder();

        for (int j = 0; j < parents.size(); j++) {
          if (!(variableNodes.contains(parents.get(j)))) {
            continue;
          }

          Node parent = parents.get(j);

          Parameter _parameter = semPm.getParameter(parent, node);
          String parameter = _parameter.getName();
          Set<Node> nodes = new HashSet<>();
          nodes.add(node);

          referencedParameters.put(parameter, nodes);

          buf.append(parameter);
          buf.append("*");
          buf.append(parents.get(j).getName());

          setParameterExpression(parameter, "Split(-1.5, -.5, .5, 1.5)");
          setStartsWithParametersTemplate(parameter.substring(0, 1), "Split(-1.5, -.5, .5, 1.5)");
          setStartsWithParametersEstimationInitializaationTemplate(
              parameter.substring(0, 1), "Split(-1.5, -.5, .5, 1.5)");

          if (j < parents.size() - 1) {
            buf.append(" + ");
          }
        }

        if (buf.toString().trim().length() != 0) {
          buf.append(" + ");
        }

        buf.append(errorNodes.get(i));
        setNodeExpression(node, buf.toString());
      }

      for (Node node : variableNodes) {
        Parameter _parameter = semPm.getParameter(node, node);
        String parameter = _parameter.getName();

        String distributionFormula = "N(0," + parameter + ")";
        setNodeExpression(getErrorNode(node), distributionFormula);
        setParameterExpression(parameter, "U(0, 1)");
        setStartsWithParametersTemplate(parameter.substring(0, 1), "U(0, 1)");
        setStartsWithParametersEstimationInitializaationTemplate(
            parameter.substring(0, 1), "U(0, 1)");
      }

      variableNames = new ArrayList<>();
      for (Node _node : variableNodes) variableNames.add(_node.getName());
      for (Node _node : errorNodes) variableNames.add(_node.getName());

    } catch (ParseException e) {
      throw new IllegalStateException("Parse error in constructing initial model.", e);
    }
  }
Beispiel #3
0
 /** @return the list of variable nodes of the model, in order. */
 public List<Node> getVariableNodes() {
   return semPm.getVariableNodes();
 }
Beispiel #4
0
 /**
  * @return For compatibility only. Returns the variable means of the model. These are always zero,
  *     since this is a standardized model. THESE ARE ALSO NOT PARAMETERS OF THE MODEL. ONLY THE
  *     COEFFICIENTS ARE PARAMETERS.
  */
 public double[] means() {
   return new double[semPm.getVariableNodes().size()];
 }
Beispiel #5
0
  /**
   * 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());
  }