code: ///////////////////////////////////////////////////////////////////////////////
  // For information as to what this class does, see the Javadoc, below.       //
  // Copyright (C) 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,       //
  // 2007, 2008, 2009, 2010, 2014, 2015 by Peter Spirtes, Richard Scheines, Joseph   //
  // Ramsey, and Clark Glymour.                                                //
  //                                                                           //
  // This program is free software; you can redistribute it and/or modify      //
  // it under the terms of the GNU General Public License as published by      //
  // the Free Software Foundation; either version 2 of the License, or         //
  // (at your option) any later version.                                       //
  //                                                                           //
  // This program is distributed in the hope that it will be useful,           //
  // but WITHOUT ANY WARRANTY; without even the implied warranty of            //
  // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the             //
  // GNU General Public License for more details.                              //
  //                                                                           //
  // You should have received a copy of the GNU General Public License         //
  // along with this program; if not, write to the Free Software               //
  // Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA //
  ///////////////////////////////////////////////////////////////////////////////
  
  package edu.cmu.tetrad.regression;
  
  
  /**
   * Stores the various components of a regression result in a single class so
   * that they can be passed together as an argument or return value.
   *
   * @author Joseph Ramsey
   */
  class RegressionPlane {
  
      /**
       * Summary string.
       */
      private String summary;
  
      /**
       * Variable names.
       */
      private String[] varNames;
  
      /**
       * The number of data points.
       */
      private int sampleSize;
  
      /**
       * The number of regressors.
       */
      private int numRegressors;
  
      /**
       * The array of regression coefficients.
       */
      private double[] coefs;
  
      /**
       * The array of coefT-statistics for the regression coefficients.
       */
      private double[] coefT;
  
      /**
       * The array of coefP-values for the regression coefficients.
       */
      private double[] coefP;
  
      /**
       * Standard errors of the coefficients
       */
      private double[] coefSE;
  
      /**
       * True iff this model assumes a zero intercept.
       */
      private boolean zeroIntercept;
  
      /**
       * R square value.
       */
      private double rSquare;
  
      /**
       * Constructs a new LinRegrResult.
       *
       * @param sampleSize    the number of spectra.
       * @param numRegressors the number of regressors
       * @param coefs         the array of regression coefficients.
       * @param coefT         the array of coefT-statistics for the regression
       *                      coefficients.
       * @param coefP         the array of coefP-values for the regression
       *                      coefficients.
       */
      public RegressionPlane(boolean zeroIntercept, String[] varNames,
                             int numRegressors, int sampleSize, double[] coefs, double[] coefT,
                             double[] coefP, double rsquare, double[] coefSE, String summary) {
          this.zeroIntercept = zeroIntercept;
  
          int error = zeroIntercept ? 0 : 1;
  
          if (varNames.length != numRegressors + error) {
              throw new IllegalArgumentException();
          }
  
          if (coefs.length != numRegressors + error) {
              throw new IllegalArgumentException();
          }
  
          if (coefT.length != numRegressors + error) {
              throw new IllegalArgumentException();
          }
  
          if (coefP.length != numRegressors + error) {
              throw new IllegalArgumentException();
          }
  
          this.varNames = varNames;
          this.numRegressors = numRegressors;
          this.sampleSize = sampleSize;
          this.coefs = coefs;
          this.coefT = coefT;
          this.coefP = coefP;
          this.coefSE = coefSE;
          this.rSquare = rsquare;
          this.summary = summary;
      }
  
      public double[] getCoefSE() {
          return coefSE;
      }
  
      public double getRSquare() {
          return rSquare;
      }
  
      /**
       * @return the number of data points.
       */
      public int getSampleSize() {
          return sampleSize;
      }
  
      /**
       * @return the number of regressors.
       */
      public int getNumRegressors() {
          return numRegressors;
      }
  
      /**
       * @return the array of regression coeffients.
       */
      public double[] getCoef() {
          return coefs;
      }
  
      /**
       * @return the array of coefT-statistics for the regression coefficients.
       */
      public double[] getCoefT() {
          return coefT;
      }
  
      /**
       * @return the array of coefP-values for the regression coefficients.
       */
      public double[] getCoefP() {
          return coefP;
      }
  
  
      public String[] getVarNames() {
          return varNames;
      }
  
      public double getPredictedValue(double[] x) {
          double yHat = 0.0;
  
          int offset = zeroIntercept ? 0 : 1;
  
          for (int i = offset; i < numRegressors; i++) {
              yHat += coefs[i + offset] * x[i];
          }
  
          if (!zeroIntercept) {
              yHat += coefs[0]; // error term.
          }
  
          return yHat;
      }
  
      public String toString() {
          return summary;
      }
  }
  
  
  
  
  
  
  