bundles/org.simantics.spreadsheet/src/org/simantics/spreadsheet/solver/formula/LinestFormulaFunction.java

   1 package org.simantics.spreadsheet.solver.formula;
   2
   3 import org.apache.commons.math3.stat.regression.OLSMultipleLinearRegression;
   4 import org.simantics.spreadsheet.SpreadsheetMatrix;
   5 import org.simantics.spreadsheet.Spreadsheets;
   6 import org.simantics.spreadsheet.solver.formula.parser.ast.AstArgList;
   7
   8 public class LinestFormulaFunction implements CellFormulaFunction<SpreadsheetMatrix> {
   9
  10     @Override
  11     public SpreadsheetMatrix evaluate(CellValueVisitor visitor, AstArgList args) {
  12         if (args.values.size() != 4)
  13             throw new IllegalStateException();
  14
  15         Object ys = args.values.get(0).accept(visitor);
  16         Object xs = args.values.get(1).accept(visitor);
  17
  18         if (xs instanceof SpreadsheetMatrix) {
  19
  20             OLSMultipleLinearRegression reg = new OLSMultipleLinearRegression();
  21
  22             SpreadsheetMatrix xsm = (SpreadsheetMatrix) xs;
  23             SpreadsheetMatrix ysm = (SpreadsheetMatrix) ys;
  24
  25             if (xsm.getWidth() > 1) {
  26
  27                 double y[] = new double[ysm.values.length];
  28                 double x[][] = new double[ysm.values.length][xsm.getWidth()];
  29
  30                 for (int i = 0; i < ysm.values.length; i++) {
  31                     y[i] = Spreadsheets.asNumber(ysm.values[i]);
  32                     for (int j = 0; j < xsm.getWidth(); j++) {
  33                         x[i][j] = Spreadsheets.asNumber(xsm.get(i, j));
  34                     }
  35                 }
  36
  37                 reg.newSampleData(y, x);
  38
  39                 int width = xsm.getWidth() + 1;
  40                 SpreadsheetMatrix result = new SpreadsheetMatrix(width, 5);
  41                 double[] pars = reg.estimateRegressionParameters();
  42                 for (int i = 0; i < width; i++)
  43                     result.set(0, width - i - 1, pars[i]);
  44                 double[] errs = reg.estimateRegressionParametersStandardErrors();
  45                 for (int i = 0; i < width; i++)
  46                     result.set(1, width - i - 1, errs[i]);
  47
  48                 double sstotal = reg.calculateTotalSumOfSquares();
  49                 double ssresid = reg.calculateResidualSumOfSquares();
  50                 double ssreg = sstotal - ssresid;
  51                 double F = 0;
  52                 double r2 = ssreg / sstotal;
  53                 double sey = reg.estimateRegressionStandardError();
  54                 double df = width;
  55
  56                 result.set(2, 0, r2);
  57                 result.set(2, 1, sey);
  58                 result.set(3, 0, F);
  59                 result.set(3, 1, df);
  60                 result.set(4, 0, ssreg);
  61                 result.set(4, 1, ssresid);
  62                 result.set(2,2, FormulaError2.NA.getString());
  63                 result.set(3,2, FormulaError2.NA.getString());
  64                 result.set(4,2, FormulaError2.NA.getString());
  65
  66                 return result;
  67
  68             }
  69
  70         }
  71
  72         return null;
  73     }
  74
  75 }