public RegressionResults regress()

in commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/regression/SimpleRegression.java [819:882]
60 lines of code
19 McCabe index (conditional complexity)

    public RegressionResults regress(int[] variablesToInclude) throws MathIllegalArgumentException{
        if( variablesToInclude == null || variablesToInclude.length == 0){
          throw new MathIllegalArgumentException(LocalizedFormats.ARRAY_ZERO_LENGTH_OR_NULL_NOT_ALLOWED);
        }
        if( variablesToInclude.length > 2 || (variablesToInclude.length > 1 && !hasIntercept) ){
            throw new ModelSpecificationException(
                    LocalizedFormats.ARRAY_SIZE_EXCEEDS_MAX_VARIABLES,
                    (variablesToInclude.length > 1 && !hasIntercept) ? 1 : 2);
        }

        if( hasIntercept ){
            if( variablesToInclude.length == 2 ){
                if( variablesToInclude[0] == 1 ){
                    throw new ModelSpecificationException(LocalizedFormats.NOT_INCREASING_SEQUENCE);
                }else if( variablesToInclude[0] != 0 ){
                    throw new OutOfRangeException( variablesToInclude[0], 0,1 );
                }
                if( variablesToInclude[1] != 1){
                     throw new OutOfRangeException( variablesToInclude[0], 0,1 );
                }
                return regress();
            }else{
                if( variablesToInclude[0] != 1 && variablesToInclude[0] != 0 ){
                     throw new OutOfRangeException( variablesToInclude[0],0,1 );
                }
                final double mean = sumY * sumY / n;
                final double syy = sumYY + mean;
                if( variablesToInclude[0] == 0 ){
                    //just the mean
                    final double[] vcv = new double[]{ sumYY/(((n-1)*n)) };
                    final double[] params = new double[]{ ybar };
                    return new RegressionResults(
                      params, new double[][]{vcv}, true, n, 1,
                      sumY, syy+mean, sumYY,true,false);
                }else if( variablesToInclude[0] == 1){
                    //final double _syy = sumYY + sumY * sumY / ((double) n);
                    final double sxx = sumXX + sumX * sumX / n;
                    final double sxy = sumXY + sumX * sumY / n;
                    final double sse = JdkMath.max(0d, syy - sxy * sxy / sxx);
                    final double mse = sse/((n-1));
                    if( !Double.isNaN(sxx) ){
                        final double[] vcv = new double[]{ mse / sxx };
                        final double[] params = new double[]{ sxy/sxx };
                        return new RegressionResults(
                                    params, new double[][]{vcv}, true, n, 1,
                                    sumY, syy, sse,false,false);
                    }else{
                        final double[] vcv = new double[]{Double.NaN };
                        final double[] params = new double[]{ Double.NaN };
                        return new RegressionResults(
                                    params, new double[][]{vcv}, true, n, 1,
                                    Double.NaN, Double.NaN, Double.NaN,false,false);
                    }
                }
            }
        }else{
            if( variablesToInclude[0] != 0 ){
                throw new OutOfRangeException(variablesToInclude[0],0,0);
            }
            return regress();
        }

        return null;
    }