public RegressionResults regress()

in commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/regression/MillerUpdatingRegression.java [1010:1106]
91 lines of code
21 McCabe index (conditional complexity)

    public RegressionResults regress(int[] variablesToInclude) throws ModelSpecificationException {
        if (variablesToInclude.length > this.nvars) {
            throw new ModelSpecificationException(
                    LocalizedFormats.TOO_MANY_REGRESSORS, variablesToInclude.length, this.nvars);
        }
        if (this.nobs <= this.nvars) {
            throw new ModelSpecificationException(
                    LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,
                    this.nobs, this.nvars);
        }
        Arrays.sort(variablesToInclude);
        int iExclude = 0;
        for (int i = 0; i < variablesToInclude.length; i++) {
            if (i >= this.nvars) {
                throw new ModelSpecificationException(
                        LocalizedFormats.INDEX_LARGER_THAN_MAX, i, this.nvars);
            }
            if (i > 0 && variablesToInclude[i] == variablesToInclude[i - 1]) {
                variablesToInclude[i] = -1;
                ++iExclude;
            }
        }
        int[] series;
        if (iExclude > 0) {
            int j = 0;
            series = new int[variablesToInclude.length - iExclude];
            for (int i = 0; i < variablesToInclude.length; i++) {
                if (variablesToInclude[i] > -1) {
                    series[j] = variablesToInclude[i];
                    ++j;
                }
            }
        } else {
            series = variablesToInclude;
        }

        reorderRegressors(series, 0);
        tolset();
        singcheck();

        double[] beta = this.regcf(series.length);

        ss();

        double[] cov = this.cov(series.length);

        int rnk = 0;
        for (int i = 0; i < this.lindep.length; i++) {
            if (!this.lindep[i]) {
                ++rnk;
            }
        }

        boolean needsReorder = false;
        for (int i = 0; i < series.length; i++) {
            if (this.vorder[i] != series[i]) {
                needsReorder = true;
                break;
            }
        }
        if (!needsReorder) {
            return new RegressionResults(
                    beta, new double[][]{cov}, true, this.nobs, rnk,
                    this.sumy, this.sumsqy, this.sserr, this.hasIntercept, false);
        } else {
            double[] betaNew = new double[beta.length];
            int[] newIndices = new int[beta.length];
            for (int i = 0; i < series.length; i++) {
                for (int j = 0; j < this.vorder.length; j++) {
                    if (this.vorder[j] == series[i]) {
                        betaNew[i] = beta[ j];
                        newIndices[i] = j;
                    }
                }
            }
            double[] covNew = new double[cov.length];
            int idx1 = 0;
            int idx2;
            int ii;
            int jj;
            for (int i = 0; i < beta.length; i++) {
                ii = newIndices[i];
                for (int j = 0; j <= i; j++, idx1++) {
                    jj = newIndices[j];
                    if (ii > jj) {
                        idx2 = ii * (ii + 1) / 2 + jj;
                    } else {
                        idx2 = jj * (jj + 1) / 2 + ii;
                    }
                    covNew[idx1] = cov[idx2];
                }
            }
            return new RegressionResults(
                    betaNew, new double[][]{covNew}, true, this.nobs, rnk,
                    this.sumy, this.sumsqy, this.sserr, this.hasIntercept, false);
        }
    }