Examples with LeastSquaresFit ubic.basecode.math.linearmodels.LeastSquaresFit used on opensource projects

Example 1 with LeastSquaresFit

use of ubic.basecode.math.linearmodels.LeastSquaresFit in project Gemma by PavlidisLab.

the class ComBat method gammaHat.

private void gammaHat(DoubleMatrix2D sdata) {
    DoubleMatrix2D Xb = x.viewPart(0, 0, x.rows(), numBatches);
    gammaHat = new LeastSquaresFit(Xb, sdata).getCoefficients();
}

Example 2 with LeastSquaresFit

use of ubic.basecode.math.linearmodels.LeastSquaresFit in project Gemma by PavlidisLab.

the class ComBat method standardize.

/**
 * Special standardization: partial regression of covariates
 *
 * @param b b
 * @param A A
 * @return double matrix 2d
 */
DoubleMatrix2D standardize(DoubleMatrix2D b, DoubleMatrix2D A) {
    DoubleMatrix2D beta = new LeastSquaresFit(A, b).getCoefficients();
    // assertEquals( 3.7805, beta.get( 0, 0 ), 0.001 );
    // assertEquals( 0.0541, beta.get( 2, 18 ), 0.001 );
    int batchIndex = 0;
    DoubleMatrix2D bba = new DenseDoubleMatrix2D(1, numBatches);
    for (String batchId : batches.keySet()) {
        bba.set(0, batchIndex++, (double) batches.get(batchId).size() / numSamples);
    }
    /*
         * Weight the non-batch coefficients by the batch sizes.
         */
    DoubleMatrix2D grandMeanM = solver.mult(bba, beta.viewPart(0, 0, numBatches, beta.columns()));
    if (hasMissing) {
        varpooled = y.copy().assign(solver.transpose(solver.mult(x, beta)), Functions.minus);
        DoubleMatrix2D var = new DenseDoubleMatrix2D(varpooled.rows(), 1);
        for (int i = 0; i < varpooled.rows(); i++) {
            DoubleMatrix1D row = varpooled.viewRow(i);
            double m = DescriptiveWithMissing.mean(new DoubleArrayList(row.toArray()));
            double v = DescriptiveWithMissing.sampleVariance(new DoubleArrayList(row.toArray()), m);
            var.set(i, 0, v);
        }
        varpooled = var;
    } else {
        varpooled = y.copy().assign(solver.transpose(solver.mult(x, beta)), Functions.minus).assign(Functions.pow(2));
        DoubleMatrix2D scale = new DenseDoubleMatrix2D(numSamples, 1);
        scale.assign(1.0 / numSamples);
        varpooled = solver.mult(varpooled, scale);
    }
    DoubleMatrix2D size = new DenseDoubleMatrix2D(numSamples, 1);
    size.assign(1.0);
    /*
         * The coefficients repeated for each sample.
         */
    standMean = solver.mult(solver.transpose(grandMeanM), solver.transpose(size));
    /*
         * Erase the batch factors from a copy of the design matrix
         */
    DoubleMatrix2D tmpX = x.copy();
    for (batchIndex = 0; batchIndex < numBatches; batchIndex++) {
        for (int j = 0; j < x.rows(); j++) {
            tmpX.set(j, batchIndex, 0.0);
        }
    }
    /*
         * row means, adjusted "per group", and ignoring batch effects.
         */
    standMean = standMean.assign(solver.transpose(solver.mult(tmpX, beta)), Functions.plus);
    DoubleMatrix2D varsq = solver.mult(varpooled.copy().assign(Functions.sqrt), solver.transpose(size));
    /*
         * Subtract the mean and divide by the standard deviations.
         */
    DoubleMatrix2D meansubtracted = y.copy().assign(standMean, Functions.minus);
    return meansubtracted.assign(varsq, Functions.div);
}