net.maizegenetics.stats.linearmodels.PartitionedLinearModel Maven / Gradle / Ivy

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TASSEL 6 is a software package to evaluate traits association. Feature Tables are at the heart of the package where, a feature is a range of positions or a single position. Row in the that table are taxon.
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package net.maizegenetics.stats.linearmodels;

import java.util.ArrayList;
import java.util.List;

import net.maizegenetics.matrixalgebra.Matrix.DoubleMatrix;
import net.maizegenetics.matrixalgebra.Matrix.DoubleMatrixFactory;
import net.maizegenetics.stats.linearmodels.ModelEffect;
import net.maizegenetics.stats.linearmodels.SweepFastLinearModel;

public class PartitionedLinearModel {
    //	 ArrayList modelEffectList;     //the model effects in the first partition
    SweepFastLinearModel lm = null;                    //the linear model representing the first partition
    ArrayList baseModel;
    DoubleMatrix G1;                          //the inverse for the first partition
    DoubleMatrix[][] x2tx1matrices;
    DoubleMatrix X1Ty;
    double modelss;
    double errorss;
    double modeldf;
    double errordf;
    double[] y;

    public PartitionedLinearModel(ArrayList baseModel, SweepFastLinearModel lm) {
        this.lm = lm;
        this.baseModel = baseModel;
        init();
    }

    public PartitionedLinearModel(List baseModel, SweepFastLinearModel lm) {
        this.lm = lm;
        if (baseModel instanceof ArrayList)
            this.baseModel = (ArrayList) baseModel;
        else
            baseModel = new ArrayList<>(baseModel);
        init();
    }

    private void init() {
        y = lm.y;
        G1 = lm.getInverseOfXtX();
        x2tx1matrices = new DoubleMatrix[1][baseModel.size()];
        int nx1 = baseModel.size();
        DoubleMatrix[][] x1tymatrices = new DoubleMatrix[nx1][1];
        for (int i = 0; i < nx1; i++) {
            x1tymatrices[i][0] = baseModel.get(i).getXty(y);
        }
        X1Ty = DoubleMatrixFactory.DEFAULT.compose(x1tymatrices);
    }

    public SweepFastLinearModel getLinearModel() {
        return lm;
    }

    public void testNewModelEffect(ModelEffect me) {
        int nx1 = baseModel.size();
        for (int i = 0; i < nx1; i++) {
            x2tx1matrices[0][i] = ModelEffectUtils.getXtY(me, baseModel.get(i));
        }

        //from Searle. 1987. Linear Models for Unbalanced Data. p.264
        //for the added term, ssmodel = y'M1X2BX2'M1y, where
        //B = inverse(X2'M1X2)
        //X2'M1X2 = X2'X2 - X2'X1G1X1'X2
        //use A = X2'X1G1 and X2'M1X2 = X2'X2 - AX1'X2
        //X2'M1y = X2'y - X2'X1G1X1'y = X2Ty - AX1Ty

        DoubleMatrix X2TX1 = DoubleMatrixFactory.DEFAULT.compose(x2tx1matrices);
        DoubleMatrix X2TX2 = me.getXtX();
        DoubleMatrix A = X2TX1.mult(G1);
        DoubleMatrix X2TM1X2 = X2TX2.minus(A.tcrossproduct(X2TX1));
        double[] ssdf = lm.getResidualSSdf();
        DoubleMatrix X2Ty = me.getXty(y);
        DoubleMatrix X2TM1y = X2Ty.minus(A.mult(X1Ty));

        int[] rank = new int[] { 0 };
        DoubleMatrix B = X2TM1X2.generalizedInverseWithRank(rank);
        modelss = X2TM1y.crossproduct(B).mult(X2TM1y).get(0, 0);
        errorss = ssdf[0] - modelss;
        modeldf = rank[0];
        errordf = ssdf[1] - modeldf;
    }

    public double testNewModelEffect(double[] covariate) {
        //from Searle. 1987. Linear Models for Unbalanced Data. p.264
        //for the added term, ssmodel = y'M1X2BX2'M1y, where
        //B = inverse(X2'M1X2)
        //X2'M1X2 = X2'X2 - X2'X1G1X1'X2
        //use A = X2'X1G1 and X2'X1G1X1'X2 = AX1'X2
        //X2'M1y = X2'y - X2'X1G1X1'y = X2Ty - AX1Ty

        int nx1 = baseModel.size();
        for (int i = 0; i < nx1; i++) {
            x2tx1matrices[0][i] = baseModel.get(i).getXty(covariate).transpose();
        }
        DoubleMatrix X2TX1 = DoubleMatrixFactory.DEFAULT.compose(x2tx1matrices);
        DoubleMatrix A = X2TX1.mult(G1);
        double ax1ty = A.mult(X1Ty).get(0, 0);
        double ax1tx2 = A.tcrossproduct(X2TX1).get(0, 0);

        double x2tx2 = 0;
        double x2ty = 0;
        int count = 0;
        for (double d : covariate) {
            x2tx2 += d * d;
            x2ty += d * y[count++];
        }

        double x2tm1x2 = x2tx2 - ax1tx2;
        double x2tm1y = x2ty - ax1ty;
        if (x2tm1x2 < 1e-12) {
            return 0;
        }
        return x2tm1y * x2tm1y / x2tm1x2;
    }

    //only appropriate for a covariate which has one df
    public void setModelSS(double modelss) {
        this.modelss = modelss;
        double[] ssdf = lm.getResidualSSdf();
        errorss = ssdf[0] - modelss;
        modeldf = 1;
        errordf = ssdf[1] - modeldf;
    }

    public double getModelSS() {
        return modelss;
    }

    public double getModeldf() {
        return modeldf;
    }

    public double getErrorSS() {
        return errorss;
    }

    public double getErrordf() {
        return errordf;
    }

    public double getF() {
        return modelss / errorss / modeldf * errordf;
    }

    public double getp() {
        double p = 1;
        if (modeldf == 0)
            return 1;
        try {
            p = LinearModelUtils.Ftest(getF(), modeldf, errordf);
        } catch (Exception e) {
            //	            System.err.println("error calculating p");
        }
        return p;
    }

    public double[] getFp() {
        double F = modelss / errorss / modeldf * errordf;
        double p = Double.NaN;
        if (modeldf > 0) {
            try {
                p = LinearModelUtils.Ftest(getF(), modeldf, errordf);
            } catch (Exception e) {
                //	            System.err.println("error calculating p");
            }
        }
        return new double[] { F, p };

    }
}