void calcMeanAndCovarWeighedVectorized(const Eigen::MatrixXf &input, const Eigen::VectorXd &inputWeights, Eigen::MatrixXf &out_covMat, Eigen::VectorXf &out_mean,Eigen::MatrixXf &temp)
{
out_mean=input.col(0); //to resize
out_mean.setZero();
double wSumInv=1.0/inputWeights.sum();
for (int k=0;k<inputWeights.size();k++){
double w=inputWeights[k];
out_mean+=input.col(k)*(float)(w*wSumInv);
}
out_mean = input.rowwise().mean();
temp = (input.colwise() - out_mean);
for (int k=0;k<inputWeights.size();k++){
temp.col(k) *= (float)(sqrt(inputWeights(k)*wSumInv)); //using square roots, as we only want the normalized weights to be included once for each result element in the multiplication below
}
out_covMat = temp*temp.transpose();
}
int TestCovariate(Matrix& Xnull, Matrix& Y, Matrix& Xcol,
const EigenMatrix& kinshipU, const EigenMatrix& kinshipS){
Eigen::MatrixXf g;
G_to_Eigen(Xcol, &g);
// store U'*G for computing AF later.
const Eigen::MatrixXf& U = kinshipU.mat;
this->ug = U.transpose() * g;
Eigen::RowVectorXf g_mean = g.colwise().mean();
g = g.rowwise() - g_mean;
double gTg = g.array().square().sum();
double t_new = (g.array() * this->transformedY.array()).sum();
t_new = t_new * t_new / gTg;
double t_score = t_new / this->gamma;
this->betaG = (g.transpose() * this->transformedY).sum() / gTg / this->gamma;
this->betaGVar = this->ySigmaY / gTg / this->gamma;
this->pvalue = gsl_cdf_chisq_Q(t_score, 1.0);
return 0;
}
Eigen::MatrixXf centerMatrix(const Eigen::MatrixXf& x) {
return x.rowwise() - x.colwise().mean();
}
