bool gcta::comput_inverse_logdet_LDLT(eigenMatrix &Vi, eigenVector &prev_varcmp, double &logdet)
{
int i=0, n=Vi.cols();
LDLT<eigenMatrix> ldlt(Vi);
eigenVector d=ldlt.vectorD();
if(d.minCoeff()<0){
if(prev_varcmp.minCoeff()>0) return false;
else throw("Error: the matrix V becomes negative-definite because of one of the variance component is negative.\nPlease re-run the analysis without the --reml-no-constrain option.");
}
logdet=0.0;
for(i=0; i<n; i++) logdet+=log(d[i]);
Vi.setIdentity();
ldlt.solveInPlace(Vi);
return true;
}
bool gcta::bending_eigenval(eigenVector &eval)
{
int j=0;
double eval_m=eval.mean();
if(eval.minCoeff()>0.0) return false;
double S=0.0, P=0.0;
for(j=0; j<eval.size(); j++){
if(eval[j]>=0) continue;
S+=eval[j];
P=-eval[j];
}
double W=S*S*100.0+1;
for(j=0; j<eval.size(); j++){
if(eval[j]>=0) continue;
eval[j]=P*(S-eval[j])*(S-eval[j])/W;
}
eval*=eval_m/eval.mean();
return true;
}