void linear_reg::estimate(regdata& rdatain, int verbose, double tol_chol,
int model, int interaction, int ngpreds,
masked_matrix& invvarmatrixin, int robust,
int nullmodel)
{
//suda ineraction parameter
// model should come here
regdata rdata = rdatain.get_unmasked_data();
if (invvarmatrixin.length_of_mask != 0)
{
invvarmatrixin.update_mask(rdatain.masked_data);
// invvarmatrixin.masked_data->print();
}
if (verbose)
{
cout << rdata.is_interaction_excluded
<< " <-irdata.is_interaction_excluded\n";
// std::cout << "invvarmatrix:\n";
// invvarmatrixin.masked_data->print();
std::cout << "rdata.X:\n";
rdata.X.print();
}
mematrix<double> X = apply_model(rdata.X, model, interaction, ngpreds,
rdata.is_interaction_excluded, false,
nullmodel);
if (verbose)
{
std::cout << "X:\n";
X.print();
std::cout << "Y:\n";
rdata.Y.print();
}
int length_beta = X.ncol;
beta.reinit(length_beta, 1);
sebeta.reinit(length_beta, 1);
//Han Chen
if (length_beta > 1)
{
if (model == 0 && interaction != 0 && ngpreds == 2 && length_beta > 2)
{
covariance.reinit(length_beta - 2, 1);
}
else
{
covariance.reinit(length_beta - 1, 1);
}
}
//Oct 26, 2009
mematrix<double> tX = transpose(X);
if (invvarmatrixin.length_of_mask != 0)
{
tX = tX * invvarmatrixin.masked_data;
//!check if quicker
//tX = productXbySymM(tX,invvarmatrix);
// = invvarmatrix*X;
// std::cout<<"new tX.nrow="<<X.nrow<<" tX.ncol="<<X.ncol<<"\n";
}
mematrix<double> tXX = tX * X;
double N = X.nrow;
#if EIGEN_COMMENTEDOUT
MatrixXd Xeigen = X.data;
MatrixXd tXeigen = Xeigen.transpose();
MatrixXd tXXeigen = tXeigen * Xeigen;
VectorXd Yeigen = rdata.Y.data;
VectorXd tXYeigen = tXeigen * Yeigen;
// Solve X^T * X * beta = X^T * Y for beta:
VectorXd betaeigen = tXXeigen.fullPivLu().solve(tXYeigen);
beta.data = betaeigen;
if (verbose)
{
std::cout << setprecision(9) << "Xeigen:\n" << Xeigen << endl;
std::cout << setprecision(9) << "tX:\n" << tXeigen << endl;
std::cout << setprecision(9) << "tXX:\n" << tXXeigen << endl;
std::cout << setprecision(9) << "tXY:\n" << tXYeigen << endl;
std::cout << setprecision(9) << "beta:\n"<< betaeigen << endl;
printf("----\n");
printf("beta[0] = %e\n", betaeigen.data()[0]);
printf("----\n");
// (beta).print();
double relative_error = (tXXeigen * betaeigen - tXYeigen).norm() / tXYeigen.norm(); // norm() is L2 norm
cout << "The relative error is:\n" << relative_error << endl;
}
// This one is needed later on in this function
mematrix<double> tXX_i = invert(tXX);
#else
//
// use cholesky to invert
//
mematrix<double> tXX_i = tXX;
cholesky2_mm(tXX_i, tol_chol);
chinv2_mm(tXX_i);
// before was
// mematrix<double> tXX_i = invert(tXX);
mematrix<double> tXY = tX * (rdata.Y);
beta = tXX_i * tXY;
if (verbose)
{
std::cout << "tX:\n";
tX.print();
std::cout << "tXX:\n";
tXX.print();
std::cout << "chole tXX:\n";
tXX_i.print();
std::cout << "tXX-1:\n";
tXX_i.print();
std::cout << "tXY:\n";
tXY.print();
std::cout << "beta:\n";
(beta).print();
}
#endif
// now compute residual variance
sigma2 = 0.;
mematrix<double> ttX = transpose(tX);
mematrix<double> sigma2_matrix = rdata.Y;
mematrix<double> sigma2_matrix1 = ttX * beta;
// std::cout << "sigma2_matrix\n";
// sigma2_matrix.print();
//
// std::cout << "sigma2_matrix1\n";
// sigma2_matrix1.print();
sigma2_matrix = sigma2_matrix - sigma2_matrix1;
// std::cout << "sigma2_matrix\n";
// sigma2_matrix.print();
static double val;
// std::cout << "sigma2_matrix.nrow=" << sigma2_matrix.nrow
// << "sigma2_matrix.ncol" << sigma2_matrix.ncol
// <<"\n";
for (int i = 0; i < sigma2_matrix.nrow; i++)
{
val = sigma2_matrix.get(i, 0);
// std::cout << "val = " << val << "\n";
sigma2 += val * val;
// std::cout << "sigma2+= " << sigma2 << "\n";
}
double sigma2_internal = sigma2 / (N - static_cast<double>(length_beta));
// now compute residual variance
// sigma2 = 0.;
// for (int i =0;i<(rdata.Y).nrow;i++)
// sigma2 += ((rdata.Y).get(i,0))*((rdata.Y).get(i,0));
// for (int i=0;i<length_beta;i++)
// sigma2 -= 2. * (beta.get(i,0)) * tXY.get(i,0);
// for (int i=0;i<(length_beta);i++)
// for (int j=0;j<(length_beta);j++)
// sigma2 += (beta.get(i,0)) * (beta.get(j,0)) * tXX.get(i,j);
// std::cout<<"sigma2="<<sigma2<<"\n";
// std::cout<<"sigma2_internal="<<sigma2_internal<<"\n";
// replaced for ML
// sigma2_internal = sigma2/(N - double(length_beta) - 1);
// std::cout << "sigma2/=N = "<< sigma2 << "\n";
sigma2 /= N;
// std::cout<<"N="<<N<<", length_beta="<<length_beta<<"\n";
if (verbose)
{
std::cout << "sigma2 = " << sigma2 << "\n";
}
/*
loglik = 0.;
double ss=0;
for (int i=0;i<rdata.nids;i++) {
double resid = rdata.Y[i] - beta.get(0,0); // intercept
for (int j=1;j<beta.nrow;j++) resid -= beta.get(j,0)*X.get(i,j);
// residuals[i] = resid;
ss += resid*resid;
}
sigma2 = ss/N;
*/
//cout << "estimate " << rdata.nids << "\n";
//(rdata.X).print();
//for (int i=0;i<rdata.nids;i++) cout << rdata.masked_data[i] << " ";
//cout << endl;
loglik = 0.;
double halfrecsig2 = .5 / sigma2;
for (int i = 0; i < rdata.nids; i++)
{
double resid = rdata.Y[i] - beta.get(0, 0); // intercept
for (int j = 1; j < beta.nrow; j++)
resid -= beta.get(j, 0) * X.get(i, j);
residuals[i] = resid;
loglik -= halfrecsig2 * resid * resid;
}
loglik -= static_cast<double>(rdata.nids) * log(sqrt(sigma2));
// cout << "estimate " << rdata.nids << "\n";
//
// Ugly fix to the fact that if we do mmscore, sigma2 is already
// in the matrix...
// YSA, 2009.07.20
//
//cout << "estimate 0\n";
if (invvarmatrixin.length_of_mask != 0)
sigma2_internal = 1.0;
mematrix<double> robust_sigma2(X.ncol, X.ncol);
if (robust)
{
mematrix<double> XbyR = X;
for (int i = 0; i < X.nrow; i++)
for (int j = 0; j < X.ncol; j++)
{
double tmpval = XbyR.get(i, j) * residuals[i];
XbyR.put(tmpval, i, j);
}
XbyR = transpose(XbyR) * XbyR;
robust_sigma2 = tXX_i * XbyR;
robust_sigma2 = robust_sigma2 * tXX_i;
}
//cout << "estimate 0\n";
for (int i = 0; i < (length_beta); i++)
{
if (robust)
{
// cout << "estimate :robust\n";
double value = sqrt(robust_sigma2.get(i, i));
sebeta.put(value, i, 0);
//Han Chen
if (i > 0)
{
if (model == 0 && interaction != 0 && ngpreds == 2
&& length_beta > 2)
{
if (i > 1)
{
double covval = robust_sigma2.get(i, i - 2);
covariance.put(covval, i - 2, 0);
}
}
else
{
double covval = robust_sigma2.get(i, i - 1);
covariance.put(covval, i - 1, 0);
}
}
//Oct 26, 2009
}
else
{
// cout << "estimate :non-robust\n";
double value = sqrt(sigma2_internal * tXX_i.get(i, i));
sebeta.put(value, i, 0);
//Han Chen
if (i > 0)
{
if (model == 0 && interaction != 0 && ngpreds == 2
&& length_beta > 2)
{
if (i > 1)
{
double covval = sigma2_internal * tXX_i.get(i, i - 2);
covariance.put(covval, i - 2, 0);
}
}
else
{
double covval = sigma2_internal * tXX_i.get(i, i - 1);
covariance.put(covval, i - 1, 0);
}
}
//Oct 26, 2009
}
}
//cout << "estimate E\n";
if (verbose)
{
std::cout << "sebeta (" << sebeta.nrow << "):\n";
sebeta.print();
}
}
void logistic_reg::estimate(regdata& rdatain, int verbose, int maxiter,
double eps, double tol_chol, int model,
int interaction, int ngpreds,
masked_matrix& invvarmatrixin, int robust,
int nullmodel)
{
// In contrast to the 'linear' case 'invvarmatrix' contains the
// inverse of correlation matrix (not the inverse of var-cov matrix)
// h2.object$InvSigma * h.object2$h2an$estimate[length(h2$h2an$estimate)]
// the inverse of var-cov matrix scaled by total variance
regdata rdata = rdatain.get_unmasked_data();
// a lot of code duplicated between linear and logistic...
// e.g. a piece below...
mematrix<double> invvarmatrix;
if (invvarmatrixin.length_of_mask != 0)
{
invvarmatrixin.update_mask(rdatain.masked_data);
}
mematrix<double> X = apply_model(rdata.X, model, interaction, ngpreds,
rdata.is_interaction_excluded, false,
nullmodel);
int length_beta = X.ncol;
beta.reinit(length_beta, 1);
sebeta.reinit(length_beta, 1);
//Han Chen
if (length_beta > 1)
{
if (model == 0 && interaction != 0 && ngpreds == 2 && length_beta > 2)
{
covariance.reinit(length_beta - 2, 1);
}
else
{
covariance.reinit(length_beta - 1, 1);
}
}
//Oct 26, 2009
mematrix<double> W((X).nrow, 1);
mematrix<double> z((X).nrow, 1);
mematrix<double> tXWX(length_beta, length_beta);
mematrix<double> tXWX_i(length_beta, length_beta);
mematrix<double> tXWz(length_beta, 1);
double prev = (rdata.Y).column_mean(0);
if (prev >= 1. || prev <= 0.)
{
std::cerr << "prevalence not within (0,1)\n";
exit(1);
}
for (int i = 0; i < length_beta; i++)
beta.put(0., i, 0);
beta.put(log(prev / (1. - prev)), 0, 0);
mematrix<double> tX = transpose(X);
if (invvarmatrix.nrow != 0 && invvarmatrix.ncol != 0)
{
//TODO(maarten):invvarmatix is symmetric:is there an more effective way?
tX = tX * invvarmatrix;
}
/*
std::cout << "\n";
std::cout << "X " << X.get(0,0) << " " << X.get(0,1) << " "
<< X.get(0,2) << "\n";
if (X.ncol==4) std::cout << "X[4] " << X.get(0,3) << "\n";
std::cout << "Inv " << invvarmatrix.get(0,0) << " "
<< invvarmatrix.get(0,1) << " "
<< invvarmatrix.get(0,2) << "\n";
if (X.ncol==4) std::cout << ,"X[4] " << invvarmatrix.get(0,3) << "\n";
std::cout << "tXInv " << tX.get(0,0) << " " << tX.get(1,0) << " "
<< tX.get(2,0) << "%f\n";
if (X.ncol==4) std::cout << "X[4] " << tX.get(3,0) << "\n";
*/
niter = 0;
double delta = 1.;
double prevlik = 0.;
while (niter < maxiter && delta > eps)
{
mematrix<double> eMu = (X) * beta;
mematrix<double> eMu_us = eMu;
for (int i = 0; i < eMu.nrow; i++)
{
double emu = eMu.get(i, 0);
double value = emu;
double zval = 0.;
value = exp(value) / (1. + exp(value));
residuals[i] = (rdata.Y).get(i, 0) - value;
eMu.put(value, i, 0);
W.put(value * (1. - value), i, 0);
zval = emu
+ (1. / (value * (1. - value)))
* (((rdata.Y).get(i, 0)) - value);
z.put(zval, i, 0);
}
mematrix<double> tmp = productMatrDiag(tX, W);
if (verbose)
{
std::cout << "tXW:\n";
tmp.print();
}
mematrix<double> tXWX = tmp * (X);
//N = tXWX.get(0, 0);
if (verbose)
{
std::cout << "tXWX:\n";
tXWX.print();
}
// std::cout << "tXWX:\n";tXWX.print();
//
// use cholesky to invert
//
// tXWX_i = tXWX;
//cholesky2_mm(tXWX_i,tol_chol);
//if (verbose) {std::cout << "chole tXWX:\n"; tXWX_i.print();}
//std::cout << "chole tXWX:\n"; tXWX_i.print();
//chinv2_mm(tXWX_i);
// was before
tXWX_i = invert(tXWX);
if (verbose)
{
std::cout << "tXWX-1:\n";
tXWX_i.print();
}
// std::cout << "*** tXWX_i\n"; tXWX_i.print();
mematrix<double> tmp1 = productMatrDiag(tX, W);
mematrix<double> tXWz = tmp1 * z;
if (verbose)
{
std::cout << "tXWz:\n";
tXWz.print();
}
beta = tXWX_i * tXWz;
// std::cout << "*** res: " << residuals[0] << " "
// << residuals[1] << " " << residuals[2] << "\n";
//mematrix<double> txres = tx * residuals;
// std::cout << "*** txres\n";txres.print();
//beta = txwx_i* txres;
if (verbose)
{
std::cout << "beta:\n";
beta.print();
}
// std::cout << "beta:\n"; beta.print();
// compute likelihood
prevlik = loglik;
loglik = 0.;
for (int i = 0; i < eMu.nrow; i++)
loglik += rdata.Y[i] * eMu_us[i] - log(1. + exp(eMu_us[i]));
delta = fabs(1. - (prevlik / loglik));
niter++;
}
sigma2 = 0.;
mematrix<double> robust_sigma2(X.ncol, X.ncol);
if (robust)
{
mematrix<double> XbyR = X;
for (int i = 0; i < X.nrow; i++)
for (int j = 0; j < X.ncol; j++)
{
double tmpval = XbyR.get(i, j) * residuals[i];
XbyR.put(tmpval, i, j);
}
XbyR = transpose(XbyR) * XbyR;
robust_sigma2 = tXWX_i * XbyR;
robust_sigma2 = robust_sigma2 * tXWX_i;
}
for (int i = 0; i < (length_beta); i++)
{
if (robust)
{
double value = sqrt(robust_sigma2.get(i, i));
sebeta.put(value, i, 0);
//Han Chen
if (i > 0)
{
if (model == 0 && interaction != 0 && ngpreds == 2
&& length_beta > 2)
{
if (i > 1)
{
double covval = robust_sigma2.get(i, i - 2);
covariance.put(covval, i - 2, 0);
}
}
else
{
double covval = robust_sigma2.get(i, i - 1);
covariance.put(covval, i - 1, 0);
}
}
//Oct 26, 2009
}
else
{
double value = sqrt(tXWX_i.get(i, i));
sebeta.put(value, i, 0);
//Han Chen
if (i > 0)
{
if (model == 0 && interaction != 0 && ngpreds == 2
&& length_beta > 2)
{
if (i > 1)
{
double covval = tXWX_i.get(i, i - 2);
covariance.put(covval, i - 2, 0);
}
}
else
{
double covval = tXWX_i.get(i, i - 1);
covariance.put(covval, i - 1, 0);
}
}
//Oct 26, 2009
}
}
if (verbose)
{
std::cout << "sebeta (" << sebeta.nrow << "):\n";
sebeta.print();
}
// std::cout << "beta (" << beta.nrow << "):\n"; beta.print();
// std::cout << "sebeta (" << sebeta.nrow << "):\n"; sebeta.print();
// exit(1);
}
/**
* \brief Estimate the parameters for linear regression.
*
* @param verbose Turns verbose printing of various matrices on if
* non-zero.
* @param model The number of the genetic model (e.g. additive,
* recessive, ...) that is to be applied by the apply_model() function.
* @param interaction
* @param ngpreds Number of genomic predictors (1 for dosages, 2 for
* probabilities).
* @param invvarmatrixin
* @param robust If non-zero calculate robust standard errors.
* @param nullmodel If non-zero calculate the null model (excluding
* SNP information).
*/
void linear_reg::estimate(const int verbose,
const int model,
const int interaction,
const int ngpreds,
masked_matrix& invvarmatrixin,
const int robust,
const int nullmodel) {
// suda interaction parameter
// model should come here
//regdata rdata = rdatain.get_unmasked_data();
if (verbose)
{
cout << reg_data.is_interaction_excluded
<< " <-rdata.is_interaction_excluded\n";
// std::cout << "invvarmatrix:\n";
// invvarmatrixin.masked_data->print();
std::cout << "rdata.X:\n";
reg_data.X.print();
}
mematrix<double> X = apply_model(reg_data.X, model, interaction, ngpreds,
reg_data.is_interaction_excluded, false, nullmodel);
if (verbose)
{
std::cout << "X:\n";
X.print();
std::cout << "Y:\n";
reg_data.Y.print();
}
int length_beta = X.ncol;
beta.reinit(length_beta, 1);
sebeta.reinit(length_beta, 1);
//Han Chen
if (length_beta > 1)
{
if (model == 0 && interaction != 0 && ngpreds == 2 && length_beta > 2)
{
covariance.reinit(length_beta - 2, 1);
}
else
{
covariance.reinit(length_beta - 1, 1);
}
}
double sigma2_internal;
LDLT <MatrixXd> Ch;
if (invvarmatrixin.length_of_mask != 0)
{
//retrieve masked data W
invvarmatrixin.update_mask(reg_data.masked_data);
mmscore_regression(X, invvarmatrixin, Ch);
double N = X.nrow;
//sigma2_internal = sigma2 / (N - static_cast<double>(length_beta));
// Ugly fix to the fact that if we do mmscore, sigma2 is already
// in the matrix...
// YSA, 2009.07.20
sigma2_internal = 1.0;
sigma2 /= N;
}
else // NO mm-score regression : normal least square regression
{
LeastSquaredRegression(X, Ch);
double N = static_cast<double>(X.nrow);
double P = static_cast<double>(length_beta);
sigma2_internal = sigma2 / (N - P);
sigma2 /= N;
}
/*
loglik = 0.;
double ss=0;
for (int i=0;i<rdata.nids;i++) {
double resid = rdata.Y[i] - beta.get(0,0); // intercept
for (int j=1;j<beta.nrow;j++) resid -= beta.get(j,0)*X.get(i,j);
// residuals[i] = resid;
ss += resid*resid;
}
sigma2 = ss/N;
*/
//cout << "estimate " << rdata.nids << "\n";
//(rdata.X).print();
//for (int i=0;i<rdata.nids;i++) cout << rdata.masked_data[i] << " ";
//cout << endl;
logLikelihood(X);
MatrixXd tXX_inv = Ch.solve(MatrixXd(length_beta, length_beta).
Identity(length_beta, length_beta));
mematrix<double> robust_sigma2(X.ncol, X.ncol);
int offset = X.ncol- 1;
//if additive and interaction and 2 predictors and more than 2 betas
if (model == 0 && interaction != 0 && ngpreds == 2 && length_beta > 2){
offset = X.ncol - 2;
}
if (robust)
{
RobustSEandCovariance(X, robust_sigma2, tXX_inv, offset);
}
else
{
PlainSEandCovariance(sigma2_internal, tXX_inv, offset);
}
}
