int DataLoader::useResidualAsPhenotype() {
if (binaryPhenotype) {
logger->warn(
"WARNING: Skip transforming binary phenotype, although you want to "
"use residual as phenotype!");
return 0;
}
LinearRegression lr;
Vector pheno;
Matrix covAndInt;
const int numCovariate = covariate.ncol();
copyPhenotype(phenotype, &pheno);
copyCovariateAndIntercept(pheno.Length(), covariate, &covAndInt);
if (!lr.FitLinearModel(covAndInt, pheno)) {
if (numCovariate > 0) {
logger->error(
"Cannot fit model: [ phenotype ~ 1 + covariates ], now use the "
"original phenotype");
} else {
logger->error(
"Cannot fit model: [ phenotype ~ 1 ], now use the "
"original phenotype");
}
} else { // linear model fitted successfully
copyVectorToMatrixColumn(lr.GetResiduals(), &phenotype, 0);
// const int n = lr.GetResiduals().Length();
// for (int i = 0; i < n; ++i) {
// // phenotypeInOrder[i] = lr.GetResiduals()[i];
// phenotype[i][0] = lr.GetResiduals()[i];
// }
covariate.clear();
if (numCovariate > 0) {
logger->info(
"DONE: Fit model [ phenotype ~ 1 + covariates ] and model "
"residuals will be used as responses");
} else {
logger->info("DONE: Use residual as phenotype by centerng it");
}
// store fitting results
Vector& beta = lr.GetCovEst();
Matrix& betaSd = lr.GetCovB();
const int n = beta.Length();
for (int i = 0; i < n; ++i) {
addFittedParameter(covAndInt.GetColumnLabel(i), beta[i], betaSd[i][i]);
}
addFittedParameter("Sigma2", lr.GetSigma2(), NAN);
}
#if 0
if (covariate.ncol() > 0) {
LinearRegression lr;
Vector pheno;
Matrix covAndInt;
copyPhenotype(phenotype, &pheno);
copyCovariateAndIntercept(covariate.nrow(), covariate, &covAndInt);
if (!lr.FitLinearModel(covAndInt, pheno)) {
logger->error(
"Cannot fit model: [ phenotype ~ 1 + covariates ], now use the "
"original phenotype");
} else {
const int n = lr.GetResiduals().Length();
for (int i = 0; i < n; ++i) {
// phenotypeInOrder[i] = lr.GetResiduals()[i];
phenotype[i][0] = lr.GetResiduals()[i];
}
covariate.clear();
logger->info(
"DONE: Fit model [ phenotype ~ 1 + covariates ] and model "
"residuals will be used as responses");
}
storeFittedModel(lr);
} else { // no covaraites
// centerVector(&phenotypeInOrder);
std::vector<double> v;
phenotype.extractCol(0, &v);
centerVector(&v);
phenotype.setCol(0, v);
logger->info("DONE: Use residual as phenotype by centerng it");
}
#endif
return 0;
}
