// [[Rcpp::export]]
Rcpp::List getWEXPcutoff(arma::mat data, arma::mat subdata, arma::mat Y, arma::mat subY, int N, arma::mat RT_out, double predictTime, arma::vec resid_sco, double fitvar, arma::colvec cutoffs){
//create dataD
arma::mat dataDuns = data.rows(find(data.col(1)==1));
arma::mat dataD = dataDuns.rows(sort_index(dataDuns.col(0)));
int n = data.n_rows, nD = dataD.n_rows, np = Y.n_cols, ncuts = subdata.n_rows;
//guide
colvec times = data.col(0);
// status= data.col(1);
// Dtimes = dataD.col(0);
// weights = data.col(4);
// Dweights = dataD.col(4);
uvec tmpind = find((data.col(0) <= predictTime)%data.col(1)); // indices of (data$times<=predict.time)&(data$status==1)
mat myempty(1,1);
uvec tmpindT = conv_to<uvec>::from(CSumI(times.elem(tmpind), 4, dataD.col(0), myempty, FALSE));
colvec rrk = exp(data.col(6)); //data.col(6) is data$linearY
// build riskmat
mat riskmat(n, nD);
mat::iterator riskmat_it = riskmat.begin();
for(colvec::iterator j = dataD.begin_col(0); j != dataD.end_col(0); j++){
for( colvec::iterator i = data.begin_col(0); i != data.end_col(0); i++){
*riskmat_it = *i >= *j; riskmat_it++;
}
}
//s0 and s1
colvec s0 = riskmat.t()*(rrk%data.col(4));
colvec s1 = riskmat.t()*trans(Vec2Mat(rrk%data.col(4), np)%Y.t());
//haz0 and cumhaz0
colvec haz0 = dataD.col(4)/sum(riskmat%trans(Vec2Mat(rrk%data.col(4), nD))).t();
colvec cumhaz0 = cumsum(haz0);
colvec ptvec(1); ptvec(0) = predictTime;
colvec cumhaz_t0 = CSumI(ptvec, 4, dataD.col(0), haz0, TRUE);
//Wexp
colvec Wexp_beta = resid_sco*fitvar*N;
colvec WexpLam1(n); WexpLam1.zeros(n);
WexpLam1(tmpind) = N/s0(tmpindT - 1);
WexpLam1 = WexpLam1 - CSumI( myPmin(data.col(0), predictTime), 4, dataD.col(0), haz0/s0, TRUE)%rrk*N;
colvec WexpLam2 = Wexp_beta*CSumI(ptvec, 4, dataD.col(0), haz0%s1/trans(Vec2Mat(s0, np)), TRUE);
colvec WexpLam = WexpLam1 - WexpLam2;
//Fyk = Pr(Sy < c)
colvec Fyk = CSumI( cutoffs, 4, data.col(6), data.col(4), TRUE)/sum(data.col(4));
colvec dFyk(Fyk.n_elem); dFyk(0) = 0;
dFyk(span(1, Fyk.n_elem - 1)) = Fyk(span(0, Fyk.n_elem-2));
dFyk = Fyk - dFyk;
colvec Sy = subdata.col(5);
colvec Syall = data.col(5);
colvec St0_Fyk = cumsum(Sy%dFyk);
double St0 = max(St0_Fyk);
colvec St0_Syk = St0 - St0_Fyk;
//
mat Wexp_Cond_Stc = -Vec2Mat(Sy%exp(subdata.col(6)), n)%(trans(Vec2Mat(WexpLam, ncuts)) +as_scalar(cumhaz_t0)*Wexp_beta*subY.t());
mat tmpmat = conv_to<mat>::from(trans(Vec2Mat(data.col(6), ncuts)) > Vec2Mat(cutoffs, n));
mat Wexp_Stc = trans(CSumI(cutoffs, 0, subdata.col(6), Wexp_Cond_Stc.t()%Vec2Mat(dFyk, n).t(), TRUE)) + trans(Vec2Mat(Syall,ncuts))%tmpmat - Vec2Mat(St0_Syk, n);
colvec Wexp_St = sum(trans(Wexp_Cond_Stc)%trans(Vec2Mat(dFyk, n))).t() + Syall - St0;
mat Wexp_Fc = 1-tmpmat - Vec2Mat(Fyk, n);
//assemble for classic performance measures, given linear predictor
List out(8);
out[0] = -Wexp_Cond_Stc;
out[1] = Wexp_Fc;
mat Wexp_St_mat = Vec2Mat(Wexp_St, ncuts).t();
out[2] = (-Wexp_St_mat%Vec2Mat(RT_out.col(3), n) + Wexp_Stc)/St0;
out[3] = (Wexp_St_mat%Vec2Mat(RT_out.col(4), n) -Wexp_Fc - Wexp_Stc)/(1-St0);
out[4] = -Wexp_St;
out[5] = (Wexp_St_mat - Wexp_Stc - Vec2Mat(RT_out.col(6), n)%Wexp_Fc)/Vec2Mat(Fyk, n);
out[6] = (Vec2Mat(RT_out.col(5)-1, n)%Wexp_Fc - Wexp_Stc)/Vec2Mat(1-Fyk, n);
out[7] = Wexp_beta;
return out;
}
tuple<double, double, int, int, double, double> simulate(const arma::Col<double> &Y, const vector<int> X,
double sigma, bool varianceKnown,
arma::mat &Z, mt19937_64 &rng,
bool interceptTerm) {
bernoulli_distribution bernoulli(0.5);
int N = X.size();
Z.fill(0);
// bestColumns[k] keeps track of the k + 1 or k + 2 columns that produce the smallest p-value depending on interceptTerm
vector<arma::uvec> bestColumns; bestColumns.reserve(N - 1);
if (interceptTerm) { // make intercept term last column of Z
fill(Z.begin_col(N - 1), Z.end_col(N - 1), 1);
copy(X.begin(), X.end(), Z.begin_col(0));
bestColumns.push_back(arma::uvec{0, (unsigned long long) N - 1ULL});
} else {
copy(X.begin(), X.end(), Z.begin_col(0));
bestColumns.push_back(arma::uvec{0});
}
// bestPValues[k] corresponds to p-value if the columns bestColumns[k] are used
vector<pair<double, double>> bestPValues; bestPValues.reserve(N - 1);
bestPValues.push_back(calculateBetaPValue(Z.cols(bestColumns.front()), Y, sigma, varianceKnown));
if (bestPValues.front().first <= 0.05) {
return make_tuple(bestPValues.front().first, bestPValues.front().second, 0, 0, -1, bestPValues.front().first);
} else { // need more covariates
bool done = false;
int smallestSubsetSize = INT_MAX;
/* add covariates one-by-one, we always include the treatment
* if we're using the intercept two covariates are included by default
*/
for (int j = 1; j < N - 2 || (j == N - 2 && !interceptTerm); ++j) {
for (int k = 0; k < N; ++k) Z(k, j) = bernoulli(rng);
if (!interceptTerm) {
while (arma::rank(Z) <= j) {
for (int k = 0; k < N; ++k) Z(k, j) = bernoulli(rng);
}
} else { // offset rank by 1 for intercept term
while (arma::rank(Z) <= j + 1) {
for (int k = 0; k < N; ++k) Z(k, j) = bernoulli(rng);
}
}
for (int k = j; k >= 1; --k) { // loop through subset sizes, k is the number of additional covariates
pair<double, double> newPValue;
if (k == j) { // use all available covariates
bestColumns.emplace_back(bestColumns.back().n_rows + 1); // add one more to biggest subset
for (int l = 0; l < bestColumns.back().n_rows - 1; ++l) {
bestColumns.back()(l) = bestColumns[j - 1](l); // copy over from original subset
}
bestColumns.back()(bestColumns.back().n_rows - 1) = j; // add new covariate
newPValue = calculateBetaPValue(Z.cols(bestColumns.back()), Y, sigma, varianceKnown);
bestPValues.push_back(newPValue);
} else { // make a new subset of same size with new covariate
arma::uvec columnSubset(bestColumns[k].n_rows);
for (int l = 0; l < columnSubset.n_rows - 1; ++l)
columnSubset(l) = bestColumns[k - 1](l); // copy over from smaller subset
columnSubset(columnSubset.n_rows - 1) = j; // add new covariate
newPValue = calculateBetaPValue(Z.cols(columnSubset), Y, sigma, varianceKnown);
if (bestPValues[k].first > newPValue.first) { // if better subset replace
bestPValues[k] = newPValue;
bestColumns[k] = columnSubset;
}
}
if (newPValue.first <= 0.05) { // stop when we reach significance
done = true;
smallestSubsetSize = k;
}
}
if (done) {
// compute balance p value in special case that only 1 covariate was needed
double balancePValue = -1;
if (smallestSubsetSize == 1 && !interceptTerm) {
balancePValue = testBalance(Z.col(bestColumns[1](1)), Z.col(0));
} else if (smallestSubsetSize == 1 && interceptTerm) {
balancePValue = testBalance(Z.col(bestColumns[1](2)), Z.col(0));
}
return make_tuple(bestPValues.front().first, bestPValues[smallestSubsetSize].second,
j, smallestSubsetSize, balancePValue, bestPValues[smallestSubsetSize].first);
}
}
}
return make_tuple(bestPValues.front().first, bestPValues.front().second, -1, -1, -1, bestPValues.front().first);
}
