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194 | #include <RcppArmadillo.h>
#include "c_optim.h"
namespace rstpm2 {
using namespace Rcpp;
using namespace arma;
// assume right censored values in ascending order of time
RcppExport SEXP test_cox_tvc2(SEXP args) {
List largs = as<List>(args);
vec time = as<vec>(largs["time"]); // length n
vec event = as<vec>(largs["event"]); // length n
mat X = as<mat>(largs["X"]); // design matrix, n*c
vec beta = as<vec>(largs["beta"]); // length c+1
int k = as<int>(largs["k"]); // column to use for tvc
int n = time.size();
int c = X.n_cols;
double llike = 0.0, lsum;
vec eta;
vec eta0 = X * beta(span(0,c-1));
for (int i=0; i<n; ++i) {
if (event(i) == 1 && (i==0 || time(i) != time(i-1))) {
eta = eta0(span(i,n-1)) + beta(c)*log(time(i))*X(span(i,n-1),k);
lsum = log(sum(exp(eta)));
for (int j=i; j<n && time(j)==time(i) && event(j)==1; ++j) {
llike += eta(j-i) - lsum;
}
}
}
return wrap(llike);
}
// Breslow estimator of the baseline cumulative hazard;
// assumes right censored values in ascending order of time
RcppExport SEXP test_cox_tvc2_H0(SEXP args) {
List largs = as<List>(args);
vec time = as<vec>(largs["time"]); // length n
vec event = as<vec>(largs["event"]); // length n
mat X = as<mat>(largs["X"]); // design matrix, n*c
vec beta = as<vec>(largs["beta"]); // length c+1
int k = as<int>(largs["k"]); // column to use for tvc
int n = time.size();
int c = X.n_cols;
double logHk, lsum;
std::vector<double> H, etimes;
vec eta;
vec eta0 = X * beta(span(0,c-1));
for (int i=0; i<n; ++i) {
if (event(i) == 1 && (i==0 || time(i) != time(i-1))) {
eta = eta0(span(i,n-1)) + beta(c)*log(time(i))*X(span(i,n-1),k);
logHk = 0.0;
lsum = sum(exp(eta));
for (int j=i; j<n && time(j)==time(i) && event(j)==1; ++j) {
logHk += 1 - lsum;
}
etimes.push_back(time(i));
H.push_back(exp(logHk));
}
}
return List::create(_("H")=wrap(H),_("times")=wrap(etimes));
}
// // Breslow estimator of the cumulative hazard for a given set of covariates;
// // assumes right censored values in ascending order of time
// RcppExport SEXP test_cox_tvc2_Hx0(SEXP args) {
// List largs = as<List>(args);
// vec time = as<vec>(largs["time"]); // length n
// vec event = as<vec>(largs["event"]); // length n
// mat X = as<mat>(largs["X"]); // design matrix, n*c
// vec x0 = as<vec>(largs["x0"]); // design row for specific covariate pattern, length c
// vec beta = as<vec>(largs["beta"]); // length c+1
// int k = as<int>(largs["k"]); // column to use for tvc
// int n = time.size();
// int c = X.n_cols;
// double logHk, lsum;
// std::vector<double> H, etimes;
// vec eta;
// vec eta1 = X * beta(span(0,c-1));
// double eta0;
// for (int i=0; i<n; ++i) {
// if (event(i) == 1 && (i==0 || time(i) != time(i-1))) {
// eta = eta1(span(i,n-1)) + beta(c)*log(time(i))*X(span(i,n-1),k);
// eta0 = x0 * beta(span(0,c-1)) + beta(c)*log(time(i))*x0(k);
// logHk = 0.0;
// lsum = sum(exp(eta));
// for (int j=i; j<n && time(j)==time(i) && event(j)==1; ++j) {
// logHk += eta0 - lsum;
// }
// etimes.push_back(time(i));
// H.push_back(exp(logHk));
// }
// }
// return List::create(_("H")=wrap(H),_("times")=wrap(etimes));
// }
RcppExport SEXP test_cox_tvc2_grad(SEXP args) {
List largs = as<List>(args);
vec time = as<vec>(largs["time"]); // length n
vec event = as<vec>(largs["event"]); // length n
mat X = as<mat>(largs["X"]); // one covariate, length n
vec beta = as<vec>(largs["beta"]); // length 2
int k = as<int>(largs["k"]); // column to use for tvc
int n = time.size();
int c = X.n_cols;
vec grad(beta.size(),fill::zeros);
vec lsum(beta.size(),fill::zeros);
vec eta0 = X * beta(span(0,c-1));
vec risk;
mat Xrisk;
for (int i=0; i<n; ++i) {
if (event(i) == 1 && (i==0 || time(i) != time(i-1))) {
risk = exp(eta0(span(i,n-1)) + beta(c)*log(time(i))*X(span(i,n-1),k));
Xrisk = X(span(i,n-1), span::all);
Xrisk.each_col() %= risk;
lsum(span(0,c-1)) = sum(Xrisk, 0).t() / sum(risk);
lsum(c) = sum(log(time(i))*Xrisk(span::all,k))/sum(risk);
for (int j=i; j<n && time(j)==time(i) && event(j)==1; ++j) {
grad(span(0,c-1)) += X(j,span::all).t() - lsum(span(0,c-1));
grad(c) += X(j,k)*log(time(i)) - lsum(c);
}
}
}
return wrap(grad);
}
struct Tvc {
int n;
vec time, event, x, beta;
};
double test_cox_tvc3_negll(int n, double * beta, void * e) {
Tvc * data = (Tvc *) e;
double llike = 0.0;
vec eta;
for (int i=0; i < data->n; ++i) {
if (data->event(i) == 1) {
eta = beta[0]*data->x(span(i,data->n-1)) +
beta[1]*log(data->time(i)) * data->x(span(i,data->n-1));
llike += eta(0) - log(sum(exp(eta)));
}
}
return -llike;
}
void test_cox_tvc3_negll_gr(int ncoef, double * beta, double * gr, void * e) {
Tvc * data = (Tvc *) e;
vec risk, subx;
gr[0] = gr[1] = 0.0;
for (int i=0; i<data->n; ++i) {
if (data->event(i) == 1) {
subx = data->x(span(i,data->n-1));
risk = exp(beta[0]*subx + beta[1]*log(data->time(i)) * subx);
gr[0] -= data->x(i) - sum(subx % risk)/sum(risk);
gr[1] -= data->x(i)*log(data->time(i)) - sum(log(data->time(i)) * subx % risk)/sum(risk);
}
}
}
RcppExport SEXP test_cox_tvc3(SEXP args) {
List largs = as<List>(args);
vec time = as<vec>(largs["time"]); // length n
vec event = as<vec>(largs["event"]); // length n
vec x = as<vec>(largs["x"]); // one covariate, length n
NumericVector beta = clone(as<NumericVector>(largs["beta"])); // length 2
int n = time.size();
Tvc data = {n, time, event, x, beta};
BFGS bfgs;
bfgs.optim(test_cox_tvc3_negll,test_cox_tvc3_negll_gr,beta, (void *) &data);
return wrap(List::create(_("coef")=bfgs.coef,
_("negll")=bfgs.Fmin,
_("hessian")=bfgs.hessian));
}
} // namespace
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