swh:1:snp:e54c5a53e4a16dbf51a30f8ff6154320b593eadb
Tip revision: fc209a6a2ac5055c14dced7fa571148c6b9e743b authored by Filip Borowicz on 05 March 2006, 00:00:00 UTC
version1.0
version1.0
Tip revision: fc209a6
re.c
/*
* Copyright (C) 2006 Filip Borowicz
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 59 Temple Place, Suite 330, Boston,
* MA 02111-1307 USA
*/
/**
* Calculates the value of the negative robust log-likelihood function
* and the first and secod derivative at parameter value beta
*
* @parma the beta vector
* @parma the vector of times
* @parma the vector of censoring indicators
* @parma the covariate matrix z
* @parma the vector of exp(prev_beta'z) (prev_beta - beta obatined through previous step)
* @parma the M value
* @parma the number of individuals
* @parma the number of covariates
* @parma the type of weighting function
* @parma the value of the negative robust log-likelihood function
* @parma the first derivative of the negative robust log-likelihood function
* @parma the second derivative of the negative robust log-likelihood function
*
* @return void
*/
#include "coxrobust.h"
void re(double *beta, double *time, int *status, double *covar,
double *prev_exp_zbeta, double *M, int*n_row, int *n_col,
int *a_type, double *res, double *gradient, double *hessian) {
int i, ii, j, k;
double sum1, a_ii, tmp;
double *sum2, *zbeta, *tmp_mat;
double *exp_zbeta;
double **z, **sum3, **hess;
WEIGHT_FUNCTION A;
A = get_weight_function(*a_type);
zbeta = (double *)R_alloc(*n_row, sizeof(double));
exp_zbeta = (double *)R_alloc(*n_row, sizeof(double));
sum2 = (double *)R_alloc(*n_col, sizeof(double));
tmp_mat = (double *)R_alloc(*n_col * *n_col, sizeof(double));
z = dmatrix(covar, *n_row, *n_col);
sum3 = dmatrix(tmp_mat, *n_col, *n_col);
hess = dmatrix(hessian, *n_col, *n_col);
sum1 = 0;
*res = 0;
for (j=0; j<*n_col; j++) {
gradient[j] = 0;
}
for (i=*n_row-1; i>=0; i--) {
zbeta[i] = 0;
for (j=0; j<*n_col; j++) {
zbeta[i] += beta[j] * z[j][i];
sum2[j] = 0;
for (k=0; k<=j; k++) {
sum3[j][k] = 0;
}
}
exp_zbeta[i] = exp(zbeta[i]);
if ( status[i] != 0 ) {
a_ii = A(time[i], prev_exp_zbeta[i], *M);
sum1 = 0;
for (ii=i; ii<*n_row; ii++) {
tmp = A(time[i], prev_exp_zbeta[ii], *M)*exp_zbeta[ii];
sum1 += tmp;
for (j=0; j<*n_col; j++) {
sum2[j] += z[j][ii] * tmp;
for (k=0; k<=j; k++) {
sum3[j][k] += z[j][ii] * z[k][ii] * tmp;
}
}
}
if ( sum1 == 0 ) {
sum1 = 1.0;
}
*res += a_ii*(log(sum1) - zbeta[i]);
for (j=0; j<*n_col; j++) {
tmp = sum2[j]/sum1;
gradient[j] += (tmp - z[j][i])*a_ii;
for (k=0; k<=j; k++) {
hess[j][k] += ( (sum3[j][k] - tmp*sum2[k])/sum1 ) * a_ii;
}
}
}
}
for (j=1; j<*n_col; j++) {
for (k=0; k<j; k++) {
hess[k][j] = hess[j][k];
}
}
}