protected void calcCounts(CoxPHModel model, final CoxPHTask coxMR) {
CoxPHModel.CoxPHParameters p = model._parms;
CoxPHModel.CoxPHOutput o = model._output;
o.n_missing = o.n - coxMR.n;
o.n = coxMR.n;
for (int j = 0; j < o.x_mean_cat.length; j++)
o.x_mean_cat[j] = coxMR.sumWeightedCatX[j] / coxMR.sumWeights;
for (int j = 0; j < o.x_mean_num.length; j++)
o.x_mean_num[j] = coxMR.dinfo()._normSub[j] + coxMR.sumWeightedNumX[j] / coxMR.sumWeights;
System.arraycopy(
coxMR.dinfo()._normSub, o.x_mean_num.length, o.mean_offset, 0, o.mean_offset.length);
int nz = 0;
for (int t = 0; t < coxMR.countEvents.length; ++t) {
o.total_event += coxMR.countEvents[t];
if (coxMR.sizeEvents[t] > 0 || coxMR.sizeCensored[t] > 0) {
o.time[nz] = o.min_time + t;
o.n_risk[nz] = coxMR.sizeRiskSet[t];
o.n_event[nz] = coxMR.sizeEvents[t];
o.n_censor[nz] = coxMR.sizeCensored[t];
nz++;
}
}
if (p.start_column == null)
for (int t = o.n_risk.length - 2; t >= 0; --t) o.n_risk[t] += o.n_risk[t + 1];
}
protected void calcModelStats(
CoxPHModel model, final double[] newCoef, final double newLoglik) {
CoxPHModel.CoxPHParameters p = model._parms;
CoxPHModel.CoxPHOutput o = model._output;
final int n_coef = o.coef.length;
final Matrix inv_hessian = new Matrix(o.hessian).inverse();
for (int j = 0; j < n_coef; ++j) {
for (int k = 0; k <= j; ++k) {
final double elem = -inv_hessian.get(j, k);
o.var_coef[j][k] = elem;
o.var_coef[k][j] = elem;
}
}
for (int j = 0; j < n_coef; ++j) {
o.coef[j] = newCoef[j];
o.exp_coef[j] = Math.exp(o.coef[j]);
o.exp_neg_coef[j] = Math.exp(-o.coef[j]);
o.se_coef[j] = Math.sqrt(o.var_coef[j][j]);
o.z_coef[j] = o.coef[j] / o.se_coef[j];
}
if (o.iter == 0) {
o.null_loglik = newLoglik;
o.maxrsq = 1 - Math.exp(2 * o.null_loglik / o.n);
o.score_test = 0;
for (int j = 0; j < n_coef; ++j) {
double sum = 0;
for (int k = 0; k < n_coef; ++k) sum += o.var_coef[j][k] * o.gradient[k];
o.score_test += o.gradient[j] * sum;
}
}
o.loglik = newLoglik;
o.loglik_test = -2 * (o.null_loglik - o.loglik);
o.rsq = 1 - Math.exp(-o.loglik_test / o.n);
o.wald_test = 0;
for (int j = 0; j < n_coef; ++j) {
double sum = 0;
for (int k = 0; k < n_coef; ++k) sum -= o.hessian[j][k] * (o.coef[k] - p.init);
o.wald_test += (o.coef[j] - p.init) * sum;
}
}
protected void initStats(final CoxPHModel model, final DataInfo dinfo) {
CoxPHModel.CoxPHParameters p = model._parms;
CoxPHModel.CoxPHOutput o = model._output;
o.n = p.stop_column.length();
o.data_info = dinfo;
final int n_offsets = (p.offset_columns == null) ? 0 : p.offset_columns.length;
final int n_coef = o.data_info.fullN() - n_offsets;
final String[] coefNames = o.data_info.coefNames();
o.coef_names = new String[n_coef];
System.arraycopy(coefNames, 0, o.coef_names, 0, n_coef);
o.coef = MemoryManager.malloc8d(n_coef);
o.exp_coef = MemoryManager.malloc8d(n_coef);
o.exp_neg_coef = MemoryManager.malloc8d(n_coef);
o.se_coef = MemoryManager.malloc8d(n_coef);
o.z_coef = MemoryManager.malloc8d(n_coef);
o.gradient = MemoryManager.malloc8d(n_coef);
o.hessian = malloc2DArray(n_coef, n_coef);
o.var_coef = malloc2DArray(n_coef, n_coef);
o.x_mean_cat = MemoryManager.malloc8d(n_coef - (o.data_info._nums - n_offsets));
o.x_mean_num = MemoryManager.malloc8d(o.data_info._nums - n_offsets);
o.mean_offset = MemoryManager.malloc8d(n_offsets);
o.offset_names = new String[n_offsets];
System.arraycopy(coefNames, n_coef, o.offset_names, 0, n_offsets);
final Vec start_column = p.start_column;
final Vec stop_column = p.stop_column;
o.min_time =
p.start_column == null ? (long) stop_column.min() : (long) start_column.min() + 1;
o.max_time = (long) stop_column.max();
final int n_time = new Vec.CollectDomain().doAll(stop_column).domain().length;
o.time = MemoryManager.malloc8(n_time);
o.n_risk = MemoryManager.malloc8d(n_time);
o.n_event = MemoryManager.malloc8d(n_time);
o.n_censor = MemoryManager.malloc8d(n_time);
o.cumhaz_0 = MemoryManager.malloc8d(n_time);
o.var_cumhaz_1 = MemoryManager.malloc8d(n_time);
o.var_cumhaz_2 = malloc2DArray(n_time, n_coef);
}
