static void
drop_redundant_vars (MODEL *pmod, DATASET *dset, gretl_matrix *R,
int rank, gretlopt opt)
{
int *droplist = NULL;
int i, vi, pos, nd;
double d;
#if REDEBUG
printlist(pmod->list, "pmod->list, into drop_redundant_vars");
fprintf(stderr, "rank = %d\n", rank);
#endif
pos = 2;
nd = 0;
for (i=0; i<R->rows; i++) {
d = gretl_matrix_get(R, i, i);
if (fabs(d) < R_DIAG_MIN) {
vi = pmod->list[pos];
gretl_list_append_term(&droplist, vi);
fprintf(stderr, "dropping redundant variable %d (%s)\n",
vi, dset->varname[vi]);
gretl_list_delete_at_pos(pmod->list, pos--);
nd++;
}
pos++;
}
pmod->ncoeff -= nd;
pmod->dfd = pmod->nobs - pmod->ncoeff;
pmod->dfn = pmod->ncoeff - pmod->ifc;
if (droplist != NULL) {
gretl_model_set_list_as_data(pmod, "droplist", droplist);
}
}
static int finalize_midas_model (MODEL *pmod,
const int *list,
const char *param,
const DATASET *dset,
midas_info *minfo,
int nmidas,
int *xlist,
int ldepvar,
int hfslopes)
{
int type0, mixed = 0;
int i, err = 0;
gretl_model_set_string_as_data(pmod, "midas_spec",
gretl_strdup(param));
if (pmod->ci == OLS) {
/* @pmod is the result of OLS estimation */
int vi;
gretl_model_allocate_param_names(pmod, pmod->ncoeff);
for (i=0; i<pmod->ncoeff; i++) {
vi = pmod->list[i+2];
gretl_model_set_param_name(pmod, i, dset->varname[vi]);
}
gretl_model_set_int(pmod, "umidas", 1);
} else {
/* @pmod is the result of NLS estimation */
free(pmod->depvar);
pmod->depvar = gretl_strdup(dset->varname[list[1]]);
free(pmod->list);
pmod->list = gretl_list_copy(list);
}
pmod->ci = MIDASREG;
/* record list of low-frequency regressors */
gretl_model_set_list_as_data(pmod, "lfxlist", xlist);
if (ldepvar) {
gretl_model_set_int(pmod, "dynamic", 1);
}
/* record the (common) type of MIDAS term? */
type0 = minfo[0].type;
if (nmidas > 1) {
for (i=1; i<nmidas; i++) {
if (minfo[i].type != type0) {
mixed = 1;
break;
}
}
}
if (!mixed && type0 > 0) {
gretl_model_set_int(pmod, "midas_type", type0);
}
if (nmidas == 1) {
/* Record the "gross" MIDAS coefficients, to enable
drawing of a plot? We'll do this only if we have
a single MIDAS term, which is probably the most
most common case. Otherwise it becomes too hard
to get the plot right.
*/
int nx = list[0] - 1;
const double *b = pmod->coeff + nx;
add_midas_plot_matrix(pmod, &minfo[0], b);
}
if (!err) {
err = model_add_minfo_array(pmod, minfo, nmidas);
}
return err;
}
