#include <gsl/gsl_matrix.h>
#include "alloc.h"
#include "case.h"
+#include "casefile.h"
+#include "cat.h"
+#include "command.h"
#include "dictionary.h"
+#include "error.h"
#include "file-handle.h"
-#include "command.h"
+#include "gettext.h"
#include "lexer.h"
+#include <linreg/pspp_linreg.h>
#include "tab.h"
#include "var.h"
#include "vfm.h"
-#include "casefile.h"
-#include <linreg/pspp_linreg.h>
-#include "cat.h"
+
/* (headers) */
*/
size_t *indep_vars;
+/*
+ Return value for the procedure.
+ */
+int pspp_reg_rc = CMD_SUCCESS;
+
static void run_regression (const struct casefile *, void *);
/*
STATISTICS subcommand output functions.
static void
reg_stats_r (pspp_linreg_cache * c)
{
+ struct tab_table *t;
+ int n_rows = 2;
+ int n_cols = 5;
+ double rsq;
+ double adjrsq;
+ double std_error;
+
assert (c != NULL);
+ rsq = c->ssm / c->sst;
+ adjrsq = 1.0 - (1.0 - rsq) * (c->n_obs - 1.0) / (c->n_obs - c->n_indeps);
+ std_error = sqrt ((c->n_indeps - 1.0) / (c->n_obs - 1.0));
+ t = tab_create (n_cols, n_rows, 0);
+ tab_dim (t, tab_natural_dimensions);
+ tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 0, n_cols - 1, n_rows - 1);
+ tab_hline (t, TAL_2, 0, n_cols - 1, 1);
+ tab_vline (t, TAL_2, 2, 0, n_rows - 1);
+ tab_vline (t, TAL_0, 1, 0, 0);
+
+ tab_text (t, 1, 0, TAB_CENTER | TAT_TITLE, _("R"));
+ tab_text (t, 2, 0, TAB_CENTER | TAT_TITLE, _("R Square"));
+ tab_text (t, 3, 0, TAB_CENTER | TAT_TITLE, _("Adjusted R Square"));
+ tab_text (t, 4, 0, TAB_CENTER | TAT_TITLE, _("Std. Error of the Estimate"));
+ tab_float (t, 1, 1, TAB_RIGHT, sqrt (rsq), 10, 2);
+ tab_float (t, 2, 1, TAB_RIGHT, rsq, 10, 2);
+ tab_float (t, 3, 1, TAB_RIGHT, adjrsq, 10, 2);
+ tab_float (t, 4, 1, TAB_RIGHT, std_error, 10, 2);
+ tab_title (t, 0, _("Model Summary"));
+ tab_submit (t);
}
/*
struct tab_table *t;
assert (c != NULL);
- n_rows = 2 + c->param_estimates->size;
+ n_rows = 2;
+
t = tab_create (n_cols, n_rows, 0);
tab_headers (t, 2, 0, 1, 0);
tab_dim (t, tab_natural_dimensions);
static void
reg_stats_bcov (pspp_linreg_cache * c)
{
+ int n_cols;
+ int n_rows;
+ int i;
+ int j;
+ int k;
+ int row;
+ int col;
+ const char *label;
+ struct tab_table *t;
+
assert (c != NULL);
+ n_cols = c->n_indeps + 1 + 2;
+ n_rows = 2 * (c->n_indeps + 1);
+ t = tab_create (n_cols, n_rows, 0);
+ tab_headers (t, 2, 0, 1, 0);
+ tab_dim (t, tab_natural_dimensions);
+ tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 0, n_cols - 1, n_rows - 1);
+ tab_hline (t, TAL_2, 0, n_cols - 1, 1);
+ tab_vline (t, TAL_2, 2, 0, n_rows - 1);
+ tab_vline (t, TAL_0, 1, 0, 0);
+ tab_text (t, 0, 0, TAB_CENTER | TAT_TITLE, _("Model"));
+ tab_text (t, 1, 1, TAB_CENTER | TAT_TITLE, _("Covariances"));
+ for (i = 1; i < c->n_indeps + 1; i++)
+ {
+ j = indep_vars[(i - 1)];
+ struct variable *v = cmd.v_variables[j];
+ label = var_to_string (v);
+ tab_text (t, 2, i, TAB_CENTER, label);
+ tab_text (t, i + 2, 0, TAB_CENTER, label);
+ for (k = 1; k < c->n_indeps + 1; k++)
+ {
+ col = (i <= k) ? k : i;
+ row = (i <= k) ? i : k;
+ tab_float (t, k + 2, i, TAB_CENTER,
+ gsl_matrix_get (c->cov, row, col), 8, 3);
+ }
+ }
+ tab_title (t, 0, _("Coefficient Correlations"));
+ tab_submit (t);
}
static void
reg_stats_ses (pspp_linreg_cache * c)
*/
for (i = 0; i < f; i++)
{
- *(keywords + i) = 1;
+ keywords[i] = 1;
}
}
else
*/
if (keywords[defaults] | d)
{
- *(keywords + anova) = 1;
- *(keywords + outs) = 1;
- *(keywords + coeff) = 1;
- *(keywords + r) = 1;
+ keywords[anova] = 1;
+ keywords[outs] = 1;
+ keywords[coeff] = 1;
+ keywords[r] = 1;
}
}
statistics_keyword_output (reg_stats_r, keywords[r], c);
}
multipass_procedure_with_splits (run_regression, &cmd);
- return CMD_SUCCESS;
+ return pspp_reg_rc;
}
/*
}
static void
-run_regression (const struct casefile *cf, void *cmd_)
+run_regression (const struct casefile *cf, void *cmd_ UNUSED)
{
size_t i;
size_t k;
size_t n_data = 0;
size_t row;
int n_indep;
+ int j = 0;
const union value *val;
struct casereader *r;
struct casereader *r2;
n_data = casefile_get_case_cnt (cf);
n_indep = cmd.n_variables - cmd.n_dependent;
- indep_vars = (size_t *) malloc (n_indep * sizeof (*indep_vars));
+ indep_vars = xnmalloc (n_indep, sizeof *indep_vars);
Y = gsl_vector_alloc (n_data);
lopts.get_depvar_mean_std = 1;
- lopts.get_indep_mean_std = (int *) malloc (n_indep * sizeof (int));
+ lopts.get_indep_mean_std = xnmalloc (n_indep, sizeof (int));
lcache = pspp_linreg_cache_alloc (n_data, n_indep);
lcache->indep_means = gsl_vector_alloc (n_indep);
*/
if (is_depvar (i))
{
- assert(v->type == NUMERIC);
+ if (v->type != NUMERIC)
+ {
+ msg (SE, gettext ("Dependent variable must be numeric."));
+ pspp_reg_rc = CMD_FAILURE;
+ return;
+ }
+ lcache->depvar = (const struct var *) v;
gsl_vector_set (Y, row, val->f);
}
else
}
}
}
+ /*
+ Now that we know the number of coefficients, allocate space
+ and store pointers to the variables that correspond to the
+ coefficients.
+ */
+ lcache->coeff = xnmalloc (X->m->size2 + 1, sizeof (*lcache->coeff));
+ for (i = 0; i < X->m->size2; i++)
+ {
+ j = i + 1; /* The first coeff is the intercept. */
+ lcache->coeff[j].v =
+ (const struct variable *) design_matrix_col_to_var (X, i);
+ }
/*
Find the least-squares estimates and other statistics.
*/
free (lopts.get_indep_mean_std);
free (indep_vars);
casereader_destroy (r);
+ return;
}
/*
projects / pspp-builds.git / blobdiff