02110-1301, USA. */
#include <config.h>
-#include <stdlib.h>
+
#include <gsl/gsl_cdf.h>
-#include <gsl/gsl_vector.h>
#include <gsl/gsl_matrix.h>
+#include <gsl/gsl_vector.h>
#include <math.h>
-#include <libpspp/alloc.h>
+#include <stdlib.h>
+
+#include "regression-export.h"
#include <data/case.h>
#include <data/casefile.h>
-#include <data/category.h>
#include <data/cat-routines.h>
-#include <language/command.h>
-#include <libpspp/compiler.h>
-#include <math/design-matrix.h>
+#include <data/category.h>
#include <data/dictionary.h>
-#include <libpspp/message.h>
+#include <data/missing-values.h>
+#include <data/procedure.h>
+#include <data/transformations.h>
+#include <data/value-labels.h>
+#include <data/variable.h>
+#include <language/command.h>
+#include <language/dictionary/split-file.h>
#include <language/data-io/file-handle.h>
-#include "gettext.h"
#include <language/lexer/lexer.h>
+#include <libpspp/alloc.h>
+#include <libpspp/compiler.h>
+#include <libpspp/message.h>
+#include <math/design-matrix.h>
+#include <math/coefficient.h>
#include <math/linreg/linreg.h>
-#include <math/linreg/coefficient.h>
-#include <data/missing-values.h>
-#include "regression-export.h"
#include <output/table.h>
-#include <data/value-labels.h>
-#include <data/variable.h>
-#include <procedure.h>
+
+#include "gettext.h"
#define REG_LARGE_DATA 1000
/* (specification)
"REGRESSION" (regression_):
*variables=custom;
- statistics[st_]=r,
- coeff,
- anova,
- outs,
- zpp,
- label,
- sha,
- ci,
- bcov,
- ses,
- xtx,
- collin,
- tol,
- selection,
- f,
- defaults,
- all;
+ +statistics[st_]=r,
+ coeff,
+ anova,
+ outs,
+ zpp,
+ label,
+ sha,
+ ci,
+ bcov,
+ ses,
+ xtx,
+ collin,
+ tol,
+ selection,
+ f,
+ defaults,
+ all;
export=custom;
^dependent=varlist;
- save=residuals;
- method=enter.
+ +save[sv_]=resid,pred;
+ +method=enter.
*/
/* (declarations) */
/* (functions) */
/* Linear regression models. */
pspp_linreg_cache **models = NULL;
+/*
+ Transformations for saving predicted values
+ and residuals, etc.
+ */
+struct reg_trns
+{
+ int n_trns; /* Number of transformations. */
+ int trns_id; /* Which trns is this one? */
+ pspp_linreg_cache *c; /* Linear model for this trns. */
+};
/*
Variables used (both explanatory and response).
*/
*/
int pspp_reg_rc = CMD_SUCCESS;
-static bool run_regression (const struct casefile *, void *);
+static bool run_regression (const struct ccase *,
+ const struct casefile *, void *);
/*
STATISTICS subcommand output functions.
tab_text (t, 5, 0, TAB_CENTER | TAT_TITLE, _("t"));
tab_text (t, 6, 0, TAB_CENTER | TAT_TITLE, _("Significance"));
tab_text (t, 1, 1, TAB_LEFT | TAT_TITLE, _("(Constant)"));
- coeff = c->coeff[0].estimate;
+ coeff = c->coeff[0]->estimate;
tab_float (t, 2, 1, 0, coeff, 10, 2);
std_err = sqrt (gsl_matrix_get (c->cov, 0, 0));
tab_float (t, 3, 1, 0, std_err, 10, 2);
tab_float (t, 6, 1, 0, pval, 10, 2);
for (j = 1; j <= c->n_indeps; j++)
{
- v = pspp_linreg_coeff_get_var (c->coeff + j, 0);
+ v = pspp_coeff_get_var (c->coeff[j], 0);
label = var_to_string (v);
/* Do not overwrite the variable's name. */
strncpy (tmp, label, MAX_STRING);
for that value.
*/
- val = pspp_linreg_coeff_get_value (c->coeff + j, v);
+ val = pspp_coeff_get_value (c->coeff[j], v);
val_s = value_to_string (val, v);
strncat (tmp, val_s, MAX_STRING);
}
/*
Regression coefficients.
*/
- coeff = c->coeff[j].estimate;
+ coeff = c->coeff[j]->estimate;
tab_float (t, 2, j + 1, 0, coeff, 10, 2);
/*
Standard error of the coefficients.
tab_text (t, 1, 1, TAB_CENTER | TAT_TITLE, _("Covariances"));
for (i = 1; i < c->n_coeffs; i++)
{
- const struct variable *v = pspp_linreg_coeff_get_var (c->coeff + i, 0);
+ const struct variable *v = pspp_coeff_get_var (c->coeff[i], 0);
label = var_to_string (v);
tab_text (t, 2, i, TAB_CENTER, label);
tab_text (t, i + 2, 0, TAB_CENTER, label);
statistics_keyword_output (reg_stats_tol, keywords[tol], c);
statistics_keyword_output (reg_stats_selection, keywords[selection], c);
}
+
+/*
+ Free the transformation. Free its linear model if this
+ transformation is the last one.
+ */
+static bool
+regression_trns_free (void *t_)
+{
+ bool result = true;
+ struct reg_trns *t = t_;
+
+ if (t->trns_id == t->n_trns)
+ {
+ result = pspp_linreg_cache_free (t->c);
+ }
+ free (t);
+
+ return result;
+}
+
+/*
+ Gets the predicted values.
+ */
static int
-regression_trns_proc (void *m, struct ccase *c, int case_idx UNUSED)
+regression_trns_pred_proc (void *t_, struct ccase *c, int case_idx UNUSED)
{
size_t i;
- size_t n_vars;
- size_t n_vals = 0;
- pspp_linreg_cache *model = m;
- union value *output;
+ size_t n_vals;
+ struct reg_trns *trns = t_;
+ pspp_linreg_cache *model;
+ union value *output = NULL;
+ const union value **vals = NULL;
+ struct variable **vars = NULL;
+
+ assert (trns != NULL);
+ model = trns->c;
+ assert (model != NULL);
+ assert (model->depvar != NULL);
+ assert (model->pred != NULL);
+
+ vars = xnmalloc (model->n_coeffs, sizeof (*vars));
+ n_vals = (*model->get_vars) (model, vars);
+
+ vals = xnmalloc (n_vals, sizeof (*vals));
+ output = case_data_rw (c, model->pred->fv);
+ assert (output != NULL);
+
+ for (i = 0; i < n_vals; i++)
+ {
+ vals[i] = case_data (c, vars[i]->fv);
+ }
+ output->f = (*model->predict) ((const struct variable **) vars,
+ vals, model, n_vals);
+ free (vals);
+ free (vars);
+ return TRNS_CONTINUE;
+}
+
+/*
+ Gets the residuals.
+ */
+static int
+regression_trns_resid_proc (void *t_, struct ccase *c, int case_idx UNUSED)
+{
+ size_t i;
+ size_t n_vals;
+ struct reg_trns *trns = t_;
+ pspp_linreg_cache *model;
+ union value *output = NULL;
const union value **vals = NULL;
const union value *obs = NULL;
struct variable **vars = NULL;
-
+
+ assert (trns != NULL);
+ model = trns->c;
assert (model != NULL);
assert (model->depvar != NULL);
assert (model->resid != NULL);
-
- dict_get_vars (default_dict, &vars, &n_vars, 1u << DC_SYSTEM);
- vals = xnmalloc (n_vars, sizeof (*vals));
- assert (vals != NULL);
+
+ vars = xnmalloc (model->n_coeffs, sizeof (*vars));
+ n_vals = (*model->get_vars) (model, vars);
+
+ vals = xnmalloc (n_vals, sizeof (*vals));
output = case_data_rw (c, model->resid->fv);
assert (output != NULL);
- for (i = 0; i < n_vars; i++)
+ for (i = 0; i < n_vals; i++)
{
- /* Do not use the residual variable. */
- if (vars[i]->index != model->resid->index)
- {
- /* Do not use the dependent variable as a predictor. */
- if (vars[i]->index == model->depvar->index)
- {
- obs = case_data (c, i);
- assert (obs != NULL);
- }
- else
- {
- vals[i] = case_data (c, i);
- n_vals++;
- }
- }
+ vals[i] = case_data (c, vars[i]->fv);
}
- output->f = (*model->residual) ((const struct variable **) vars,
+ obs = case_data (c, model->depvar->fv);
+ output->f = (*model->residual) ((const struct variable **) vars,
vals, obs, model, n_vals);
free (vals);
+ free (vars);
return TRNS_CONTINUE;
}
+
+/*
+ Returns 0 if NAME is a duplicate of any existing variable name.
+*/
+static int
+try_name (char *name)
+{
+ if (dict_lookup_var (default_dict, name) != NULL)
+ return 0;
+
+ return 1;
+}
static void
-subcommand_save (int save, pspp_linreg_cache **models)
+reg_get_name (char name[LONG_NAME_LEN], const char prefix[LONG_NAME_LEN])
+{
+ int i = 1;
+
+ snprintf (name, LONG_NAME_LEN, "%s%d", prefix, i);
+ while (!try_name (name))
+ {
+ i++;
+ snprintf (name, LONG_NAME_LEN, "%s%d", prefix, i);
+ }
+}
+static void
+reg_save_var (const char *prefix, trns_proc_func * f,
+ pspp_linreg_cache * c, struct variable **v, int n_trns)
+{
+ static int trns_index = 1;
+ char name[LONG_NAME_LEN];
+ struct variable *new_var;
+ struct reg_trns *t = NULL;
+
+ t = xmalloc (sizeof (*t));
+ t->trns_id = trns_index;
+ t->n_trns = n_trns;
+ t->c = c;
+ reg_get_name (name, prefix);
+ new_var = dict_create_var (default_dict, name, 0);
+ assert (new_var != NULL);
+ *v = new_var;
+ add_transformation (f, regression_trns_free, t);
+ trns_index++;
+}
+static void
+subcommand_save (int save, pspp_linreg_cache ** models)
{
- struct variable *residuals = NULL;
pspp_linreg_cache **lc;
+ int n_trns = 0;
+ int i;
assert (models != NULL);
if (save)
{
+ /* Count the number of transformations we will need. */
+ for (i = 0; i < REGRESSION_SV_count; i++)
+ {
+ if (cmd.a_save[i])
+ {
+ n_trns++;
+ }
+ }
+ n_trns *= cmd.n_dependent;
+
for (lc = models; lc < models + cmd.n_dependent; lc++)
{
assert (*lc != NULL);
assert ((*lc)->depvar != NULL);
- residuals = dict_create_var (default_dict, "residuals", 0);
- assert (residuals != NULL);
- (*lc)->resid = residuals;
- add_transformation (regression_trns_proc, pspp_linreg_cache_free, *lc);
+ if (cmd.a_save[REGRESSION_SV_RESID])
+ {
+ reg_save_var ("RES", regression_trns_resid_proc, *lc,
+ &(*lc)->resid, n_trns);
+ }
+ if (cmd.a_save[REGRESSION_SV_PRED])
+ {
+ reg_save_var ("PRED", regression_trns_pred_proc, *lc,
+ &(*lc)->pred, n_trns);
+ }
}
}
- else
+ else
{
for (lc = models; lc < models + cmd.n_dependent; lc++)
{
size_t j;
int n_vars = 0;
struct variable **varlist;
- struct pspp_linreg_coeff *coeff;
+ struct pspp_coeff *coeff;
const struct variable *v;
union value *val;
varlist = xnmalloc (c->n_indeps, sizeof (*varlist));
for (i = 1; i < c->n_indeps; i++) /* c->coeff[0] is the intercept. */
{
- coeff = c->coeff + i;
- v = pspp_linreg_coeff_get_var (coeff, 0);
+ coeff = c->coeff[i];
+ v = pspp_coeff_get_var (coeff, 0);
if (v->type == ALPHA)
{
if (!reg_inserted (v, varlist, n_vars))
for (i = 0; i < n_vars; i++)
{
- coeff = c->coeff + i;
+ coeff = c->coeff[i];
fprintf (fp, "%s.name = \"%s\";\n\t", varlist[i]->name,
varlist[i]->name);
fprintf (fp, "%s.n_vals = %d;\n\t", varlist[i]->name,
reg_print_depvars (FILE * fp, pspp_linreg_cache * c)
{
int i;
- struct pspp_linreg_coeff *coeff;
+ struct pspp_coeff *coeff;
const struct variable *v;
fprintf (fp, "char *model_depvars[%d] = {", c->n_indeps);
for (i = 1; i < c->n_indeps; i++)
{
- coeff = c->coeff + i;
- v = pspp_linreg_coeff_get_var (coeff, 0);
+ coeff = c->coeff[i];
+ v = pspp_coeff_get_var (coeff, 0);
fprintf (fp, "\"%s\",\n\t\t", v->name);
}
- coeff = c->coeff + i;
- v = pspp_linreg_coeff_get_var (coeff, 0);
+ coeff = c->coeff[i];
+ v = pspp_coeff_get_var (coeff, 0);
fprintf (fp, "\"%s\"};\n\t", v->name);
}
static void
{
int i;
const struct variable *v;
-
+
for (i = 1; i < c->n_coeffs; i++)
{
- v = pspp_linreg_coeff_get_var (c->coeff + i, 0);
+ v = pspp_coeff_get_var (c->coeff[i], 0);
if (v->type == ALPHA)
{
return 1;
int n_quantiles = 100;
double increment;
double tmp;
- struct pspp_linreg_coeff coeff;
+ struct pspp_coeff *coeff;
if (export)
{
for (i = 1; i < c->n_indeps; i++)
{
coeff = c->coeff[i];
- fprintf (fp, "%.15e,\n\t\t", coeff.estimate);
+ fprintf (fp, "%.15e,\n\t\t", coeff->estimate);
}
coeff = c->coeff[i];
- fprintf (fp, "%.15e};\n\t", coeff.estimate);
+ fprintf (fp, "%.15e};\n\t", coeff->estimate);
coeff = c->coeff[0];
- fprintf (fp, "double estimate = %.15e;\n\t", coeff.estimate);
+ fprintf (fp, "double estimate = %.15e;\n\t", coeff->estimate);
fprintf (fp, "int i;\n\tint j;\n\n\t");
fprintf (fp, "for (i = 0; i < %d; i++)\n\t", c->n_indeps);
fprintf (fp, "%s", reg_getvar);
}
}
static int
-regression_custom_export (struct cmd_regression *cmd UNUSED)
+regression_custom_export (struct cmd_regression *cmd UNUSED, void *aux UNUSED)
{
/* 0 on failure, 1 on success, 2 on failure that should result in syntax error */
if (!lex_force_match ('('))
int
cmd_regression (void)
{
- if (!parse_regression (&cmd))
+ if (!parse_regression (&cmd, NULL))
return CMD_FAILURE;
models = xnmalloc (cmd.n_dependent, sizeof *models);
is_depvar (size_t k, const struct variable *v)
{
/*
- compare_var_names returns 0 if the variable
- names match.
- */
+ compare_var_names returns 0 if the variable
+ names match.
+ */
if (!compare_var_names (v, v_variables[k], NULL))
return 1;
/* Parser for the variables sub command */
static int
-regression_custom_variables(struct cmd_regression *cmd UNUSED)
+regression_custom_variables (struct cmd_regression *cmd UNUSED,
+ void *aux UNUSED)
{
- lex_match('=');
+ lex_match ('=');
if ((token != T_ID || dict_lookup_var (default_dict, tokid) == NULL)
&& token != T_ALL)
return 2;
-
- if (!parse_variables (default_dict, &v_variables, &n_variables,
- PV_NONE ))
+
+ if (!parse_variables (default_dict, &v_variables, &n_variables, PV_NONE))
{
free (v_variables);
return 0;
}
- assert(n_variables);
+ assert (n_variables);
return 1;
}
+
/*
Count the explanatory variables. The user may or may
not have specified a response variable in the syntax.
*/
-static
-int get_n_indep (const struct variable *v)
+static int
+get_n_indep (const struct variable *v)
{
int result;
int i = 0;
}
return result;
}
+
/*
Read from the active file. Identify the explanatory variables in
v_variables. Encode categorical variables. Drop cases with missing
values.
*/
-static
-int prepare_data (int n_data, int is_missing_case[],
- struct variable **indep_vars,
- struct variable *depvar,
- const struct casefile *cf)
+static int
+prepare_data (int n_data, int is_missing_case[],
+ struct variable **indep_vars,
+ struct variable *depvar, const struct casefile *cf)
{
int i;
int j;
assert (indep_vars != NULL);
j = 0;
for (i = 0; i < n_variables; i++)
- {
+ {
if (!is_depvar (i, depvar))
{
indep_vars[j] = v_variables[i];
if (v_variables[i]->type == ALPHA)
{
/* Make a place to hold the binary vectors
- corresponding to this variable's values. */
+ corresponding to this variable's values. */
cat_stored_values_create (v_variables[i]);
}
- n_data = mark_missing_cases (cf, v_variables[i], is_missing_case, n_data);
+ n_data =
+ mark_missing_cases (cf, v_variables[i], is_missing_case, n_data);
}
}
/*
- Mark missing cases for the dependent variable.
+ Mark missing cases for the dependent variable.
*/
n_data = mark_missing_cases (cf, depvar, is_missing_case, n_data);
return n_data;
}
+static void
+coeff_init (pspp_linreg_cache *c, struct design_matrix *dm)
+{
+ c->coeff = xnmalloc (dm->m->size2 + 1, sizeof (*c->coeff));
+ c->coeff[0] = xmalloc (sizeof (*(c->coeff[0]))); /* The first coefficient is the intercept. */
+ c->coeff[0]->v_info = NULL; /* Intercept has no associated variable. */
+ pspp_coeff_init (c->coeff + 1, dm);
+}
static bool
-run_regression (const struct casefile *cf, void *cmd_ UNUSED)
+run_regression (const struct ccase *first,
+ const struct casefile *cf, void *cmd_ UNUSED)
{
size_t i;
- size_t n_data = 0; /* Number of valide cases. */
- size_t n_cases; /* Number of cases. */
+ size_t n_data = 0; /* Number of valide cases. */
+ size_t n_cases; /* Number of cases. */
size_t row;
size_t case_num;
int n_indep = 0;
pspp_linreg_opts lopts;
assert (models != NULL);
+
+ output_split_file_values (first);
+
if (!v_variables)
{
dict_get_vars (default_dict, &v_variables, &n_variables,
{
n_indep = get_n_indep ((const struct variable *) cmd.v_dependent[k]);
lopts.get_indep_mean_std = xnmalloc (n_indep, sizeof (int));
- indep_vars = xnmalloc (n_indep, sizeof *indep_vars);
+ indep_vars = xnmalloc (n_indep, sizeof *indep_vars);
assert (indep_vars != NULL);
for (i = 0; i < n_cases; i++)
{
is_missing_case[i] = 0;
}
- n_data = prepare_data (n_cases, is_missing_case, indep_vars,
- cmd.v_dependent[k],
+ n_data = prepare_data (n_cases, is_missing_case, indep_vars,
+ cmd.v_dependent[k],
(const struct casefile *) cf);
Y = gsl_vector_alloc (n_data);
for (i = 0; i < n_variables; ++i) /* Iterate over the
variables for the
current case.
- */
+ */
{
val = case_data (&c, v_variables[i]->fv);
/*
{
if (v_variables[i]->type == ALPHA)
{
- design_matrix_set_categorical (X, row, v_variables[i], val);
+ design_matrix_set_categorical (X, row,
+ v_variables[i], val);
}
else if (v_variables[i]->type == NUMERIC)
{
- design_matrix_set_numeric (X, row, v_variables[i], val);
+ design_matrix_set_numeric (X, row, v_variables[i],
+ val);
}
}
}
and store pointers to the variables that correspond to the
coefficients.
*/
- pspp_linreg_coeff_init (models[k], X);
-
+ coeff_init (models[k], X);
+
/*
Find the least-squares estimates and other statistics.
*/
projects / pspp-builds.git / blobdiff