/* PSPP - a program for statistical analysis.
- Copyright (C) 2007 Free Software Foundation, Inc.
+ Copyright (C) 2007, 2009 Free Software Foundation, Inc.
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
#include <language/dictionary/split-file.h>
#include <language/data-io/file-handle.h>
#include <language/lexer/lexer.h>
-#include <libpspp/alloc.h>
#include <libpspp/compiler.h>
+#include <libpspp/hash.h>
#include <libpspp/message.h>
-#include <math/design-matrix.h>
+#include <math/covariance-matrix.h>
#include <math/coefficient.h>
-#include <math/linreg/linreg.h>
+#include <math/linreg.h>
#include <math/moments.h>
#include <output/table.h>
+#include "xalloc.h"
#include "gettext.h"
-#define GLM_LARGE_DATA 1000
-
/* (headers) */
/* (specification)
"GLM" (glm_):
*dependent=custom;
+ design=custom;
by=varlist;
with=varlist.
*/
/* (functions) */
static struct cmd_glm cmd;
+
/*
Moments for each of the variables used.
*/
struct moments_var
{
struct moments1 *m;
+ double *weight;
+ double *mean;
+ double *variance;
const struct variable *v;
};
*/
static size_t n_dependent;
-/*
- Return value for the procedure.
- */
-static int pspp_glm_rc = CMD_SUCCESS;
+size_t n_inter; /* Number of interactions. */
+size_t n_members; /* Number of memebr variables in an interaction. */
+
+struct interaction_variable **interactions;
+
+int cmd_glm (struct lexer *lexer, struct dataset *ds);
-static bool run_glm (struct casereader*,
+static bool run_glm (struct casereader *,
struct cmd_glm *,
- const struct dataset *,
- pspp_linreg_cache *);
+ const struct dataset *);
int
cmd_glm (struct lexer *lexer, struct dataset *ds)
{
struct casegrouper *grouper;
struct casereader *group;
- pspp_linreg_cache *model = NULL;
bool ok;
- model = xmalloc (sizeof *model);
-
if (!parse_glm (lexer, ds, &cmd, NULL))
return CMD_FAILURE;
grouper = casegrouper_create_splits (proc_open (ds), dataset_dict (ds));
while (casegrouper_get_next_group (grouper, &group))
{
- run_glm (group, &cmd, ds, model);
+ run_glm (group, &cmd, ds);
}
ok = casegrouper_destroy (grouper);
ok = proc_commit (ds) && ok;
- free (model);
free (v_dependent);
return ok ? CMD_SUCCESS : CMD_FAILURE;
}
+static int
+parse_interactions (struct lexer *lexer, const struct variable **interaction_vars, int n_members,
+ int max_members, struct dataset *ds)
+{
+ if (lex_match (lexer, '*'))
+ {
+ if (n_members > max_members)
+ {
+ max_members *= 2;
+ xnrealloc (interaction_vars, max_members, sizeof (*interaction_vars));
+ }
+ interaction_vars[n_members] = parse_variable (lexer, dataset_dict (ds));
+ parse_interactions (lexer, interaction_vars, n_members++, max_members, ds);
+ }
+ return n_members;
+}
+/* Parser for the design subcommand. */
+static int
+glm_custom_design (struct lexer *lexer, struct dataset *ds,
+ struct cmd_glm *cmd UNUSED, void *aux UNUSED)
+{
+ size_t n_inter = 0;
+ size_t n_allocated = 2;
+ size_t n_members;
+ struct variable **interaction_vars;
+ struct variable *this_var;
+ interactions = xnmalloc (n_allocated, sizeof (*interactions));
+
+ while (lex_token (lexer) != T_STOP && lex_token (lexer) != '.')
+ {
+ this_var = parse_variable (lexer, dataset_dict (ds));
+ if (lex_match (lexer, '('))
+ {
+ lex_force_match (lexer, ')');
+ }
+ else if (lex_match (lexer, '*'))
+ {
+ n_members = 1;
+ interaction_vars = xnmalloc (2 * n_inter, sizeof (*interaction_vars));
+ n_members = parse_interactions (lexer, interaction_vars, 1, 2 * n_inter, ds);
+ if (n_allocated < n_inter)
+ {
+ n_allocated *= 2;
+ xnrealloc (interactions, n_allocated, sizeof (*interactions));
+ }
+ interactions [n_inter - 1] =
+ interaction_variable_create (interaction_vars, n_members);
+ n_inter++;
+ free (interaction_vars);
+ }
+ }
+ return 1;
+}
/* Parser for the dependent sub command */
static int
glm_custom_dependent (struct lexer *lexer, struct dataset *ds,
- struct cmd_glm *cmd UNUSED,
- void *aux UNUSED)
+ struct cmd_glm *cmd UNUSED, void *aux UNUSED)
{
const struct dictionary *dict = dataset_dict (ds);
assert (n_dependent);
if (n_dependent > 1)
msg (SE, _("Multivariate GLM not yet supported"));
- n_dependent = 1; /* Drop this line after adding support for multivariate GLM. */
+ n_dependent = 1; /* Drop this line after adding support for multivariate GLM. */
return 1;
}
-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);
-}
-
/*
- Put the moments in the linreg cache.
+ COV is the covariance matrix for variables included in the
+ model. That means the dependent variable is in there, too.
*/
static void
-compute_moments (pspp_linreg_cache * c, struct moments_var *mom,
- struct design_matrix *dm, size_t n)
+coeff_init (pspp_linreg_cache * c, const struct design_matrix *cov)
{
- size_t i;
- size_t j;
- double weight;
- double mean;
- double variance;
- double skewness;
- double kurtosis;
- /*
- Scan the variable names in the columns of the design matrix.
- When we find the variable we need, insert its mean in the cache.
- */
- for (i = 0; i < dm->m->size2; i++)
- {
- for (j = 0; j < n; j++)
- {
- if (design_matrix_col_to_var (dm, i) == (mom + j)->v)
- {
- moments1_calculate ((mom + j)->m, &weight, &mean, &variance,
- &skewness, &kurtosis);
- gsl_vector_set (c->indep_means, i, mean);
- gsl_vector_set (c->indep_std, i, sqrt (variance));
- }
- }
- }
+ c->coeff = xnmalloc (cov->m->size2, sizeof (*c->coeff));
+ c->n_coeffs = cov->m->size2 - 1;
+ pspp_coeff_init (c->coeff, cov);
}
-/* Encode categorical variables.
- Returns number of valid cases. */
-static int
-prepare_categories (struct casereader *input,
- const struct variable **vars, size_t n_vars,
- struct moments_var *mom)
-{
- int n_data;
- struct ccase c;
- size_t i;
- for (i = 0; i < n_vars; i++)
- if (var_is_alpha (vars[i]))
- cat_stored_values_create (vars[i]);
- n_data = 0;
- for (; casereader_read (input, &c); case_destroy (&c))
- {
- /*
- The second condition ensures the program will run even if
- there is only one variable to act as both explanatory and
- response.
- */
- for (i = 0; i < n_vars; i++)
- {
- const union value *val = case_data (&c, vars[i]);
- if (var_is_alpha (vars[i]))
- cat_value_update (vars[i], val);
- else
- moments1_add (mom[i].m, val->f, 1.0);
- }
- n_data++;
- }
- casereader_destroy (input);
-
- return n_data;
+static pspp_linreg_cache *
+fit_model (const struct covariance_matrix *cov,
+ const struct variable *dep_var,
+ const struct variable ** indep_vars,
+ size_t n_data, size_t n_indep)
+{
+ pspp_linreg_cache *result = NULL;
+ result = pspp_linreg_cache_alloc (dep_var, indep_vars, n_data, n_indep);
+ coeff_init (result, covariance_to_design (cov));
+ pspp_linreg_with_cov (cov, result);
+
+ return result;
}
static bool
run_glm (struct casereader *input,
struct cmd_glm *cmd,
- const struct dataset *ds,
- pspp_linreg_cache *model)
+ const struct dataset *ds)
{
- size_t i;
- int n_indep = 0;
- struct ccase c;
- const struct variable **indep_vars;
- struct design_matrix *X;
- struct moments_var *mom;
- gsl_vector *Y;
- struct casereader *reader;
casenumber row;
- size_t n_data; /* Number of valid cases. */
-
+ const struct variable **indep_vars;
+ const struct variable **all_vars;
+ int n_indep = 0;
+ pspp_linreg_cache *model = NULL;
pspp_linreg_opts lopts;
+ struct ccase *c;
+ size_t i;
+ size_t n_all_vars;
+ size_t n_data; /* Number of valid cases. */
+ struct casereader *reader;
+ struct covariance_matrix *cov;
- assert (model != NULL);
-
- if (!casereader_peek (input, 0, &c))
- return true;
- output_split_file_values (ds, &c);
- case_destroy (&c);
+ c = casereader_peek (input, 0);
+ if (c == NULL)
+ {
+ casereader_destroy (input);
+ return true;
+ }
+ output_split_file_values (ds, c);
+ case_unref (c);
if (!v_dependent)
{
1u << DC_SYSTEM);
}
- for (i = 0; i < n_dependent; i++)
- {
- if (!var_is_numeric (v_dependent[i]))
- {
- msg (SE, _("Dependent variable must be numeric."));
- return false;
- }
- }
-
- mom = xnmalloc (n_dependent, sizeof (*mom));
- mom->m = moments1_create (MOMENT_VARIANCE);
- mom->v = v_dependent[0];
lopts.get_depvar_mean_std = 1;
lopts.get_indep_mean_std = xnmalloc (n_dependent, sizeof (int));
indep_vars = xnmalloc (cmd->n_by, sizeof *indep_vars);
+ n_all_vars = cmd->n_by + n_dependent;
+ all_vars = xnmalloc (n_all_vars, sizeof *all_vars);
+ for (i = 0; i < n_dependent; i++)
+ {
+ all_vars[i] = v_dependent[i];
+ }
for (i = 0; i < cmd->n_by; i++)
{
indep_vars[i] = cmd->v_by[i];
+ all_vars[i + n_dependent] = cmd->v_by[i];
}
n_indep = cmd->n_by;
-
+
reader = casereader_clone (input);
reader = casereader_create_filter_missing (reader, indep_vars, n_indep,
- MV_ANY, NULL);
+ MV_ANY, NULL, NULL);
reader = casereader_create_filter_missing (reader, v_dependent, 1,
- MV_ANY, NULL);
- n_data = prepare_categories (casereader_clone (reader),
- indep_vars, n_indep, mom);
+ MV_ANY, NULL, NULL);
- if ((n_data > 0) && (n_indep > 0))
+ if (n_indep > 0)
{
- Y = gsl_vector_alloc (n_data);
- X =
- design_matrix_create (n_indep,
- (const struct variable **) indep_vars,
- n_data);
- for (i = 0; i < X->m->size2; i++)
+ for (i = 0; i < n_all_vars; i++)
+ if (var_is_alpha (all_vars[i]))
+ cat_stored_values_create (all_vars[i]);
+
+ cov = covariance_matrix_init (n_all_vars, all_vars, ONE_PASS, PAIRWISE, MV_ANY);
+
+ reader = casereader_create_counter (reader, &row, -1);
+
+ for (i = 0; i < n_inter; i++)
+ if (var_is_alpha (interaction_get_variable (interactions[i])))
+ cat_stored_values_create (interaction_get_variable (interactions[i]));
+ covariance_interaction_set (cov, interactions, 1);
+ for (; (c = casereader_read (reader)) != NULL; case_unref (c))
{
- lopts.get_indep_mean_std[i] = 1;
+ /*
+ Accumulate the covariance matrix.
+ */
+ covariance_matrix_accumulate (cov, c, interactions, 1);
+ n_data++;
}
- model = pspp_linreg_cache_alloc (X->m->size1, X->m->size2);
- model->indep_means = gsl_vector_alloc (X->m->size2);
- model->indep_std = gsl_vector_alloc (X->m->size2);
- model->depvar = v_dependent[0];
- reader = casereader_create_counter (reader, &row, -1);
- for (; casereader_read (reader, &c); case_destroy (&c))
+ covariance_matrix_compute (cov);
+ for (i = 0; i < n_dependent; i++)
{
- for (i = 0; i < n_indep; ++i)
- {
- const struct variable *v = indep_vars[i];
- const union value *val = case_data (&c, v);
- if (var_is_alpha (v))
- design_matrix_set_categorical (X, row, v, val);
- else
- design_matrix_set_numeric (X, row, v, val);
- }
- gsl_vector_set (Y, row, case_num (&c, model->depvar));
+ model = fit_model (cov, v_dependent[i], indep_vars, n_data, n_indep);
+ pspp_linreg_cache_free (model);
}
+
casereader_destroy (reader);
- /*
- Now that we know the number of coefficients, allocate space
- and store pointers to the variables that correspond to the
- coefficients.
- */
- coeff_init (model, X);
-
- /*
- Find the least-squares estimates and other statistics.
- */
- pspp_linreg ((const gsl_vector *) Y, X->m, &lopts, model);
- compute_moments (model, mom, X, n_dependent);
-
- gsl_vector_free (Y);
- design_matrix_destroy (X);
+ covariance_matrix_destroy (cov);
}
else
{
projects / pspp-builds.git / blobdiff