-/* PSPP - linear regression.
+/* PSPP - a program for statistical analysis.
Copyright (C) 2005 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 the Free Software Foundation; either version 2 of the
- License, or (at your option) any later version.
+ 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
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
- This program is distributed in the hope that it will be useful, but
- WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- General Public License for more details.
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
You should have received a copy of the GNU General Public License
- along with this program; if not, write to the Free Software
- Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA
- 02110-1301, USA. */
+ along with this program. If not, see <http://www.gnu.org/licenses/>. */
#include <config.h>
#include "regression-export.h"
#include <data/case.h>
-#include <data/casefile.h>
+#include <data/casegrouper.h>
+#include <data/casereader.h>
#include <data/category.h>
#include <data/dictionary.h>
#include <data/missing-values.h>
#include <libpspp/alloc.h>
#include <libpspp/compiler.h>
#include <libpspp/message.h>
+#include <libpspp/taint.h>
#include <math/design-matrix.h>
#include <math/coefficient.h>
#include <math/linreg/linreg.h>
#include <output/table.h>
#include "gettext.h"
+#define _(msgid) gettext (msgid)
#define REG_LARGE_DATA 1000
const struct variable *v;
};
-/* Linear regression models. */
-static pspp_linreg_cache **models = NULL;
-
/*
Transformations for saving predicted values
and residuals, etc.
/*
File where the model will be saved if the EXPORT subcommand
- is given.
+ is given.
*/
static struct file_handle *model_file;
-/*
- Return value for the procedure.
- */
-static int pspp_reg_rc = CMD_SUCCESS;
+static bool run_regression (struct casereader *, struct cmd_regression *,
+ struct dataset *, pspp_linreg_cache **);
-static bool run_regression (const struct ccase *,
- const struct casefile *, void *,
- const struct dataset *);
-
-/*
+/*
STATISTICS subcommand output functions.
*/
static void reg_stats_r (pspp_linreg_cache *);
static void
subcommand_statistics (int *keywords, pspp_linreg_cache * c)
{
- /*
- The order here must match the order in which the STATISTICS
+ /*
+ The order here must match the order in which the STATISTICS
keywords appear in the specification section above.
*/
enum
return TRNS_CONTINUE;
}
-/*
+/*
Returns false if NAME is a duplicate of any existing variable name.
*/
static bool
int
cmd_regression (struct lexer *lexer, struct dataset *ds)
{
+ struct casegrouper *grouper;
+ struct casereader *group;
+ pspp_linreg_cache **models;
+ bool ok;
size_t i;
if (!parse_regression (lexer, ds, &cmd, NULL))
{
models[i] = NULL;
}
- if (!multipass_procedure_with_splits (ds, run_regression, &cmd))
- return CMD_CASCADING_FAILURE;
+
+ /* Data pass. */
+ grouper = casegrouper_create_splits (proc_open (ds), dataset_dict (ds));
+ while (casegrouper_get_next_group (grouper, &group))
+ run_regression (group, &cmd, ds, models);
+ ok = casegrouper_destroy (grouper);
+ ok = proc_commit (ds) && ok;
+
subcommand_save (ds, cmd.sbc_save, models);
free (v_variables);
free (models);
- return pspp_reg_rc;
+ free_regression (&cmd);
+
+ return ok ? CMD_SUCCESS : CMD_FAILURE;
}
/*
return v == v_variables[k];
}
-/*
- Mark missing cases. Return the number of non-missing cases.
- Compute the first two moments.
- */
-static size_t
-mark_missing_cases (const struct casefile *cf, const struct variable *v,
- int *is_missing_case, double n_data,
- struct moments_var *mom)
-{
- struct casereader *r;
- struct ccase c;
- size_t row;
- const union value *val;
- double w = 1.0;
-
- for (r = casefile_get_reader (cf, NULL);
- casereader_read (r, &c); case_destroy (&c))
- {
- row = casereader_cnum (r) - 1;
-
- val = case_data (&c, v);
- if (mom != NULL)
- {
- moments1_add (mom->m, val->f, w);
- }
- cat_value_update (v, val);
- if (var_is_value_missing (v, val, MV_ANY))
- {
- if (!is_missing_case[row])
- {
- /* Now it is missing. */
- n_data--;
- is_missing_case[row] = 1;
- }
- }
- }
- casereader_destroy (r);
-
- return n_data;
-}
-
/* Parser for the variables sub command */
static int
regression_custom_variables (struct lexer *lexer, struct dataset *ds,
return 1;
}
-/*
- Count the explanatory variables. The user may or may
- not have specified a response variable in the syntax.
- */
+/* Identify the explanatory variables in v_variables. Returns
+ the number of independent variables. */
static int
-get_n_indep (const struct variable *v)
+identify_indep_vars (const struct variable **indep_vars,
+ const struct variable *depvar)
{
- int result;
- int i = 0;
+ int n_indep_vars = 0;
+ int i;
- result = n_variables;
- while (i < n_variables)
+ for (i = 0; i < n_variables; i++)
+ if (!is_depvar (i, depvar))
+ indep_vars[n_indep_vars++] = v_variables[i];
+ if ((n_indep_vars < 2) && is_depvar (0, depvar))
{
- if (is_depvar (i, v))
- {
- result--;
- i = n_variables;
- }
- i++;
+ /*
+ There is only one independent variable, and it is the same
+ as the dependent variable. Print a warning and continue.
+ */
+ msg (SE,
+ gettext ("The dependent variable is equal to the independent variable."
+ "The least squares line is therefore Y=X."
+ "Standard errors and related statistics may be meaningless."));
+ n_indep_vars = 1;
+ indep_vars[0] = v_variables[0];
}
- return (result == 0) ? 1 : result;
+ return n_indep_vars;
}
-/*
- Read from the active file. Identify the explanatory variables in
- v_variables. Encode categorical variables. Drop cases with missing
- values.
-*/
+/* Encode categorical variables.
+ Returns number of valid cases. */
static int
-prepare_data (int n_data, int is_missing_case[],
- const struct variable **indep_vars,
- const struct variable *depvar, const struct casefile *cf,
- struct moments_var *mom)
+prepare_categories (struct casereader *input,
+ const struct variable **vars, size_t n_vars,
+ struct moments_var *mom)
{
- int i;
- int j;
+ int n_data;
+ struct ccase c;
+ size_t i;
- assert (indep_vars != NULL);
- j = 0;
- for (i = 0; i < n_variables; i++)
+ assert (vars != NULL);
+ assert (mom != NULL);
+
+ 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.
+ The second condition ensures the program will run even if
+ there is only one variable to act as both explanatory and
+ response.
*/
- if ((!is_depvar (i, depvar)) || (n_variables == 1))
+ for (i = 0; i < n_vars; i++)
{
- indep_vars[j] = v_variables[i];
- j++;
- if (var_is_alpha (v_variables[i]))
- {
- /* Make a place to hold the binary vectors
- 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,
- mom + 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++;
}
- /*
- Mark missing cases for the dependent variable.
- */
- n_data = mark_missing_cases (cf, depvar, is_missing_case, n_data, NULL);
+ casereader_destroy (input);
return n_data;
}
+
static void
coeff_init (pspp_linreg_cache * c, struct design_matrix *dm)
{
}
}
}
+
static bool
-run_regression (const struct ccase *first,
- const struct casefile *cf, void *cmd_ UNUSED,
- const struct dataset *ds)
+run_regression (struct casereader *input, struct cmd_regression *cmd,
+ struct dataset *ds, pspp_linreg_cache **models)
{
size_t i;
- 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;
int k;
- /*
- Keep track of the missing cases.
- */
- int *is_missing_case;
- const union value *val;
- struct casereader *r;
struct ccase c;
const struct variable **indep_vars;
struct design_matrix *X;
assert (models != NULL);
- output_split_file_values (ds, first);
+ if (!casereader_peek (input, 0, &c))
+ {
+ casereader_destroy (input);
+ return true;
+ }
+ output_split_file_values (ds, &c);
+ case_destroy (&c);
if (!v_variables)
{
1u << DC_SYSTEM);
}
- n_cases = casefile_get_case_cnt (cf);
-
- for (i = 0; i < cmd.n_dependent; i++)
+ for (i = 0; i < cmd->n_dependent; i++)
{
- if (!var_is_numeric (cmd.v_dependent[i]))
+ if (!var_is_numeric (cmd->v_dependent[i]))
{
- msg (SE, gettext ("Dependent variable must be numeric."));
- pspp_reg_rc = CMD_FAILURE;
- return true;
+ msg (SE, _("Dependent variable must be numeric."));
+ return false;
}
}
- is_missing_case = xnmalloc (n_cases, sizeof (*is_missing_case));
mom = xnmalloc (n_variables, sizeof (*mom));
for (i = 0; i < n_variables; i++)
{
}
lopts.get_depvar_mean_std = 1;
- for (k = 0; k < cmd.n_dependent; k++)
+ lopts.get_indep_mean_std = xnmalloc (n_variables, sizeof (int));
+ indep_vars = xnmalloc (n_variables, sizeof *indep_vars);
+
+ for (k = 0; k < cmd->n_dependent; k++)
{
- 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);
- assert (indep_vars != NULL);
+ const struct variable *dep_var;
+ struct casereader *reader;
+ casenumber row;
+ struct ccase c;
+ size_t n_data; /* Number of valid cases. */
+
+ dep_var = cmd->v_dependent[k];
+ n_indep = identify_indep_vars (indep_vars, dep_var);
+ reader = casereader_clone (input);
+ reader = casereader_create_filter_missing (reader, indep_vars, n_indep,
+ MV_ANY, NULL);
+ reader = casereader_create_filter_missing (reader, &dep_var, 1,
+ MV_ANY, NULL);
+ n_data = prepare_categories (casereader_clone (reader),
+ indep_vars, n_indep, mom);
- 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],
- (const struct casefile *) cf, mom);
if ((n_data > 0) && (n_indep > 0))
{
Y = gsl_vector_alloc (n_data);
lopts.get_indep_mean_std[i] = 1;
}
models[k] = pspp_linreg_cache_alloc (X->m->size1, X->m->size2);
- models[k]->indep_means = gsl_vector_alloc (X->m->size2);
- models[k]->indep_std = gsl_vector_alloc (X->m->size2);
- models[k]->depvar = (const struct variable *) cmd.v_dependent[k];
+ models[k]->depvar = dep_var;
/*
For large data sets, use QR decomposition.
*/
if (n_data > sqrt (n_indep) && n_data > REG_LARGE_DATA)
{
- models[k]->method = PSPP_LINREG_SVD;
+ models[k]->method = PSPP_LINREG_QR;
}
/*
The second pass fills the design matrix.
*/
- row = 0;
- for (r = casefile_get_reader (cf, NULL); casereader_read (r, &c);
- case_destroy (&c))
- /* Iterate over the cases. */
+ reader = casereader_create_counter (reader, &row, -1);
+ for (; casereader_read (reader, &c); case_destroy (&c))
{
- case_num = casereader_cnum (r) - 1;
- if (!is_missing_case[case_num])
+ for (i = 0; i < n_indep; ++i)
{
- for (i = 0; i < n_variables; ++i) /* Iterate over the
- variables for the
- current case.
- */
- {
- val = case_data (&c, v_variables[i]);
- /*
- Independent/dependent variable separation. The
- 'variables' subcommand specifies a varlist which contains
- both dependent and independent variables. The dependent
- variables are specified with the 'dependent'
- subcommand, and maybe also in the 'variables' subcommand.
- We need to separate the two.
- */
- if (!is_depvar (i, cmd.v_dependent[k]))
- {
- if (var_is_alpha (v_variables[i]))
- {
- design_matrix_set_categorical (X, row,
- v_variables[i],
- val);
- }
- else
- {
- design_matrix_set_numeric (X, row,
- v_variables[i], val);
- }
- }
- }
- val = case_data (&c, cmd.v_dependent[k]);
- gsl_vector_set (Y, row, val->f);
- row++;
+ 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, dep_var));
}
/*
Now that we know the number of coefficients, allocate space
*/
coeff_init (models[k], X);
- /*
+ /*
Find the least-squares estimates and other statistics.
*/
pspp_linreg ((const gsl_vector *) Y, X->m, &lopts, models[k]);
compute_moments (models[k], mom, X, n_variables);
- subcommand_statistics (cmd.a_statistics, models[k]);
- subcommand_export (cmd.sbc_export, models[k]);
+
+ if (!taint_has_tainted_successor (casereader_get_taint (input)))
+ {
+ subcommand_statistics (cmd->a_statistics, models[k]);
+ subcommand_export (cmd->sbc_export, models[k]);
+ }
gsl_vector_free (Y);
design_matrix_destroy (X);
- free (indep_vars);
- free (lopts.get_indep_mean_std);
- casereader_destroy (r);
}
else
{
- msg (SE, gettext ("No valid data found. This command was skipped."));
+ msg (SE,
+ gettext ("No valid data found. This command was skipped."));
}
+ casereader_destroy (reader);
}
for (i = 0; i < n_variables; i++)
{
moments1_destroy ((mom + i)->m);
}
free (mom);
- free (is_missing_case);
+ free (indep_vars);
+ free (lopts.get_indep_mean_std);
+ casereader_destroy (input);
return true;
}
/*
- Local Variables:
+ Local Variables:
mode: c
End:
*/