X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=src%2Flanguage%2Fstats%2Fregression.q;h=d923a35b00770ed3e09c04f9cc5cffcf8834c98b;hb=ce16a4a594e7ddfc277afc4abb7faaeb1a03d233;hp=690b6809aacda290c88d4175c177113d60c6396f;hpb=92c09e564002d356d20fc1e2e131027ef89f6748;p=pspp-builds.git
diff --git a/src/language/stats/regression.q b/src/language/stats/regression.q
index 690b6809..d923a35b 100644
--- a/src/language/stats/regression.q
+++ b/src/language/stats/regression.q
@@ -1,20 +1,18 @@
-/* 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 . */
#include
@@ -94,9 +92,6 @@ struct moments_var
const struct variable *v;
};
-/* Linear regression models. */
-static pspp_linreg_cache **models = NULL;
-
/*
Transformations for saving predicted values
and residuals, etc.
@@ -119,14 +114,14 @@ static size_t n_variables;
/*
File where the model will be saved if the EXPORT subcommand
- is given.
+ is given.
*/
static struct file_handle *model_file;
static bool run_regression (struct casereader *, struct cmd_regression *,
- struct dataset *);
+ struct dataset *, pspp_linreg_cache **);
-/*
+/*
STATISTICS subcommand output functions.
*/
static void reg_stats_r (pspp_linreg_cache *);
@@ -454,8 +449,8 @@ statistics_keyword_output (void (*function) (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
@@ -628,7 +623,7 @@ regression_trns_resid_proc (void *t_, struct ccase *c,
return TRNS_CONTINUE;
}
-/*
+/*
Returns false if NAME is a duplicate of any existing variable name.
*/
static bool
@@ -950,6 +945,7 @@ cmd_regression (struct lexer *lexer, struct dataset *ds)
{
struct casegrouper *grouper;
struct casereader *group;
+ pspp_linreg_cache **models;
bool ok;
size_t i;
@@ -965,7 +961,7 @@ cmd_regression (struct lexer *lexer, struct dataset *ds)
/* Data pass. */
grouper = casegrouper_create_splits (proc_open (ds), dataset_dict (ds));
while (casegrouper_get_next_group (grouper, &group))
- run_regression (group, &cmd, ds);
+ run_regression (group, &cmd, ds, models);
ok = casegrouper_destroy (grouper);
ok = proc_commit (ds) && ok;
@@ -1014,7 +1010,8 @@ regression_custom_variables (struct lexer *lexer, struct dataset *ds,
/* Identify the explanatory variables in v_variables. Returns
the number of independent variables. */
static int
-identify_indep_vars (struct variable **indep_vars, struct variable *depvar)
+identify_indep_vars (const struct variable **indep_vars,
+ const struct variable *depvar)
{
int n_indep_vars = 0;
int i;
@@ -1022,7 +1019,19 @@ identify_indep_vars (struct variable **indep_vars, struct variable *depvar)
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))
+ {
+ /*
+ 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 n_indep_vars;
}
@@ -1030,35 +1039,38 @@ identify_indep_vars (struct variable **indep_vars, struct variable *depvar)
Returns number of valid cases. */
static int
prepare_categories (struct casereader *input,
- struct variable **vars, size_t n_vars,
- struct moments_var *mom)
+ const struct variable **vars, size_t n_vars,
+ struct moments_var *mom)
{
int n_data;
struct ccase c;
size_t 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))
+ 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.
*/
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++;
- }
+ {
+ 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;
@@ -1108,7 +1120,7 @@ compute_moments (pspp_linreg_cache * c, struct moments_var *mom,
static bool
run_regression (struct casereader *input, struct cmd_regression *cmd,
- struct dataset *ds)
+ struct dataset *ds, pspp_linreg_cache **models)
{
size_t i;
int n_indep = 0;
@@ -1124,7 +1136,10 @@ run_regression (struct casereader *input, struct cmd_regression *cmd,
assert (models != NULL);
if (!casereader_peek (input, 0, &c))
- return true;
+ {
+ casereader_destroy (input);
+ return true;
+ }
output_split_file_values (ds, &c);
case_destroy (&c);
@@ -1156,22 +1171,21 @@ run_regression (struct casereader *input, struct cmd_regression *cmd,
for (k = 0; k < cmd->n_dependent; k++)
{
- struct variable *dep_var;
+ 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);
+ 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);
+ MV_ANY, NULL);
+ n_data = prepare_categories (casereader_clone (reader),
+ indep_vars, n_indep, mom);
if ((n_data > 0) && (n_indep > 0))
{
@@ -1185,35 +1199,32 @@ run_regression (struct casereader *input, struct cmd_regression *cmd,
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 = dep_var;
- /*
+ 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.
- */
- reader = casereader_create_counter (reader, &row, -1);
- for (; casereader_read (reader, &c); case_destroy (&c))
- {
- for (i = 0; i < n_indep; ++i)
- {
- 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));
- }
- casereader_destroy (reader);
+ The second pass fills the design matrix.
+ */
+ reader = casereader_create_counter (reader, &row, -1);
+ for (; casereader_read (reader, &c); case_destroy (&c))
+ {
+ 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, dep_var));
+ }
/*
Now that we know the number of coefficients, allocate space
and store pointers to the variables that correspond to the
@@ -1221,26 +1232,33 @@ run_regression (struct casereader *input, struct cmd_regression *cmd,
*/
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);
- if (!taint_has_tainted_successor (casereader_get_taint (input)))
- {
- 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);
}
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 (indep_vars);
free (lopts.get_indep_mean_std);
casereader_destroy (input);
@@ -1249,7 +1267,7 @@ run_regression (struct casereader *input, struct cmd_regression *cmd,
}
/*
- Local Variables:
+ Local Variables:
mode: c
End:
*/