#include <gsl/gsl_vector.h>
#include <math.h>
#include <stdlib.h>
-
#include <data/case.h>
#include <data/casegrouper.h>
#include <data/casereader.h>
#include <libpspp/compiler.h>
#include <libpspp/message.h>
#include <libpspp/taint.h>
-#include <math/design-matrix.h>
-#include <math/coefficient.h>
+#include <math/covariance.h>
#include <math/linreg.h>
#include <math/moments.h>
#include <output/table.h>
{
int n_trns; /* Number of transformations. */
int trns_id; /* Which trns is this one? */
- pspp_linreg_cache *c; /* Linear model for this trns. */
+ linreg *c; /* Linear model for this trns. */
};
/*
Variables used (both explanatory and response).
static size_t n_variables;
static bool run_regression (struct casereader *, struct cmd_regression *,
- struct dataset *, pspp_linreg_cache **);
+ struct dataset *, linreg **);
/*
STATISTICS subcommand output functions.
*/
-static void reg_stats_r (pspp_linreg_cache *);
-static void reg_stats_coeff (pspp_linreg_cache *);
-static void reg_stats_anova (pspp_linreg_cache *);
-static void reg_stats_outs (pspp_linreg_cache *);
-static void reg_stats_zpp (pspp_linreg_cache *);
-static void reg_stats_label (pspp_linreg_cache *);
-static void reg_stats_sha (pspp_linreg_cache *);
-static void reg_stats_ci (pspp_linreg_cache *);
-static void reg_stats_f (pspp_linreg_cache *);
-static void reg_stats_bcov (pspp_linreg_cache *);
-static void reg_stats_ses (pspp_linreg_cache *);
-static void reg_stats_xtx (pspp_linreg_cache *);
-static void reg_stats_collin (pspp_linreg_cache *);
-static void reg_stats_tol (pspp_linreg_cache *);
-static void reg_stats_selection (pspp_linreg_cache *);
-static void statistics_keyword_output (void (*)(pspp_linreg_cache *),
- int, pspp_linreg_cache *);
+static void reg_stats_r (linreg *);
+static void reg_stats_coeff (linreg *);
+static void reg_stats_anova (linreg *);
+static void reg_stats_outs (linreg *);
+static void reg_stats_zpp (linreg *);
+static void reg_stats_label (linreg *);
+static void reg_stats_sha (linreg *);
+static void reg_stats_ci (linreg *);
+static void reg_stats_f (linreg *);
+static void reg_stats_bcov (linreg *);
+static void reg_stats_ses (linreg *);
+static void reg_stats_xtx (linreg *);
+static void reg_stats_collin (linreg *);
+static void reg_stats_tol (linreg *);
+static void reg_stats_selection (linreg *);
+static void statistics_keyword_output (void (*)(linreg *),
+ int, linreg *);
static void
-reg_stats_r (pspp_linreg_cache * c)
+reg_stats_r (linreg * c)
{
struct tab_table *t;
int n_rows = 2;
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 (pspp_linreg_mse (c));
+ std_error = sqrt (linreg_mse (c));
t = tab_create (n_cols, n_rows, 0);
tab_dim (t, tab_natural_dimensions, NULL);
tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 0, n_cols - 1, n_rows - 1);
Table showing estimated regression coefficients.
*/
static void
-reg_stats_coeff (pspp_linreg_cache * c)
+reg_stats_coeff (linreg * c)
{
size_t j;
int n_cols = 7;
const char *label;
const struct variable *v;
- const union value *val;
struct tab_table *t;
assert (c != NULL);
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)"));
- tab_double (t, 2, 1, 0, c->intercept, NULL);
- std_err = sqrt (gsl_matrix_get (c->cov, 0, 0));
+ tab_double (t, 2, 1, 0, linreg_intercept (c), NULL);
+ std_err = sqrt (gsl_matrix_get (linreg_cov (c), 0, 0));
tab_double (t, 3, 1, 0, std_err, NULL);
tab_double (t, 4, 1, 0, 0.0, NULL);
- t_stat = c->intercept / std_err;
+ t_stat = linreg_intercept (c) / std_err;
tab_double (t, 5, 1, 0, t_stat, NULL);
pval = 2 * gsl_cdf_tdist_Q (fabs (t_stat), 1.0);
tab_double (t, 6, 1, 0, pval, NULL);
- for (j = 0; j < c->n_coeffs; j++)
+ for (j = 0; j < linreg_n_coeffs (c); j++)
{
struct string tstr;
ds_init_empty (&tstr);
this_row = j + 2;
- v = pspp_coeff_get_var (c->coeff[j], 0);
+ v = linreg_indep_var (c, j);
label = var_to_string (v);
/* Do not overwrite the variable's name. */
ds_put_cstr (&tstr, label);
- if (var_is_alpha (v))
- {
- /*
- Append the value associated with this coefficient.
- This makes sense only if we us the usual binary encoding
- for that value.
- */
-
- val = pspp_coeff_get_value (c->coeff[j], v);
-
- var_append_value_name (v, val, &tstr);
- }
-
tab_text (t, 1, this_row, TAB_CENTER, ds_cstr (&tstr));
/*
Regression coefficients.
*/
- tab_double (t, 2, this_row, 0, c->coeff[j]->estimate, NULL);
+ tab_double (t, 2, this_row, 0, linreg_coeff (c, j), NULL);
/*
Standard error of the coefficients.
*/
- std_err = sqrt (gsl_matrix_get (c->cov, j + 1, j + 1));
+ std_err = sqrt (gsl_matrix_get (linreg_cov (c), j + 1, j + 1));
tab_double (t, 3, this_row, 0, std_err, NULL);
/*
Standardized coefficient, i.e., regression coefficient
if all variables had unit variance.
*/
- beta = pspp_coeff_get_sd (c->coeff[j]);
- beta *= c->coeff[j]->estimate / c->depvar_std;
+ beta = sqrt (gsl_matrix_get (linreg_cov (c), j, j));
+ beta *= linreg_coeff (c, j) / c->depvar_std;
tab_double (t, 4, this_row, 0, beta, NULL);
/*
Test statistic for H0: coefficient is 0.
*/
- t_stat = c->coeff[j]->estimate / std_err;
+ t_stat = linreg_coeff (c, j) / std_err;
tab_double (t, 5, this_row, 0, t_stat, NULL);
/*
P values for the test statistic above.
*/
pval =
2 * gsl_cdf_tdist_Q (fabs (t_stat),
- (double) (c->n_obs - c->n_coeffs));
+ (double) (linreg_n_obs (c) - linreg_n_coeffs (c)));
tab_double (t, 6, this_row, 0, pval, NULL);
ds_destroy (&tstr);
}
Display the ANOVA table.
*/
static void
-reg_stats_anova (pspp_linreg_cache * c)
+reg_stats_anova (linreg * c)
{
int n_cols = 7;
int n_rows = 4;
- const double msm = c->ssm / c->dfm;
- const double mse = pspp_linreg_mse (c);
+ const double msm = linreg_ssreg (c) / linreg_dfmodel (c);
+ const double mse = linreg_mse (c);
const double F = msm / mse;
const double pval = gsl_cdf_fdist_Q (F, c->dfm, c->dfe);
}
static void
-reg_stats_outs (pspp_linreg_cache * c)
+reg_stats_outs (linreg * c)
{
assert (c != NULL);
}
static void
-reg_stats_zpp (pspp_linreg_cache * c)
+reg_stats_zpp (linreg * c)
{
assert (c != NULL);
}
static void
-reg_stats_label (pspp_linreg_cache * c)
+reg_stats_label (linreg * c)
{
assert (c != NULL);
}
static void
-reg_stats_sha (pspp_linreg_cache * c)
+reg_stats_sha (linreg * c)
{
assert (c != NULL);
}
static void
-reg_stats_ci (pspp_linreg_cache * c)
+reg_stats_ci (linreg * c)
{
assert (c != NULL);
}
static void
-reg_stats_f (pspp_linreg_cache * c)
+reg_stats_f (linreg * c)
{
assert (c != NULL);
}
static void
-reg_stats_bcov (pspp_linreg_cache * c)
+reg_stats_bcov (linreg * c)
{
int n_cols;
int n_rows;
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 = 0; i < c->n_coeffs; i++)
+ for (i = 0; i < linreg_n_coeffs (c); i++)
{
- const struct variable *v = pspp_coeff_get_var (c->coeff[i], 0);
+ const struct variable *v = linreg_indep_var (c, i);
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_coeffs; k++)
+ for (k = 1; k < linreg_n_coeffs (c); k++)
{
col = (i <= k) ? k : i;
row = (i <= k) ? i : k;
tab_submit (t);
}
static void
-reg_stats_ses (pspp_linreg_cache * c)
+reg_stats_ses (linreg * c)
{
assert (c != NULL);
}
static void
-reg_stats_xtx (pspp_linreg_cache * c)
+reg_stats_xtx (linreg * c)
{
assert (c != NULL);
}
static void
-reg_stats_collin (pspp_linreg_cache * c)
+reg_stats_collin (linreg * c)
{
assert (c != NULL);
}
static void
-reg_stats_tol (pspp_linreg_cache * c)
+reg_stats_tol (linreg * c)
{
assert (c != NULL);
}
static void
-reg_stats_selection (pspp_linreg_cache * c)
+reg_stats_selection (linreg * c)
{
assert (c != NULL);
}
static void
-statistics_keyword_output (void (*function) (pspp_linreg_cache *),
- int keyword, pspp_linreg_cache * c)
+statistics_keyword_output (void (*function) (linreg *),
+ int keyword, linreg * c)
{
if (keyword)
{
}
static void
-subcommand_statistics (int *keywords, pspp_linreg_cache * c)
+subcommand_statistics (int *keywords, linreg * c)
{
/*
The order here must match the order in which the STATISTICS
if (t->trns_id == t->n_trns)
{
- result = pspp_linreg_cache_free (t->c);
+ result = linreg_free (t->c);
}
free (t);
size_t i;
size_t n_vals;
struct reg_trns *trns = t_;
- pspp_linreg_cache *model;
+ linreg *model;
union value *output = NULL;
- const union value **vals = NULL;
+ const union value *tmp;
+ double *vals;
const struct variable **vars = NULL;
assert (trns != NULL);
assert (model->depvar != NULL);
assert (model->pred != NULL);
- vars = xnmalloc (model->n_coeffs, sizeof (*vars));
- n_vals = (*model->get_vars) (model, vars);
-
+ vars = linreg_get_vars (model);
+ n_vals = linreg_n_coeffs (model);
vals = xnmalloc (n_vals, sizeof (*vals));
*c = case_unshare (*c);
+
output = case_data_rw (*c, model->pred);
for (i = 0; i < n_vals; i++)
{
- vals[i] = case_data (*c, vars[i]);
+ tmp = case_data (*c, vars[i]);
+ vals[i] = tmp->f;
}
- output->f = (*model->predict) ((const struct variable **) vars,
- vals, model, n_vals);
+ output->f = linreg_predict (model, vals, n_vals);
free (vals);
- free (vars);
return TRNS_CONTINUE;
}
size_t i;
size_t n_vals;
struct reg_trns *trns = t_;
- pspp_linreg_cache *model;
+ linreg *model;
union value *output = NULL;
- const union value **vals = NULL;
- const union value *obs = NULL;
+ const union value *tmp;
+ double *vals = NULL;
+ double obs;
const struct variable **vars = NULL;
assert (trns != NULL);
assert (model->depvar != NULL);
assert (model->resid != NULL);
- vars = xnmalloc (model->n_coeffs, sizeof (*vars));
- n_vals = (*model->get_vars) (model, vars);
+ vars = linreg_get_vars (model);
+ n_vals = linreg_n_coeffs (model);
vals = xnmalloc (n_vals, sizeof (*vals));
*c = case_unshare (*c);
for (i = 0; i < n_vals; i++)
{
- vals[i] = case_data (*c, vars[i]);
+ tmp = case_data (*c, vars[i]);
+ vals[i] = tmp->f;
}
- obs = case_data (*c, model->depvar);
- output->f = (*model->residual) ((const struct variable **) vars,
- vals, obs, model, n_vals);
+ tmp = case_data (*c, model->depvar);
+ obs = tmp->f;
+ output->f = linreg_residual (model, obs, vals, n_vals);
free (vals);
- free (vars);
+
return TRNS_CONTINUE;
}
static void
reg_save_var (struct dataset *ds, const char *prefix, trns_proc_func * f,
- pspp_linreg_cache * c, struct variable **v, int n_trns)
+ linreg * c, struct variable **v, int n_trns)
{
struct dictionary *dict = dataset_dict (ds);
static int trns_index = 1;
trns_index++;
}
static void
-subcommand_save (struct dataset *ds, int save, pspp_linreg_cache ** models)
+subcommand_save (struct dataset *ds, int save, linreg ** models)
{
- pspp_linreg_cache **lc;
+ linreg **lc;
int n_trns = 0;
int i;
{
if (*lc != NULL)
{
- pspp_linreg_cache_free (*lc);
+ linreg_free (*lc);
}
}
}
{
struct casegrouper *grouper;
struct casereader *group;
- pspp_linreg_cache **models;
+ linreg **models;
bool ok;
size_t i;
}
return n_indep_vars;
}
-
-/* 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)
+static double
+fill_covariance (gsl_matrix *cov, struct covariance *all_cov,
+ const struct variable **vars,
+ size_t n_vars, const struct variable *dep_var,
+ const struct variable **all_vars, size_t n_all_vars)
{
- 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 (; (c = casereader_read (input)) != NULL; case_unref (c))
+ size_t j;
+ size_t k = 0;
+ size_t dep_subscript;
+ size_t *rows;
+ const gsl_matrix *ssizes;
+ const gsl_matrix *cm;
+ double result = 0.0;
+
+ cm = covariance_calculate (all_cov);
+ rows = xnmalloc (cov->size1 - 1, sizeof (*rows));
+
+ for (i = 0; i < n_all_vars; i++)
{
- /*
- 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++)
+ for (j = k; j < n_vars; j++)
{
- 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);
+ if (vars[j] == all_vars[i])
+ {
+ if (vars[j] != dep_var)
+ {
+ rows[j] = i;
+ }
+ else
+ {
+ dep_subscript = i;
+ }
+ k++;
+ break;
+ }
}
- n_data++;
}
- casereader_destroy (input);
-
- return n_data;
-}
-
-static void
-coeff_init (pspp_linreg_cache * c, struct design_matrix *dm)
-{
- c->coeff = xnmalloc (dm->m->size2, sizeof (*c->coeff));
- pspp_coeff_init (c->coeff, dm);
+ for (i = 0; i < cov->size1 - 1; i++)
+ {
+ for (j = 0; j < cov->size2 - 1; j++)
+ {
+ gsl_matrix_set (cov, i, j, gsl_matrix_get (cm, rows[i], rows[j]));
+ gsl_matrix_set (cov, j, i, gsl_matrix_get (cm, rows[j], rows[i]));
+ }
+ }
+ ssizes = covariance_moments (all_cov, MOMENT_NONE);
+ result = gsl_matrix_get (ssizes, dep_subscript, rows[0]);
+ for (i = 0; i < cov->size1 - 1; i++)
+ {
+ gsl_matrix_set (cov, i, cov->size1 - 1,
+ gsl_matrix_get (cm, rows[i], dep_subscript));
+ gsl_matrix_set (cov, cov->size1 - 1, i,
+ gsl_matrix_get (cm, rows[i], dep_subscript));
+ if (result > gsl_matrix_get (ssizes, rows[i], dep_subscript))
+ {
+ result = gsl_matrix_get (ssizes, rows[i], dep_subscript);
+ }
+ }
+ free (rows);
}
static bool
run_regression (struct casereader *input, struct cmd_regression *cmd,
- struct dataset *ds, pspp_linreg_cache **models)
+ struct dataset *ds, linreg **models)
{
size_t i;
int n_indep = 0;
int k;
+ double n_data;
struct ccase *c;
- const struct variable **indep_vars;
- struct design_matrix *X;
- struct moments_var *mom;
- gsl_vector *Y;
-
- pspp_linreg_opts lopts;
+ struct covariance *cov;
+ const struct variable **vars;
+ const struct variable *dep_var;
+ struct casereader *reader;
+ const struct dictionary *dict;
+ gsl_matrix *this_cm;
assert (models != NULL);
+ for (i = 0; i < n_variables; i++)
+ {
+ if (!var_is_numeric (v_variables[i]))
+ {
+ msg (SE, _("REGRESSION requires numeric variables."));
+ return false;
+ }
+ }
+
c = casereader_peek (input, 0);
if (c == NULL)
{
output_split_file_values (ds, c);
case_unref (c);
+ dict = dataset_dict (ds);
if (!v_variables)
{
- dict_get_vars (dataset_dict (ds), &v_variables, &n_variables, 0);
+ dict_get_vars (dict, &v_variables, &n_variables, 0);
}
-
- for (i = 0; i < cmd->n_dependent; i++)
+ vars = xnmalloc (n_variables, sizeof (*vars));
+ cov = covariance_1pass_create (n_variables, v_variables,
+ dict_get_weight (dict), MV_ANY);
+
+ reader = casereader_clone (input);
+ reader = casereader_create_filter_missing (reader, v_variables, n_variables,
+ MV_ANY, NULL, NULL);
+ for (; (c = casereader_read (reader)) != NULL; case_unref (c))
{
- if (!var_is_numeric (cmd->v_dependent[i]))
- {
- msg (SE, _("Dependent variable must be numeric."));
- return false;
- }
+ covariance_accumulate (cov, c);
}
-
- mom = xnmalloc (n_variables, sizeof (*mom));
- for (i = 0; i < n_variables; i++)
- {
- (mom + i)->m = moments1_create (MOMENT_VARIANCE);
- (mom + i)->v = v_variables[i];
- }
- lopts.get_depvar_mean_std = 1;
-
-
- indep_vars = xnmalloc (n_variables, sizeof *indep_vars);
-
+
for (k = 0; k < cmd->n_dependent; k++)
{
- 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, NULL);
- reader = casereader_create_filter_missing (reader, &dep_var, 1,
- MV_ANY, NULL, NULL);
- n_data = prepare_categories (casereader_clone (reader),
- indep_vars, n_indep, mom);
-
- if ((n_data > 0) && (n_indep > 0))
+ n_indep = identify_indep_vars (vars, dep_var);
+
+ this_cm = gsl_matrix_alloc (n_indep + 1, n_indep + 1);
+ n_data = fill_covariance (this_cm, cov, vars, n_indep,
+ dep_var, v_variables, n_variables);
+ models[k] = linreg_alloc (dep_var, (const struct variable **) vars,
+ n_data, n_indep);
+ 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 = LINREG_QR;
+ }
+
+ if (n_data > 0)
{
- Y = gsl_vector_alloc (n_data);
- X =
- design_matrix_create (n_indep,
- (const struct variable **) indep_vars,
- n_data);
- lopts.get_indep_mean_std = xnmalloc (X->m->size2, sizeof (int));
- for (i = 0; i < X->m->size2; i++)
- {
- lopts.get_indep_mean_std[i] = 1;
- }
- models[k] = pspp_linreg_cache_alloc (dep_var, (const struct variable **) indep_vars,
- X->m->size1, n_indep);
- 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_QR;
- }
-
- /*
- The second pass fills the design matrix.
- */
- reader = casereader_create_counter (reader, &row, -1);
- for (; (c = casereader_read (reader)) != NULL; case_unref (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
- coefficients.
- */
- coeff_init (models[k], X);
-
/*
- Find the least-squares estimates and other statistics.
- */
- pspp_linreg ((const gsl_vector *) Y, X, &lopts, models[k]);
-
+ Find the least-squares estimates and other statistics.
+ */
+ linreg_fit (this_cm, models[k]);
+
if (!taint_has_tainted_successor (casereader_get_taint (input)))
{
subcommand_statistics (cmd->a_statistics, models[k]);
}
-
- gsl_vector_free (Y);
- design_matrix_destroy (X);
- free (lopts.get_indep_mean_std);
}
else
{
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);
- casereader_destroy (input);
+ casereader_destroy (reader);
+ free (vars);
+ casereader_destroy (input);
+ covariance_destroy (cov);
+
return true;
}
projects / pspp-builds.git / blobdiff