/* PSPP - a program for statistical analysis.
- Copyright (C) 2005, 2009, 2010, 2011, 2012, 2013 Free Software Foundation, Inc.
+ Copyright (C) 2005, 2009, 2010, 2011, 2012, 2013, 2014 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 <config.h>
+#include <float.h>
#include <stdbool.h>
#include <gsl/gsl_cdf.h>
#include <gsl/gsl_matrix.h>
#include <data/dataset.h>
+#include <data/casewriter.h>
#include "language/command.h"
#include "language/lexer/lexer.h"
#define REG_LARGE_DATA 1000
+#define STATS_R 1
+#define STATS_COEFF 2
+#define STATS_ANOVA 4
+#define STATS_OUTS 8
+#define STATS_CI 16
+#define STATS_BCOV 32
+
+#define STATS_DEFAULT (STATS_R | STATS_COEFF | STATS_ANOVA | STATS_OUTS)
+
+
+
struct regression
{
struct dataset *ds;
const struct variable **dep_vars;
size_t n_dep_vars;
- bool r;
- bool coeff;
- bool anova;
- bool bcov;
-
+ unsigned int stats;
+ double ci;
bool resid;
bool pred;
-
- linreg **models;
};
+struct regression_workspace
+{
+ /* The new variables which will be introduced by /SAVE */
+ const struct variable **predvars;
+ const struct variable **residvars;
-static void run_regression (const struct regression *cmd, struct casereader *input);
-
+ /* A reader/writer pair to temporarily hold the
+ values of the new variables */
+ struct casewriter *writer;
+ struct casereader *reader;
+ /* Indeces of the new values in the reader/writer (-1 if not applicable) */
+ int res_idx;
+ int pred_idx;
-/*
- 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? */
- linreg *c; /* Linear model for this trns. */
+ /* 0, 1 or 2 depending on what new variables are to be created */
+ int extras;
};
-/*
- Gets the predicted values.
-*/
-static int
-regression_trns_pred_proc (void *t_, struct ccase **c,
- casenumber case_idx UNUSED)
-{
- size_t i;
- size_t n_vals;
- struct reg_trns *trns = t_;
- linreg *model;
- union value *output = NULL;
- const union value *tmp;
- double *vals;
- const struct variable **vars = NULL;
-
- assert (trns != NULL);
- model = trns->c;
- assert (model != NULL);
- assert (model->depvar != NULL);
- assert (model->pred != NULL);
-
- 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++)
- {
- tmp = case_data (*c, vars[i]);
- vals[i] = tmp->f;
- }
- output->f = linreg_predict (model, vals, n_vals);
- free (vals);
- return TRNS_CONTINUE;
-}
-
-/*
- Gets the residuals.
-*/
-static int
-regression_trns_resid_proc (void *t_, struct ccase **c,
- casenumber case_idx UNUSED)
-{
- size_t i;
- size_t n_vals;
- struct reg_trns *trns = t_;
- linreg *model;
- union value *output = NULL;
- const union value *tmp;
- double *vals = NULL;
- double obs;
- const struct variable **vars = NULL;
-
- assert (trns != NULL);
- model = trns->c;
- assert (model != NULL);
- assert (model->depvar != NULL);
- assert (model->resid != NULL);
-
- 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->resid);
- assert (output != NULL);
-
- for (i = 0; i < n_vals; i++)
- {
- tmp = case_data (*c, vars[i]);
- vals[i] = tmp->f;
- }
- tmp = case_data (*c, model->depvar);
- obs = tmp->f;
- output->f = linreg_residual (model, obs, vals, n_vals);
- free (vals);
-
- return TRNS_CONTINUE;
-}
+static void run_regression (const struct regression *cmd,
+ struct regression_workspace *ws,
+ struct casereader *input);
+/* Return a string based on PREFIX which may be used as the name
+ of a new variable in DICT */
static char *
reg_get_name (const struct dictionary *dict, const char *prefix)
{
/* XXX handle too-long prefixes */
name = xmalloc (strlen (prefix) + INT_BUFSIZE_BOUND (i) + 1);
- for (i = 1; ; i++)
+ for (i = 1;; i++)
{
sprintf (name, "%s%d", prefix, i);
if (dict_lookup_var (dict, name) == NULL)
}
}
-/*
- Free the transformation. Free its linear model if this
- transformation is the last one.
-*/
-static bool
-regression_trns_free (void *t_)
-{
- struct reg_trns *t = t_;
-
- if (t->trns_id == t->n_trns)
- {
- linreg_unref (t->c);
- }
- free (t);
-
- return true;
-}
-static void
-reg_save_var (struct dataset *ds, const char *prefix, trns_proc_func * f,
- linreg * c, struct variable **v, int n_trns)
+static const struct variable *
+create_aux_var (struct dataset *ds, const char *prefix)
{
+ struct variable *var;
struct dictionary *dict = dataset_dict (ds);
- static int trns_index = 1;
- char *name;
- 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;
-
- name = reg_get_name (dict, prefix);
- new_var = dict_create_var_assert (dict, name, 0);
+ char *name = reg_get_name (dict, prefix);
+ var = dict_create_var_assert (dict, name, 0);
free (name);
-
- *v = new_var;
- add_transformation (ds, f, regression_trns_free, t);
- trns_index++;
+ return var;
}
-static void
-subcommand_save (const struct regression *cmd)
+/* Auxilliary data for transformation when /SAVE is entered */
+struct save_trans_data
{
- linreg **lc;
- int n_trns = 0;
+ int n_dep_vars;
+ struct regression_workspace *ws;
+};
- if ( cmd->resid ) n_trns++;
- if ( cmd->pred ) n_trns++;
+static bool
+save_trans_free (void *aux)
+{
+ struct save_trans_data *save_trans_data = aux;
+ free (save_trans_data->ws->predvars);
+ free (save_trans_data->ws->residvars);
- n_trns *= cmd->n_dep_vars;
+ casereader_destroy (save_trans_data->ws->reader);
+ free (save_trans_data->ws);
+ free (save_trans_data);
+ return true;
+}
+
+static int
+save_trans_func (void *aux, struct ccase **c, casenumber x UNUSED)
+{
+ struct save_trans_data *save_trans_data = aux;
+ struct regression_workspace *ws = save_trans_data->ws;
+ struct ccase *in = casereader_read (ws->reader);
- for (lc = cmd->models; lc < cmd->models + cmd->n_dep_vars; lc++)
+ if (in)
{
- if (*lc != NULL)
+ int k;
+ *c = case_unshare (*c);
+
+ for (k = 0; k < save_trans_data->n_dep_vars; ++k)
{
- if ((*lc)->depvar != NULL)
+ if (ws->pred_idx != -1)
{
- (*lc)->refcnt++;
- if (cmd->resid)
- {
- reg_save_var (cmd->ds, "RES", regression_trns_resid_proc, *lc,
- &(*lc)->resid, n_trns);
- }
- if (cmd->pred)
- {
- reg_save_var (cmd->ds, "PRED", regression_trns_pred_proc, *lc,
- &(*lc)->pred, n_trns);
- }
+ double pred = case_data_idx (in, ws->extras * k + ws->pred_idx)->f;
+ case_data_rw (*c, ws->predvars[k])->f = pred;
+ }
+
+ if (ws->res_idx != -1)
+ {
+ double resid = case_data_idx (in, ws->extras * k + ws->res_idx)->f;
+ case_data_rw (*c, ws->residvars[k])->f = resid;
}
}
+ case_unref (in);
}
+
+ return TRNS_CONTINUE;
}
+
int
cmd_regression (struct lexer *lexer, struct dataset *ds)
{
- int k;
+ struct regression_workspace workspace;
struct regression regression;
const struct dictionary *dict = dataset_dict (ds);
+ bool save;
memset (®ression, 0, sizeof (struct regression));
- regression.anova = true;
- regression.coeff = true;
- regression.r = true;
-
+ regression.ci = 0.95;
+ regression.stats = STATS_DEFAULT;
regression.pred = false;
regression.resid = false;
/* Accept an optional, completely pointless "/VARIABLES=" */
lex_match (lexer, T_SLASH);
- if (lex_match_id (lexer, "VARIABLES"))
+ if (lex_match_id (lexer, "VARIABLES"))
{
- if (! lex_force_match (lexer, T_EQUALS) )
+ if (!lex_force_match (lexer, T_EQUALS))
goto error;
}
if (!parse_variables_const (lexer, dict,
- ®ression.vars, ®ression.n_vars,
- PV_NO_DUPLICATE | PV_NUMERIC))
+ ®ression.vars, ®ression.n_vars,
+ PV_NO_DUPLICATE | PV_NUMERIC))
goto error;
{
lex_match (lexer, T_SLASH);
- if (lex_match_id (lexer, "DEPENDENT"))
+ if (lex_match_id (lexer, "DEPENDENT"))
{
- if (! lex_force_match (lexer, T_EQUALS) )
+ if (!lex_force_match (lexer, T_EQUALS))
goto error;
+ free (regression.dep_vars);
+ regression.n_dep_vars = 0;
+
if (!parse_variables_const (lexer, dict,
- ®ression.dep_vars, ®ression.n_dep_vars,
+ ®ression.dep_vars,
+ ®ression.n_dep_vars,
PV_NO_DUPLICATE | PV_NUMERIC))
goto error;
}
else if (lex_match_id (lexer, "METHOD"))
- {
- lex_match (lexer, T_EQUALS);
+ {
+ lex_match (lexer, T_EQUALS);
if (!lex_force_match_id (lexer, "ENTER"))
{
}
}
else if (lex_match_id (lexer, "STATISTICS"))
- {
- lex_match (lexer, T_EQUALS);
+ {
+ lex_match (lexer, T_EQUALS);
- while (lex_token (lexer) != T_ENDCMD
- && lex_token (lexer) != T_SLASH)
- {
+ while (lex_token (lexer) != T_ENDCMD
+ && lex_token (lexer) != T_SLASH)
+ {
if (lex_match (lexer, T_ALL))
{
+ regression.stats = ~0;
}
else if (lex_match_id (lexer, "DEFAULTS"))
{
+ regression.stats |= STATS_DEFAULT;
}
else if (lex_match_id (lexer, "R"))
{
+ regression.stats |= STATS_R;
}
else if (lex_match_id (lexer, "COEFF"))
{
+ regression.stats |= STATS_COEFF;
}
else if (lex_match_id (lexer, "ANOVA"))
{
+ regression.stats |= STATS_ANOVA;
}
else if (lex_match_id (lexer, "BCOV"))
{
+ regression.stats |= STATS_BCOV;
+ }
+ else if (lex_match_id (lexer, "CI"))
+ {
+ regression.stats |= STATS_CI;
+
+ if (lex_match (lexer, T_LPAREN))
+ {
+ regression.ci = lex_number (lexer) / 100.0;
+ lex_get (lexer);
+ lex_force_match (lexer, T_RPAREN);
+ }
}
else
{
}
}
else if (lex_match_id (lexer, "SAVE"))
- {
- lex_match (lexer, T_EQUALS);
+ {
+ lex_match (lexer, T_EQUALS);
- while (lex_token (lexer) != T_ENDCMD
- && lex_token (lexer) != T_SLASH)
- {
+ while (lex_token (lexer) != T_ENDCMD
+ && lex_token (lexer) != T_SLASH)
+ {
if (lex_match_id (lexer, "PRED"))
{
regression.pred = true;
dict_get_vars (dict, ®ression.vars, ®ression.n_vars, 0);
}
+ save = regression.pred || regression.resid;
+ workspace.extras = 0;
+ workspace.res_idx = -1;
+ workspace.pred_idx = -1;
+ workspace.writer = NULL;
+ workspace.reader = NULL;
+ if (save)
+ {
+ int i;
+ struct caseproto *proto = caseproto_create ();
+
+ if (regression.resid)
+ {
+ workspace.extras ++;
+ workspace.res_idx = 0;
+ workspace.residvars = xcalloc (regression.n_dep_vars, sizeof (*workspace.residvars));
+
+ for (i = 0; i < regression.n_dep_vars; ++i)
+ {
+ workspace.residvars[i] = create_aux_var (ds, "RES");
+ proto = caseproto_add_width (proto, 0);
+ }
+ }
+
+ if (regression.pred)
+ {
+ workspace.extras ++;
+ workspace.pred_idx = 1;
+ workspace.predvars = xcalloc (regression.n_dep_vars, sizeof (*workspace.predvars));
+
+ for (i = 0; i < regression.n_dep_vars; ++i)
+ {
+ workspace.predvars[i] = create_aux_var (ds, "PRED");
+ proto = caseproto_add_width (proto, 0);
+ }
+ }
+
+ if (proc_make_temporary_transformations_permanent (ds))
+ msg (SW, _("REGRESSION with SAVE ignores TEMPORARY. "
+ "Temporary transformations will be made permanent."));
+
+ workspace.writer = autopaging_writer_create (proto);
+ caseproto_unref (proto);
+ }
- regression.models = xcalloc (regression.n_dep_vars, sizeof *regression.models);
{
struct casegrouper *grouper;
struct casereader *group;
bool ok;
-
- grouper = casegrouper_create_splits (proc_open (ds), dict);
+
+ grouper = casegrouper_create_splits (proc_open_filtering (ds, !save), dict);
+
+
while (casegrouper_get_next_group (grouper, &group))
- run_regression (®ression, group);
+ {
+ run_regression (®ression,
+ &workspace,
+ group);
+
+ }
ok = casegrouper_destroy (grouper);
ok = proc_commit (ds) && ok;
}
- if (regression.pred || regression.resid )
+ if (workspace.writer)
{
- subcommand_save (®ression);
+ struct save_trans_data *save_trans_data = xmalloc (sizeof *save_trans_data);
+ struct casereader *r = casewriter_make_reader (workspace.writer);
+ workspace.writer = NULL;
+ workspace.reader = r;
+ save_trans_data->ws = xmalloc (sizeof (workspace));
+ memcpy (save_trans_data->ws, &workspace, sizeof (workspace));
+ save_trans_data->n_dep_vars = regression.n_dep_vars;
+
+ add_transformation (ds, save_trans_func, save_trans_free, save_trans_data);
}
-
- for (k = 0; k < regression.n_dep_vars; k++)
- linreg_unref (regression.models[k]);
- free (regression.models);
+
free (regression.vars);
free (regression.dep_vars);
return CMD_SUCCESS;
-
- error:
- for (k = 0; k < regression.n_dep_vars; k++)
- linreg_unref (regression.models[k]);
- free (regression.models);
+
+error:
+
free (regression.vars);
free (regression.dep_vars);
return CMD_FAILURE;
}
-
+/* Return the size of the union of dependent and independent variables */
static size_t
get_n_all_vars (const struct regression *cmd)
{
for (i = 0; i < cmd->n_dep_vars; i++)
{
for (j = 0; j < cmd->n_vars; j++)
- {
- if (cmd->vars[j] == cmd->dep_vars[i])
- {
- result--;
- }
- }
+ {
+ if (cmd->vars[j] == cmd->dep_vars[i])
+ {
+ result--;
+ }
+ }
}
return result;
}
+/* Fill VARS with the union of dependent and independent variables */
static void
fill_all_vars (const struct variable **vars, const struct regression *cmd)
{
+ size_t x = 0;
size_t i;
- size_t j;
- bool absent;
-
for (i = 0; i < cmd->n_vars; i++)
{
vars[i] = cmd->vars[i];
}
+
for (i = 0; i < cmd->n_dep_vars; i++)
{
- absent = true;
+ size_t j;
+ bool absent = true;
for (j = 0; j < cmd->n_vars; j++)
- {
- if (cmd->dep_vars[i] == cmd->vars[j])
- {
- absent = false;
- break;
- }
- }
+ {
+ if (cmd->dep_vars[i] == cmd->vars[j])
+ {
+ absent = false;
+ break;
+ }
+ }
if (absent)
- {
- vars[i + cmd->n_vars] = cmd->dep_vars[i];
- }
+ {
+ vars[cmd->n_vars + x++] = cmd->dep_vars[i];
+ }
}
}
/* Identify the explanatory variables in v_variables. Returns
the number of independent variables. */
static int
-identify_indep_vars (const struct regression *cmd,
+identify_indep_vars (const struct regression *cmd,
const struct variable **indep_vars,
- const struct variable *depvar)
+ const struct variable *depvar)
{
int n_indep_vars = 0;
int i;
if ((n_indep_vars < 1) && is_depvar (cmd, 0, depvar))
{
/*
- There is only one independent variable, and it is the same
- as the dependent variable. Print a warning and continue.
- */
+ There is only one independent variable, and it is the same
+ as the dependent variable. Print a warning and continue.
+ */
msg (SW,
- 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."));
+ 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] = cmd->vars[0];
}
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,
- double *means)
+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,
+ double *means)
{
size_t i;
size_t j;
const gsl_matrix *mean_matrix;
const gsl_matrix *ssize_matrix;
double result = 0.0;
-
- gsl_matrix *cm = covariance_calculate_unnormalized (all_cov);
- if ( cm == NULL)
+ const gsl_matrix *cm = covariance_calculate_unnormalized (all_cov);
+
+ if (cm == NULL)
return 0;
rows = xnmalloc (cov->size1 - 1, sizeof (*rows));
-
+
for (i = 0; i < n_all_vars; i++)
{
for (j = 0; j < n_vars; j++)
- {
- if (vars[j] == all_vars[i])
- {
- rows[j] = i;
- }
- }
+ {
+ if (vars[j] == all_vars[i])
+ {
+ rows[j] = i;
+ }
+ }
if (all_vars[i] == dep_var)
- {
- dep_subscript = i;
- }
+ {
+ dep_subscript = i;
+ }
}
mean_matrix = covariance_moments (all_cov, MOMENT_MEAN);
ssize_matrix = covariance_moments (all_cov, MOMENT_NONE);
for (i = 0; i < cov->size1 - 1; i++)
{
means[i] = gsl_matrix_get (mean_matrix, rows[i], 0)
- / gsl_matrix_get (ssize_matrix, rows[i], 0);
+ / gsl_matrix_get (ssize_matrix, rows[i], 0);
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]));
- }
+ {
+ 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]));
+ }
}
means[cov->size1 - 1] = gsl_matrix_get (mean_matrix, dep_subscript, 0)
/ gsl_matrix_get (ssize_matrix, dep_subscript, 0);
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));
+ 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);
- }
+ {
+ result = gsl_matrix_get (ssizes, rows[i], dep_subscript);
+ }
}
- gsl_matrix_set (cov, cov->size1 - 1, cov->size1 - 1,
- gsl_matrix_get (cm, dep_subscript, dep_subscript));
+ gsl_matrix_set (cov, cov->size1 - 1, cov->size1 - 1,
+ gsl_matrix_get (cm, dep_subscript, dep_subscript));
free (rows);
- gsl_matrix_free (cm);
return result;
}
+\f
/*
STATISTICS subcommand output functions.
*/
-static void reg_stats_r (linreg *, void *, const struct variable *);
-static void reg_stats_coeff (linreg *, void *, const struct variable *);
-static void reg_stats_anova (linreg *, void *, const struct variable *);
-static void reg_stats_bcov (linreg *, void *, const struct variable *);
-
-static void statistics_keyword_output (void (*)(linreg *, void *, const struct variable *),
- bool, linreg *, void *, const struct variable *);
-
+static void reg_stats_r (const linreg *, const struct variable *);
+static void reg_stats_coeff (const linreg *, const gsl_matrix *, const struct variable *, const struct regression *);
+static void reg_stats_anova (const linreg *, const struct variable *);
+static void reg_stats_bcov (const linreg *, const struct variable *);
static void
-subcommand_statistics (const struct regression *cmd , linreg * c, void *aux,
- const struct variable *var)
+subcommand_statistics (const struct regression *cmd, const linreg * c, const gsl_matrix * cm,
+ const struct variable *var)
{
- statistics_keyword_output (reg_stats_r, cmd->r, c, aux, var);
- statistics_keyword_output (reg_stats_anova, cmd->anova, c, aux, var);
- statistics_keyword_output (reg_stats_coeff, cmd->coeff, c, aux, var);
- statistics_keyword_output (reg_stats_bcov, cmd->bcov, c, aux, var);
+ if (cmd->stats & STATS_R)
+ reg_stats_r (c, var);
+
+ if (cmd->stats & STATS_ANOVA)
+ reg_stats_anova (c, var);
+
+ if (cmd->stats & STATS_COEFF)
+ reg_stats_coeff (c, cm, var, cmd);
+
+ if (cmd->stats & STATS_BCOV)
+ reg_stats_bcov (c, var);
}
static void
-run_regression (const struct regression *cmd, struct casereader *input)
+run_regression (const struct regression *cmd,
+ struct regression_workspace *ws,
+ struct casereader *input)
{
size_t i;
- int n_indep = 0;
+ linreg **models;
+
int k;
- double *means;
struct ccase *c;
struct covariance *cov;
- const struct variable **vars;
- const struct variable **all_vars;
struct casereader *reader;
- size_t n_all_vars;
+ size_t n_all_vars = get_n_all_vars (cmd);
+ const struct variable **all_vars = xnmalloc (n_all_vars, sizeof (*all_vars));
- linreg **models = cmd->models;
+ double *means = xnmalloc (n_all_vars, sizeof (*means));
- n_all_vars = get_n_all_vars (cmd);
- all_vars = xnmalloc (n_all_vars, sizeof (*all_vars));
fill_all_vars (all_vars, cmd);
- vars = xnmalloc (cmd->n_vars, sizeof (*vars));
- means = xnmalloc (n_all_vars, sizeof (*means));
cov = covariance_1pass_create (n_all_vars, all_vars,
- dict_get_weight (dataset_dict (cmd->ds)), MV_ANY);
+ dict_get_weight (dataset_dict (cmd->ds)),
+ MV_ANY);
reader = casereader_clone (input);
reader = casereader_create_filter_missing (reader, all_vars, n_all_vars,
- MV_ANY, NULL, NULL);
+ MV_ANY, NULL, NULL);
- for (; (c = casereader_read (reader)) != NULL; case_unref (c))
- {
- covariance_accumulate (cov, c);
- }
+ {
+ struct casereader *r = casereader_clone (reader);
+
+ for (; (c = casereader_read (r)) != NULL; case_unref (c))
+ {
+ covariance_accumulate (cov, c);
+ }
+ casereader_destroy (r);
+ }
+ models = xcalloc (cmd->n_dep_vars, sizeof (*models));
for (k = 0; k < cmd->n_dep_vars; k++)
{
- double n_data;
+ const struct variable **vars = xnmalloc (cmd->n_vars, sizeof (*vars));
const struct variable *dep_var = cmd->dep_vars[k];
- gsl_matrix *this_cm;
-
- n_indep = identify_indep_vars (cmd, 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, all_vars, n_all_vars, means);
- models[k] = linreg_alloc (dep_var, (const struct variable **) vars,
- n_data, n_indep);
+ int n_indep = identify_indep_vars (cmd, vars, dep_var);
+ gsl_matrix *this_cm = gsl_matrix_alloc (n_indep + 1, n_indep + 1);
+ double n_data = fill_covariance (this_cm, cov, vars, n_indep,
+ dep_var, all_vars, n_all_vars, means);
+ models[k] = linreg_alloc (dep_var, vars, n_data, n_indep);
models[k]->depvar = dep_var;
for (i = 0; i < n_indep; i++)
- {
- linreg_set_indep_variable_mean (models[k], i, means[i]);
- }
+ {
+ linreg_set_indep_variable_mean (models[k], i, means[i]);
+ }
linreg_set_depvar_mean (models[k], means[i]);
/*
- For large data sets, use QR decomposition.
- */
+ For large data sets, use QR decomposition.
+ */
if (n_data > sqrt (n_indep) && n_data > REG_LARGE_DATA)
- {
- models[k]->method = LINREG_QR;
- }
+ {
+ models[k]->method = LINREG_QR;
+ }
if (n_data > 0)
- {
- /*
- 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, models[k], this_cm, dep_var);
- }
- }
+ {
+ /*
+ 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, models[k], this_cm, dep_var);
+ }
+ }
else
- {
- msg (SE,
- _("No valid data found. This command was skipped."));
- linreg_unref (models[k]);
- models[k] = NULL;
- }
+ {
+ msg (SE, _("No valid data found. This command was skipped."));
+ }
gsl_matrix_free (this_cm);
+ free (vars);
+ }
+
+
+ if (ws->extras > 0)
+ {
+ struct casereader *r = casereader_clone (reader);
+
+ for (; (c = casereader_read (r)) != NULL; case_unref (c))
+ {
+ struct ccase *outc = case_clone (c);
+ for (k = 0; k < cmd->n_dep_vars; k++)
+ {
+ const struct variable **vars = xnmalloc (cmd->n_vars, sizeof (*vars));
+ const struct variable *dep_var = cmd->dep_vars[k];
+ int n_indep = identify_indep_vars (cmd, vars, dep_var);
+ double *vals = xnmalloc (n_indep, sizeof (*vals));
+ for (i = 0; i < n_indep; i++)
+ {
+ const union value *tmp = case_data (c, vars[i]);
+ vals[i] = tmp->f;
+ }
+
+ if (cmd->pred)
+ {
+ double pred = linreg_predict (models[k], vals, n_indep);
+ case_data_rw_idx (outc, k * ws->extras + ws->pred_idx)->f = pred;
+ }
+
+ if (cmd->resid)
+ {
+ double obs = case_data (c, models[k]->depvar)->f;
+ double res = linreg_residual (models[k], obs, vals, n_indep);
+ case_data_rw_idx (outc, k * ws->extras + ws->res_idx)->f = res;
+ }
+ free (vals);
+ free (vars);
+ }
+ casewriter_write (ws->writer, outc);
+ }
+ casereader_destroy (r);
}
-
+
casereader_destroy (reader);
- free (vars);
+
+ for (k = 0; k < cmd->n_dep_vars; k++)
+ {
+ linreg_unref (models[k]);
+ }
+ free (models);
+
free (all_vars);
free (means);
casereader_destroy (input);
covariance_destroy (cov);
}
-
\f
static void
-reg_stats_r (linreg *c, void *aux UNUSED, const struct variable *var)
+reg_stats_r (const linreg * c, const struct variable *var)
{
struct tab_table *t;
int n_rows = 2;
assert (c != NULL);
rsq = linreg_ssreg (c) / linreg_sst (c);
- adjrsq = 1.0 - (1.0 - rsq) * (linreg_n_obs (c) - 1.0) / (linreg_n_obs (c) - linreg_n_coeffs (c));
+ adjrsq = rsq -
+ (1.0 - rsq) * linreg_n_coeffs (c) / (linreg_n_obs (c) -
+ linreg_n_coeffs (c) - 1);
std_error = sqrt (linreg_mse (c));
t = tab_create (n_cols, n_rows);
tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 0, n_cols - 1, n_rows - 1);
tab_text (t, 2, 0, TAB_CENTER | TAT_TITLE, _("R Square"));
tab_text (t, 3, 0, TAB_CENTER | TAT_TITLE, _("Adjusted R Square"));
tab_text (t, 4, 0, TAB_CENTER | TAT_TITLE, _("Std. Error of the Estimate"));
- tab_double (t, 1, 1, TAB_RIGHT, sqrt (rsq), NULL);
- tab_double (t, 2, 1, TAB_RIGHT, rsq, NULL);
- tab_double (t, 3, 1, TAB_RIGHT, adjrsq, NULL);
- tab_double (t, 4, 1, TAB_RIGHT, std_error, NULL);
+ tab_double (t, 1, 1, TAB_RIGHT, sqrt (rsq), NULL, RC_OTHER);
+ tab_double (t, 2, 1, TAB_RIGHT, rsq, NULL, RC_OTHER);
+ tab_double (t, 3, 1, TAB_RIGHT, adjrsq, NULL, RC_OTHER);
+ tab_double (t, 4, 1, TAB_RIGHT, std_error, NULL, RC_OTHER);
tab_title (t, _("Model Summary (%s)"), var_to_string (var));
tab_submit (t);
}
Table showing estimated regression coefficients.
*/
static void
-reg_stats_coeff (linreg * c, void *aux_, const struct variable *var)
+reg_stats_coeff (const linreg * c, const gsl_matrix *cov, const struct variable *var, const struct regression *cmd)
{
size_t j;
int n_cols = 7;
+ const int heading_rows = 2;
int n_rows;
int this_row;
double t_stat;
const struct variable *v;
struct tab_table *t;
- gsl_matrix *cov = aux_;
+
+ const double df = linreg_n_obs (c) - linreg_n_coeffs (c) - 1;
+ double q = (1 - cmd->ci) / 2.0; /* 2-tailed test */
+ double tval = gsl_cdf_tdist_Qinv (q, df);
assert (c != NULL);
- n_rows = linreg_n_coeffs (c) + 3;
+ n_rows = linreg_n_coeffs (c) + heading_rows + 1;
+
+ if (cmd->stats & STATS_CI)
+ n_cols += 2;
t = tab_create (n_cols, n_rows);
tab_headers (t, 2, 0, 1, 0);
tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 0, n_cols - 1, n_rows - 1);
- tab_hline (t, TAL_2, 0, n_cols - 1, 1);
+ tab_hline (t, TAL_2, 0, n_cols - 1, heading_rows);
tab_vline (t, TAL_2, 2, 0, n_rows - 1);
tab_vline (t, TAL_0, 1, 0, 0);
- tab_text (t, 2, 0, TAB_CENTER | TAT_TITLE, _("B"));
- tab_text (t, 3, 0, TAB_CENTER | TAT_TITLE, _("Std. Error"));
- tab_text (t, 4, 0, TAB_CENTER | TAT_TITLE, _("Beta"));
- 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, linreg_intercept (c), NULL);
+
+ tab_hline (t, TAL_1, 2, 4, 1);
+ tab_joint_text (t, 2, 0, 3, 0, TAB_CENTER | TAT_TITLE, _("Unstandardized Coefficients"));
+ tab_text (t, 2, 1, TAB_CENTER | TAT_TITLE, _("B"));
+ tab_text (t, 3, 1, TAB_CENTER | TAT_TITLE, _("Std. Error"));
+ tab_text (t, 4, 0, TAB_CENTER | TAT_TITLE, _("Standardized Coefficients"));
+ tab_text (t, 4, 1, TAB_CENTER | TAT_TITLE, _("Beta"));
+ tab_text (t, 5, 1, TAB_CENTER | TAT_TITLE, _("t"));
+ tab_text (t, 6, 1, TAB_CENTER | TAT_TITLE, _("Sig."));
+ tab_text (t, 1, heading_rows, TAB_LEFT | TAT_TITLE, _("(Constant)"));
+ tab_double (t, 2, heading_rows, 0, linreg_intercept (c), NULL, RC_OTHER);
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);
+
+ if (cmd->stats & STATS_CI)
+ {
+ double lower = linreg_intercept (c) - tval * std_err ;
+ double upper = linreg_intercept (c) + tval * std_err ;
+ tab_double (t, 7, heading_rows, 0, lower, NULL, RC_OTHER);
+ tab_double (t, 8, heading_rows, 0, upper, NULL, RC_OTHER);
+
+ tab_joint_text_format (t, 7, 0, 8, 0, TAB_CENTER | TAT_TITLE, _("%g%% Confidence Interval for B"), cmd->ci * 100);
+ tab_hline (t, TAL_1, 7, 8, 1);
+ tab_text (t, 7, 1, TAB_CENTER | TAT_TITLE, _("Lower Bound"));
+ tab_text (t, 8, 1, TAB_CENTER | TAT_TITLE, _("Upper Bound"));
+ }
+ tab_double (t, 3, heading_rows, 0, std_err, NULL, RC_OTHER);
+ tab_double (t, 4, heading_rows, 0, 0.0, NULL, RC_OTHER);
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), (double) (linreg_n_obs (c) - linreg_n_coeffs (c)));
- tab_double (t, 6, 1, 0, pval, NULL);
+ tab_double (t, 5, heading_rows, 0, t_stat, NULL, RC_OTHER);
+ pval =
+ 2 * gsl_cdf_tdist_Q (fabs (t_stat),
+ (double) (linreg_n_obs (c) - linreg_n_coeffs (c)));
+ tab_double (t, 6, heading_rows, 0, pval, NULL, RC_PVALUE);
+
for (j = 0; j < linreg_n_coeffs (c); j++)
{
struct string tstr;
ds_init_empty (&tstr);
- this_row = j + 2;
+ this_row = j + heading_rows + 1;
v = linreg_indep_var (c, j);
label = var_to_string (v);
/* Do not overwrite the variable's name. */
ds_put_cstr (&tstr, label);
- tab_text (t, 1, this_row, TAB_CENTER, ds_cstr (&tstr));
+ tab_text (t, 1, this_row, TAB_LEFT, ds_cstr (&tstr));
/*
- Regression coefficients.
- */
- tab_double (t, 2, this_row, 0, linreg_coeff (c, j), NULL);
+ Regression coefficients.
+ */
+ tab_double (t, 2, this_row, 0, linreg_coeff (c, j), NULL, RC_OTHER);
/*
- Standard error of the coefficients.
- */
+ Standard error of the coefficients.
+ */
std_err = sqrt (gsl_matrix_get (linreg_cov (c), j + 1, j + 1));
- tab_double (t, 3, this_row, 0, std_err, NULL);
+ tab_double (t, 3, this_row, 0, std_err, NULL, RC_OTHER);
/*
- Standardized coefficient, i.e., regression coefficient
- if all variables had unit variance.
- */
+ Standardized coefficient, i.e., regression coefficient
+ if all variables had unit variance.
+ */
beta = sqrt (gsl_matrix_get (cov, j, j));
- beta *= linreg_coeff (c, j) /
- sqrt (gsl_matrix_get (cov, cov->size1 - 1, cov->size2 - 1));
- tab_double (t, 4, this_row, 0, beta, NULL);
+ beta *= linreg_coeff (c, j) /
+ sqrt (gsl_matrix_get (cov, cov->size1 - 1, cov->size2 - 1));
+ tab_double (t, 4, this_row, 0, beta, NULL, RC_OTHER);
/*
- Test statistic for H0: coefficient is 0.
- */
+ Test statistic for H0: coefficient is 0.
+ */
t_stat = linreg_coeff (c, j) / std_err;
- tab_double (t, 5, this_row, 0, t_stat, NULL);
+ tab_double (t, 5, this_row, 0, t_stat, NULL, RC_OTHER);
/*
- P values for the test statistic above.
- */
- pval =
- 2 * gsl_cdf_tdist_Q (fabs (t_stat),
- (double) (linreg_n_obs (c) - linreg_n_coeffs (c)));
- tab_double (t, 6, this_row, 0, pval, NULL);
+ P values for the test statistic above.
+ */
+ pval = 2 * gsl_cdf_tdist_Q (fabs (t_stat), df);
+ tab_double (t, 6, this_row, 0, pval, NULL, RC_PVALUE);
ds_destroy (&tstr);
+
+ if (cmd->stats & STATS_CI)
+ {
+ double lower = linreg_coeff (c, j) - tval * std_err ;
+ double upper = linreg_coeff (c, j) + tval * std_err ;
+
+ tab_double (t, 7, this_row, 0, lower, NULL, RC_OTHER);
+ tab_double (t, 8, this_row, 0, upper, NULL, RC_OTHER);
+ }
}
tab_title (t, _("Coefficients (%s)"), var_to_string (var));
tab_submit (t);
Display the ANOVA table.
*/
static void
-reg_stats_anova (linreg * c, void *aux UNUSED, const struct variable *var)
+reg_stats_anova (const linreg * c, const struct variable *var)
{
int n_cols = 7;
int n_rows = 4;
tab_text (t, 3, 0, TAB_CENTER | TAT_TITLE, _("df"));
tab_text (t, 4, 0, TAB_CENTER | TAT_TITLE, _("Mean Square"));
tab_text (t, 5, 0, TAB_CENTER | TAT_TITLE, _("F"));
- tab_text (t, 6, 0, TAB_CENTER | TAT_TITLE, _("Significance"));
+ tab_text (t, 6, 0, TAB_CENTER | TAT_TITLE, _("Sig."));
tab_text (t, 1, 1, TAB_LEFT | TAT_TITLE, _("Regression"));
tab_text (t, 1, 2, TAB_LEFT | TAT_TITLE, _("Residual"));
tab_text (t, 1, 3, TAB_LEFT | TAT_TITLE, _("Total"));
/* Sums of Squares */
- tab_double (t, 2, 1, 0, linreg_ssreg (c), NULL);
- tab_double (t, 2, 3, 0, linreg_sst (c), NULL);
- tab_double (t, 2, 2, 0, linreg_sse (c), NULL);
+ tab_double (t, 2, 1, 0, linreg_ssreg (c), NULL, RC_OTHER);
+ tab_double (t, 2, 3, 0, linreg_sst (c), NULL, RC_OTHER);
+ tab_double (t, 2, 2, 0, linreg_sse (c), NULL, RC_OTHER);
/* Degrees of freedom */
- tab_text_format (t, 3, 1, TAB_RIGHT, "%g", c->dfm);
- tab_text_format (t, 3, 2, TAB_RIGHT, "%g", c->dfe);
- tab_text_format (t, 3, 3, TAB_RIGHT, "%g", c->dft);
+ tab_text_format (t, 3, 1, TAB_RIGHT, "%.*g", DBL_DIG + 1, c->dfm);
+ tab_text_format (t, 3, 2, TAB_RIGHT, "%.*g", DBL_DIG + 1, c->dfe);
+ tab_text_format (t, 3, 3, TAB_RIGHT, "%.*g", DBL_DIG + 1, c->dft);
/* Mean Squares */
- tab_double (t, 4, 1, TAB_RIGHT, msm, NULL);
- tab_double (t, 4, 2, TAB_RIGHT, mse, NULL);
+ tab_double (t, 4, 1, TAB_RIGHT, msm, NULL, RC_OTHER);
+ tab_double (t, 4, 2, TAB_RIGHT, mse, NULL, RC_OTHER);
- tab_double (t, 5, 1, 0, F, NULL);
+ tab_double (t, 5, 1, 0, F, NULL, RC_OTHER);
- tab_double (t, 6, 1, 0, pval, NULL);
+ tab_double (t, 6, 1, 0, pval, NULL, RC_PVALUE);
tab_title (t, _("ANOVA (%s)"), var_to_string (var));
tab_submit (t);
static void
-reg_stats_bcov (linreg * c, void *aux UNUSED, const struct variable *var)
+reg_stats_bcov (const linreg * c, const struct variable *var)
{
int n_cols;
int n_rows;
tab_text (t, 2, i, TAB_CENTER, label);
tab_text (t, i + 2, 0, TAB_CENTER, label);
for (k = 1; k < linreg_n_coeffs (c); k++)
- {
- col = (i <= k) ? k : i;
- row = (i <= k) ? i : k;
- tab_double (t, k + 2, i, TAB_CENTER,
- gsl_matrix_get (c->cov, row, col), NULL);
- }
+ {
+ col = (i <= k) ? k : i;
+ row = (i <= k) ? i : k;
+ tab_double (t, k + 2, i, TAB_CENTER,
+ gsl_matrix_get (c->cov, row, col), NULL, RC_OTHER);
+ }
}
tab_title (t, _("Coefficient Correlations (%s)"), var_to_string (var));
tab_submit (t);
}
-static void
-statistics_keyword_output (void (*function) (linreg *, void *, const struct variable *var),
- bool keyword, linreg * c, void *aux, const struct variable *var)
-{
- if (keyword)
- {
- (*function) (c, aux, var);
- }
-}
projects / pspp / blobdiff