/* 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, 2016 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>
#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;
};
-struct per_split_ws
-{
- linreg **models;
-};
-
struct regression_workspace
{
- struct per_split_ws *psw;
-
/* 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 per_split_ws *psw,
struct regression_workspace *ws,
struct casereader *input);
int
cmd_regression (struct lexer *lexer, struct dataset *ds)
{
- int i;
- int n_splits = 0;
struct regression_workspace workspace;
struct regression regression;
const struct dictionary *dict = dataset_dict (ds);
bool save;
- workspace.psw = NULL;
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;
if (lex_match_id (lexer, "DEPENDENT"))
{
- if (!lex_force_match (lexer, T_EQUALS))
- goto error;
+ lex_match (lexer, T_EQUALS);
+ free (regression.dep_vars);
+ regression.n_dep_vars = 0;
+
if (!parse_variables_const (lexer, dict,
®ression.dep_vars,
®ression.n_dep_vars,
}
else if (lex_match_id (lexer, "STATISTICS"))
{
+ unsigned long statistics = 0;
lex_match (lexer, T_EQUALS);
while (lex_token (lexer) != T_ENDCMD
{
if (lex_match (lexer, T_ALL))
{
+ statistics = ~0;
}
else if (lex_match_id (lexer, "DEFAULTS"))
{
+ statistics |= STATS_DEFAULT;
}
else if (lex_match_id (lexer, "R"))
{
+ statistics |= STATS_R;
}
else if (lex_match_id (lexer, "COEFF"))
{
+ statistics |= STATS_COEFF;
}
else if (lex_match_id (lexer, "ANOVA"))
{
+ statistics |= STATS_ANOVA;
}
else if (lex_match_id (lexer, "BCOV"))
{
+ statistics |= STATS_BCOV;
+ }
+ else if (lex_match_id (lexer, "CI"))
+ {
+ statistics |= 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
{
goto error;
}
}
+
+ if (statistics)
+ regression.stats = statistics;
+
}
else if (lex_match_id (lexer, "SAVE"))
{
workspace.pred_idx = -1;
workspace.writer = NULL;
workspace.reader = NULL;
+ workspace.residvars = NULL;
+ workspace.predvars = NULL;
if (save)
{
int i;
if (regression.resid)
{
- workspace.extras ++;
- workspace.res_idx = 0;
+ workspace.res_idx = workspace.extras ++;
workspace.residvars = xcalloc (regression.n_dep_vars, sizeof (*workspace.residvars));
for (i = 0; i < regression.n_dep_vars; ++i)
if (regression.pred)
{
- workspace.extras ++;
- workspace.pred_idx = 1;
+ workspace.pred_idx = workspace.extras ++;
workspace.predvars = xcalloc (regression.n_dep_vars, sizeof (*workspace.predvars));
for (i = 0; i < regression.n_dep_vars; ++i)
msg (SW, _("REGRESSION with SAVE ignores TEMPORARY. "
"Temporary transformations will be made permanent."));
+ if (dict_get_filter (dict))
+ msg (SW, _("REGRESSION with SAVE ignores FILTER. "
+ "All cases will be processed."));
+
workspace.writer = autopaging_writer_create (proto);
caseproto_unref (proto);
}
- n_splits = 0;
{
struct casegrouper *grouper;
struct casereader *group;
while (casegrouper_get_next_group (grouper, &group))
{
- workspace.psw = xrealloc (workspace.psw, ++n_splits * sizeof (*workspace.psw));
-
- run_regression (®ression, &workspace.psw[n_splits - 1],
+ run_regression (®ression,
&workspace,
group);
add_transformation (ds, save_trans_func, save_trans_free, save_trans_data);
}
- for (i = 0; i < n_splits; ++i)
- {
- int k;
-
- for (k = 0; k < regression.n_dep_vars; ++k)
- linreg_unref (workspace.psw[i].models[k]);
-
- free (workspace.psw[i].models);
- }
- free (workspace.psw);
-
free (regression.vars);
free (regression.dep_vars);
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])
}
if (absent)
{
- vars[i + cmd->n_vars] = cmd->dep_vars[i];
+ vars[cmd->n_vars + x++] = cmd->dep_vars[i];
}
}
}
*/
msg (SW,
gettext
- ("The dependent variable is equal to the independent variable."
- "The least squares line is therefore Y=X."
+ ("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];
STATISTICS subcommand output functions.
*/
static void reg_stats_r (const linreg *, const struct variable *);
-static void reg_stats_coeff (const linreg *, const gsl_matrix *, 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 *);
subcommand_statistics (const struct regression *cmd, const linreg * c, const gsl_matrix * cm,
const struct variable *var)
{
- if (cmd->r)
+ if (cmd->stats & STATS_R)
reg_stats_r (c, var);
- if (cmd->anova)
+ if (cmd->stats & STATS_ANOVA)
reg_stats_anova (c, var);
- if (cmd->coeff)
- reg_stats_coeff (c, cm, var);
+ if (cmd->stats & STATS_COEFF)
+ reg_stats_coeff (c, cm, var, cmd);
- if (cmd->bcov)
+ if (cmd->stats & STATS_BCOV)
reg_stats_bcov (c, var);
}
static void
run_regression (const struct regression *cmd,
- struct per_split_ws *psw,
struct regression_workspace *ws,
struct casereader *input)
{
size_t i;
+ linreg **models;
int k;
struct ccase *c;
casereader_destroy (r);
}
- psw->models = xcalloc (cmd->n_dep_vars, sizeof (*psw->models));
+ models = xcalloc (cmd->n_dep_vars, sizeof (*models));
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);
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);
- psw->models[k] = linreg_alloc (dep_var, vars, n_data, n_indep);
- psw->models[k]->depvar = dep_var;
+ 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 (psw->models[k], i, means[i]);
+ linreg_set_indep_variable_mean (models[k], i, means[i]);
}
- linreg_set_depvar_mean (psw->models[k], means[i]);
+ linreg_set_depvar_mean (models[k], means[i]);
/*
For large data sets, use QR decomposition.
*/
if (n_data > sqrt (n_indep) && n_data > REG_LARGE_DATA)
{
- psw->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, psw->models[k]);
+ linreg_fit (this_cm, models[k]);
if (!taint_has_tainted_successor (casereader_get_taint (input)))
{
- subcommand_statistics (cmd, psw->models[k], this_cm, dep_var);
+ subcommand_statistics (cmd, models[k], this_cm, dep_var);
}
}
else
for (; (c = casereader_read (r)) != NULL; case_unref (c))
{
- struct ccase *outc = case_clone (c);
+ struct ccase *outc = case_create (casewriter_get_proto (ws->writer));
for (k = 0; k < cmd->n_dep_vars; k++)
{
const struct variable **vars = xnmalloc (cmd->n_vars, sizeof (*vars));
if (cmd->pred)
{
- double pred = linreg_predict (psw->models[k], vals, n_indep);
+ 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, psw->models[k]->depvar)->f;
- double res = linreg_residual (psw->models[k], obs, vals, n_indep);
+ 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);
casereader_destroy (reader);
+ 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
-
+\f
static void
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 (const linreg * c, const gsl_matrix *cov, 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;
+ 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);
+ 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, 1, 0, pval, NULL);
+ 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);
+ tab_double (t, 2, this_row, 0, linreg_coeff (c, j), NULL, RC_OTHER);
/*
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.
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);
+ tab_double (t, 4, this_row, 0, beta, NULL, RC_OTHER);
/*
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) - 1));
- tab_double (t, 6, this_row, 0, pval, NULL);
+ 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);
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);
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);
+ gsl_matrix_get (c->cov, row, col), NULL, RC_OTHER);
}
}
tab_title (t, _("Coefficient Correlations (%s)"), var_to_string (var));
projects / pspp / blobdiff