/* PSPP - a program for statistical analysis.
- Copyright (C) 2010 Free Software Foundation, Inc.
+ Copyright (C) 2010, 2011 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 <data/case.h>
-#include <data/casegrouper.h>
-#include <data/casereader.h>
-
-#include <math/covariance.h>
-#include <math/categoricals.h>
-#include <math/moments.h>
-#include <gsl/gsl_matrix.h>
-#include <linreg/sweep.h>
-
-#include <libpspp/ll.h>
-
-#include <language/lexer/lexer.h>
-#include <language/lexer/variable-parser.h>
-#include <language/lexer/value-parser.h>
-#include <language/command.h>
-
-#include <data/procedure.h>
-#include <data/value.h>
-#include <data/dictionary.h>
-
-#include <language/dictionary/split-file.h>
-#include <libpspp/taint.h>
-#include <libpspp/misc.h>
-
#include <gsl/gsl_cdf.h>
+#include <gsl/gsl_matrix.h>
#include <math.h>
-#include <data/format.h>
-
-#include <libpspp/message.h>
-#include <output/tab.h>
+#include "data/case.h"
+#include "data/casegrouper.h"
+#include "data/casereader.h"
+#include "data/dataset.h"
+#include "data/dictionary.h"
+#include "data/format.h"
+#include "data/value.h"
+#include "language/command.h"
+#include "language/dictionary/split-file.h"
+#include "language/lexer/lexer.h"
+#include "language/lexer/value-parser.h"
+#include "language/lexer/variable-parser.h"
+#include "libpspp/ll.h"
+#include "libpspp/message.h"
+#include "libpspp/misc.h"
+#include "libpspp/taint.h"
+#include "linreg/sweep.h"
+#include "math/categoricals.h"
+#include "math/covariance.h"
+#include "math/moments.h"
+#include "output/tab.h"
#include "gettext.h"
#define _(msgid) gettext (msgid)
size_t n_factor_vars;
const struct variable **factor_vars;
+ /* In the current implementation, design_vars will
+ normally be the same as factor_vars.
+ This will change once interactions, nested variables
+ and repeated measures become involved.
+ */
+ size_t n_design_vars;
+ const struct variable **design_vars;
+
enum mv_class exclude;
/* The weight variable */
const struct variable *wv;
+ const struct dictionary *dict;
+
bool intercept;
+
+ double alpha;
};
struct glm_workspace
{
double total_ssq;
struct moments *totals;
+
+ struct categoricals *cats;
+
+ /*
+ Sums of squares due to different variables. Element 0 is the SSE
+ for the entire model. For i > 0, element i is the SS due to
+ variable i.
+ */
+ gsl_vector *ssq;
};
-static void output_glm (const struct glm_spec *, const struct glm_workspace *ws);
-static void run_glm (const struct glm_spec *cmd, struct casereader *input, const struct dataset *ds);
+static void output_glm (const struct glm_spec *,
+ const struct glm_workspace *ws);
+static void run_glm (struct glm_spec *cmd, struct casereader *input,
+ const struct dataset *ds);
+
+
+static bool parse_design_spec (struct lexer *lexer, struct glm_spec *glm);
+
int
cmd_glm (struct lexer *lexer, struct dataset *ds)
{
- const struct dictionary *dict = dataset_dict (ds);
- struct glm_spec glm ;
+ struct const_var_set *factors = NULL;
+ struct glm_spec glm;
+ bool design = false;
+ glm.dict = dataset_dict (ds);
glm.n_dep_vars = 0;
glm.n_factor_vars = 0;
+ glm.n_design_vars = 0;
glm.dep_vars = NULL;
glm.factor_vars = NULL;
+ glm.design_vars = NULL;
glm.exclude = MV_ANY;
glm.intercept = true;
- glm.wv = dict_get_weight (dict);
+ glm.wv = dict_get_weight (glm.dict);
+ glm.alpha = 0.05;
-
- if (!parse_variables_const (lexer, dict,
+ if (!parse_variables_const (lexer, glm.dict,
&glm.dep_vars, &glm.n_dep_vars,
PV_NO_DUPLICATE | PV_NUMERIC))
goto error;
lex_force_match (lexer, T_BY);
- if (!parse_variables_const (lexer, dict,
+ if (!parse_variables_const (lexer, glm.dict,
&glm.factor_vars, &glm.n_factor_vars,
PV_NO_DUPLICATE | PV_NUMERIC))
goto error;
- if ( glm.n_dep_vars > 1)
+ if (glm.n_dep_vars > 1)
{
msg (ME, _("Multivariate analysis is not yet implemented"));
return CMD_FAILURE;
}
- struct const_var_set *factors = const_var_set_create_from_array (glm.factor_vars, glm.n_factor_vars);
-
+ factors =
+ const_var_set_create_from_array (glm.factor_vars, glm.n_factor_vars);
while (lex_token (lexer) != T_ENDCMD)
{
lex_match (lexer, T_SLASH);
if (lex_match_id (lexer, "MISSING"))
- {
- lex_match (lexer, T_EQUALS);
- while (lex_token (lexer) != T_ENDCMD && lex_token (lexer) != T_SLASH)
- {
+ {
+ lex_match (lexer, T_EQUALS);
+ while (lex_token (lexer) != T_ENDCMD
+ && lex_token (lexer) != T_SLASH)
+ {
if (lex_match_id (lexer, "INCLUDE"))
{
glm.exclude = MV_SYSTEM;
}
else
{
- lex_error (lexer, NULL);
+ lex_error (lexer, NULL);
goto error;
}
}
}
else if (lex_match_id (lexer, "INTERCEPT"))
- {
- lex_match (lexer, T_EQUALS);
- while (lex_token (lexer) != T_ENDCMD && lex_token (lexer) != T_SLASH)
- {
+ {
+ lex_match (lexer, T_EQUALS);
+ while (lex_token (lexer) != T_ENDCMD
+ && lex_token (lexer) != T_SLASH)
+ {
if (lex_match_id (lexer, "INCLUDE"))
{
glm.intercept = true;
}
else
{
- lex_error (lexer, NULL);
+ lex_error (lexer, NULL);
goto error;
}
}
}
- else if (lex_match_id (lexer, "DESIGN"))
- {
- size_t n_des;
- const struct variable **des;
- lex_match (lexer, T_EQUALS);
+ else if (lex_match_id (lexer, "CRITERIA"))
+ {
+ lex_match (lexer, T_EQUALS);
+ if (lex_match_id (lexer, "ALPHA"))
+ {
+ if (lex_force_match (lexer, T_LPAREN))
+ {
+ if (! lex_force_num (lexer))
+ {
+ lex_error (lexer, NULL);
+ goto error;
+ }
+
+ glm.alpha = lex_number (lexer);
+ lex_get (lexer);
+ if ( ! lex_force_match (lexer, T_RPAREN))
+ {
+ lex_error (lexer, NULL);
+ goto error;
+ }
+ }
+ }
+ else
+ {
+ lex_error (lexer, NULL);
+ goto error;
+ }
+ }
+ else if (lex_match_id (lexer, "METHOD"))
+ {
+ lex_match (lexer, T_EQUALS);
+ if ( !lex_force_match_id (lexer, "SSTYPE"))
+ {
+ lex_error (lexer, NULL);
+ goto error;
+ }
+
+ if ( ! lex_force_match (lexer, T_LPAREN))
+ {
+ lex_error (lexer, NULL);
+ goto error;
+ }
+
+ if ( ! lex_force_int (lexer))
+ {
+ lex_error (lexer, NULL);
+ goto error;
+ }
- parse_const_var_set_vars (lexer, factors, &des, &n_des, 0);
+ if (3 != lex_integer (lexer))
+ {
+ msg (ME, _("Only type 3 sum of squares are currently implemented"));
+ goto error;
+ }
+
+ lex_get (lexer);
+
+ if ( ! lex_force_match (lexer, T_RPAREN))
+ {
+ lex_error (lexer, NULL);
+ goto error;
+ }
+ }
+ else if (lex_match_id (lexer, "DESIGN"))
+ {
+ lex_match (lexer, T_EQUALS);
+
+ if (! parse_design_spec (lexer, &glm))
+ goto error;
+
+ if ( glm.n_design_vars == 0)
+ {
+ msg (ME, _("One or more design variables must be given"));
+ goto error;
+ }
+
+ design = true;
}
else
{
}
}
+ if ( ! design )
+ {
+ lex_error (lexer, _("/DESIGN is mandatory in GLM"));
+ goto error;
+ }
{
struct casegrouper *grouper;
struct casereader *group;
bool ok;
- grouper = casegrouper_create_splits (proc_open (ds), dict);
+ grouper = casegrouper_create_splits (proc_open (ds), glm.dict);
while (casegrouper_get_next_group (grouper, &group))
run_glm (&glm, group, ds);
ok = casegrouper_destroy (grouper);
ok = proc_commit (ds) && ok;
}
+ const_var_set_destroy (factors);
+ free (glm.factor_vars);
+ free (glm.design_vars);
+ free (glm.dep_vars);
+
+
return CMD_SUCCESS;
- error:
+error:
+
+ const_var_set_destroy (factors);
+ free (glm.factor_vars);
+ free (glm.design_vars);
+ free (glm.dep_vars);
+
return CMD_FAILURE;
}
-static void dump_matrix (const gsl_matrix *m);
+static void get_ssq (struct covariance *, gsl_vector *,
+ const struct glm_spec *);
+
+static bool
+not_dropped (size_t j, size_t * dropped, size_t n_dropped)
+{
+ size_t i;
+
+ for (i = 0; i < n_dropped; i++)
+ {
+ if (j == dropped[i])
+ return false;
+ }
+ return true;
+}
static void
-run_glm (const struct glm_spec *cmd, struct casereader *input, const struct dataset *ds)
+get_ssq (struct covariance *cov, gsl_vector * ssq, const struct glm_spec *cmd)
{
+ const struct variable **vars;
+ gsl_matrix *small_cov = NULL;
+ gsl_matrix *cm = covariance_calculate_unnormalized (cov);
+ size_t i;
+ size_t j;
+ size_t k;
+ size_t n;
+ size_t m;
+ size_t *dropped;
+ size_t n_dropped;
+
+ dropped = xcalloc (covariance_dim (cov), sizeof (*dropped));
+ vars = xcalloc (covariance_dim (cov), sizeof (*vars));
+ covariance_get_var_indices (cov, vars);
+
+ for (k = 0; k < cmd->n_design_vars; k++)
+ {
+ n_dropped = 0;
+ for (i = 1; i < covariance_dim (cov); i++)
+ {
+ if (vars[i] == cmd->design_vars[k])
+ {
+ dropped[n_dropped++] = i;
+ }
+ }
+ small_cov =
+ gsl_matrix_alloc (cm->size1 - n_dropped, cm->size2 - n_dropped);
+ gsl_matrix_set (small_cov, 0, 0, gsl_matrix_get (cm, 0, 0));
+ n = 0;
+ m = 0;
+ for (i = 0; i < cm->size1; i++)
+ {
+ if (not_dropped (i, dropped, n_dropped))
+ {
+ m = 0;
+ for (j = 0; j < cm->size2; j++)
+ {
+ if (not_dropped (j, dropped, n_dropped))
+ {
+ gsl_matrix_set (small_cov, n, m,
+ gsl_matrix_get (cm, i, j));
+ m++;
+ }
+ }
+ n++;
+ }
+ }
+ reg_sweep (small_cov, 0);
+ gsl_vector_set (ssq, k + 1,
+ gsl_matrix_get (small_cov, 0, 0)
+ - gsl_vector_get (ssq, 0));
+ gsl_matrix_free (small_cov);
+ }
+
+ free (dropped);
+ free (vars);
+ gsl_matrix_free (cm);
+}
+
+//static void dump_matrix (const gsl_matrix *m);
+
+static void
+run_glm (struct glm_spec *cmd, struct casereader *input,
+ const struct dataset *ds)
+{
+ bool warn_bad_weight = true;
int v;
struct taint *taint;
struct dictionary *dict = dataset_dict (ds);
struct ccase *c;
struct glm_workspace ws;
+ struct covariance *cov;
+ ws.cats = categoricals_create (cmd->design_vars, cmd->n_design_vars,
+ cmd->wv, cmd->exclude,
+ NULL, NULL, NULL, NULL);
- struct categoricals *cats = categoricals_create (cmd->factor_vars, cmd->n_factor_vars,
- cmd->wv, cmd->exclude,
- NULL, NULL,
- NULL, NULL);
-
- struct covariance *cov = covariance_2pass_create (cmd->n_dep_vars, cmd->dep_vars,
- cats,
- cmd->wv, cmd->exclude);
+ cov = covariance_2pass_create (cmd->n_dep_vars, cmd->dep_vars,
+ ws.cats, cmd->wv, cmd->exclude);
c = casereader_peek (input, 0);
ws.totals = moments_create (MOMENT_VARIANCE);
- bool warn_bad_weight = true;
for (reader = casereader_clone (input);
(c = casereader_read (reader)) != NULL; case_unref (c))
{
double weight = dict_get_case_weight (dict, c, &warn_bad_weight);
- for ( v = 0; v < cmd->n_dep_vars; ++v)
- moments_pass_one (ws.totals, case_data (c, cmd->dep_vars[v])->f, weight);
+ for (v = 0; v < cmd->n_dep_vars; ++v)
+ moments_pass_one (ws.totals, case_data (c, cmd->dep_vars[v])->f,
+ weight);
covariance_accumulate_pass1 (cov, c);
}
casereader_destroy (reader);
- categoricals_done (cats);
+ categoricals_done (ws.cats);
- for (reader = casereader_clone (input);
+ for (reader = input;
(c = casereader_read (reader)) != NULL; case_unref (c))
{
double weight = dict_get_case_weight (dict, c, &warn_bad_weight);
- for ( v = 0; v < cmd->n_dep_vars; ++v)
- moments_pass_two (ws.totals, case_data (c, cmd->dep_vars[v])->f, weight);
+ for (v = 0; v < cmd->n_dep_vars; ++v)
+ moments_pass_two (ws.totals, case_data (c, cmd->dep_vars[v])->f,
+ weight);
covariance_accumulate_pass2 (cov, c);
}
{
gsl_matrix *cm = covariance_calculate_unnormalized (cov);
- dump_matrix (cm);
+ // dump_matrix (cm);
ws.total_ssq = gsl_matrix_get (cm, 0, 0);
reg_sweep (cm, 0);
- dump_matrix (cm);
+ /*
+ Store the overall SSE.
+ */
+ ws.ssq = gsl_vector_alloc (cm->size1);
+ gsl_vector_set (ws.ssq, 0, gsl_matrix_get (cm, 0, 0));
+ get_ssq (cov, ws.ssq, cmd);
+ // dump_matrix (cm);
+
+ gsl_matrix_free (cm);
}
if (!taint_has_tainted_successor (taint))
output_glm (cmd, &ws);
+ gsl_vector_free (ws.ssq);
+
+ covariance_destroy (cov);
+ moments_destroy (ws.totals);
+
taint_destroy (taint);
}
static void
output_glm (const struct glm_spec *cmd, const struct glm_workspace *ws)
{
- const struct fmt_spec *wfmt = cmd->wv ? var_get_print_format (cmd->wv) : &F_8_0;
+ const struct fmt_spec *wfmt =
+ cmd->wv ? var_get_print_format (cmd->wv) : &F_8_0;
+
+ double n_total, mean;
+ double df_corr = 0.0;
+ double mse = 0;
int f;
int r;
const int heading_columns = 1;
const int heading_rows = 1;
- struct tab_table *t ;
+ struct tab_table *t;
const int nc = 6;
- int nr = heading_rows + 4 + cmd->n_factor_vars;
+ int nr = heading_rows + 4 + cmd->n_design_vars;
if (cmd->intercept)
nr++;
tab_headers (t, heading_columns, 0, heading_rows, 0);
- tab_box (t,
- TAL_2, TAL_2,
- -1, TAL_1,
- 0, 0,
- nc - 1, nr - 1);
+ tab_box (t, TAL_2, TAL_2, -1, TAL_1, 0, 0, nc - 1, nr - 1);
tab_hline (t, TAL_2, 0, nc - 1, heading_rows);
tab_vline (t, TAL_2, heading_columns, 0, nr - 1);
tab_text (t, 0, 0, TAB_CENTER | TAT_TITLE, _("Source"));
/* TRANSLATORS: The parameter is a roman numeral */
- tab_text_format (t, 1, 0, TAB_CENTER | TAT_TITLE, _("Type %s Sum of Squares"), "III");
+ tab_text_format (t, 1, 0, TAB_CENTER | TAT_TITLE,
+ _("Type %s Sum of Squares"), "III");
tab_text (t, 2, 0, TAB_CENTER | TAT_TITLE, _("df"));
tab_text (t, 3, 0, TAB_CENTER | TAT_TITLE, _("Mean Square"));
tab_text (t, 4, 0, TAB_CENTER | TAT_TITLE, _("F"));
tab_text (t, 5, 0, TAB_CENTER | TAT_TITLE, _("Sig."));
+ moments_calculate (ws->totals, &n_total, &mean, NULL, NULL, NULL);
+
+ if (cmd->intercept)
+ df_corr += 1.0;
+
+ for (f = 0; f < cmd->n_design_vars; ++f)
+ df_corr += categoricals_n_count (ws->cats, f) - 1.0;
+
+ mse = gsl_vector_get (ws->ssq, 0) / (n_total - df_corr);
+
r = heading_rows;
- tab_text (t, 0, r++, TAB_LEFT | TAT_TITLE, _("Corrected Model"));
+ tab_text (t, 0, r, TAB_LEFT | TAT_TITLE, _("Corrected Model"));
+
+ r++;
- double intercept, n_total;
if (cmd->intercept)
{
- double mean;
- moments_calculate (ws->totals, &n_total, &mean, NULL, NULL, NULL);
- intercept = pow2 (mean * n_total) / n_total;
-
+ const double intercept = pow2 (mean * n_total) / n_total;
+ const double df = 1.0;
+ const double F = intercept / df / mse;
tab_text (t, 0, r, TAB_LEFT | TAT_TITLE, _("Intercept"));
tab_double (t, 1, r, 0, intercept, NULL);
tab_double (t, 2, r, 0, 1.00, wfmt);
-
- tab_double (t, 3, r, 0, intercept / 1.0 , NULL);
+ tab_double (t, 3, r, 0, intercept / df, NULL);
+ tab_double (t, 4, r, 0, F, NULL);
+ tab_double (t, 5, r, 0, gsl_cdf_fdist_Q (F, df, n_total - df_corr),
+ NULL);
r++;
}
- for (f = 0; f < cmd->n_factor_vars; ++f)
+ for (f = 0; f < cmd->n_design_vars; ++f)
{
- tab_text (t, 0, r++, TAB_LEFT | TAT_TITLE,
- var_to_string (cmd->factor_vars[f]));
+ const double df = categoricals_n_count (ws->cats, f) - 1.0;
+ const double ssq = gsl_vector_get (ws->ssq, f + 1);
+ const double F = ssq / df / mse;
+ tab_text (t, 0, r, TAB_LEFT | TAT_TITLE,
+ var_to_string (cmd->design_vars[f]));
+
+ tab_double (t, 1, r, 0, ssq, NULL);
+ tab_double (t, 2, r, 0, df, wfmt);
+ tab_double (t, 3, r, 0, ssq / df, NULL);
+ tab_double (t, 4, r, 0, F, NULL);
+
+ tab_double (t, 5, r, 0, gsl_cdf_fdist_Q (F, df, n_total - df_corr),
+ NULL);
+
+
+ r++;
}
- tab_text (t, 0, r++, TAB_LEFT | TAT_TITLE, _("Error"));
+ {
+ /* Corrected Model */
+ const double df = df_corr - 1.0;
+ const double ssq = ws->total_ssq - gsl_vector_get (ws->ssq, 0);
+ const double F = ssq / df / mse;
+ tab_double (t, 1, heading_rows, 0, ssq, NULL);
+ tab_double (t, 2, heading_rows, 0, df, wfmt);
+ tab_double (t, 3, heading_rows, 0, ssq / df, NULL);
+ tab_double (t, 4, heading_rows, 0, F, NULL);
+
+ tab_double (t, 5, heading_rows, 0,
+ gsl_cdf_fdist_Q (F, df, n_total - df_corr), NULL);
+ }
+
+ {
+ const double df = n_total - df_corr;
+ const double ssq = gsl_vector_get (ws->ssq, 0);
+ const double mse = ssq / df;
+ tab_text (t, 0, r, TAB_LEFT | TAT_TITLE, _("Error"));
+ tab_double (t, 1, r, 0, ssq, NULL);
+ tab_double (t, 2, r, 0, df, wfmt);
+ tab_double (t, 3, r++, 0, mse, NULL);
+ }
if (cmd->intercept)
{
- double ssq = intercept + ws->total_ssq;
- double mse = ssq / n_total;
+ const double intercept = pow2 (mean * n_total) / n_total;
+ const double ssq = intercept + ws->total_ssq;
+
tab_text (t, 0, r, TAB_LEFT | TAT_TITLE, _("Total"));
tab_double (t, 1, r, 0, ssq, NULL);
tab_double (t, 2, r, 0, n_total, wfmt);
tab_text (t, 0, r, TAB_LEFT | TAT_TITLE, _("Corrected Total"));
+
tab_double (t, 1, r, 0, ws->total_ssq, NULL);
tab_double (t, 2, r, 0, n_total - 1.0, wfmt);
tab_submit (t);
}
-static
-void dump_matrix (const gsl_matrix *m)
+#if 0
+static void
+dump_matrix (const gsl_matrix * m)
{
size_t i, j;
for (i = 0; i < m->size1; ++i)
}
printf ("\n");
}
+#endif
+
+
+\f
+
+/* Match a variable.
+ If the match succeeds, the variable will be placed in VAR.
+ Returns true if successful */
+static bool
+lex_match_variable (struct lexer *lexer, const struct glm_spec *glm, const struct variable **var)
+{
+ if (lex_token (lexer) != T_ID)
+ return false;
+
+ *var = parse_variable_const (lexer, glm->dict);
+
+ if ( *var == NULL)
+ return false;
+ return true;
+}
+
+/* An interaction is a variable followed by {*, BY} followed by an interaction */
+static bool
+parse_design_interaction (struct lexer *lexer, struct glm_spec *glm)
+{
+ const struct variable *v = NULL;
+ if (! lex_match_variable (lexer, glm, &v))
+ return false;
+
+ if ( lex_match (lexer, T_ASTERISK) || lex_match (lexer, T_BY))
+ {
+ lex_error (lexer, "Interactions are not yet implemented"); return false;
+ return parse_design_interaction (lexer, glm);
+ }
+
+ glm->n_design_vars++;
+ glm->design_vars = xrealloc (glm->design_vars, sizeof (*glm->design_vars) * glm->n_design_vars);
+ glm->design_vars[glm->n_design_vars - 1] = v;
+
+ return true;
+}
+
+/* A design term is a varible OR an interaction */
+static bool
+parse_design_term (struct lexer *lexer, struct glm_spec *glm)
+{
+ const struct variable *v = NULL;
+ if (parse_design_interaction (lexer, glm))
+ return true;
+
+ /* FIXME: This should accept nexted variables */
+ if ( lex_match_variable (lexer, glm, &v))
+ {
+ return true;
+ }
+
+ return false;
+}
+
+
+
+/* Parse a complete DESIGN specification.
+ A design spec is a design term, optionally followed by a comma,
+ and another design spec.
+*/
+static bool
+parse_design_spec (struct lexer *lexer, struct glm_spec *glm)
+{
+ /* Kludge: Return success if end of design spec */
+ if (lex_token (lexer) == T_ENDCMD || lex_token (lexer) == T_SLASH)
+ return true;
+
+ if ( ! parse_design_term (lexer, glm))
+ return false;
+
+ lex_match (lexer, T_COMMA);
+
+ return parse_design_spec (lexer, glm);
+}
+
projects / pspp / blobdiff