/* PSPP - a program for statistical analysis.
- Copyright (C) 2010, 2011 Free Software Foundation, Inc.
+ Copyright (C) 2010, 2011, 2012 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 <gsl/gsl_cdf.h>
#include <gsl/gsl_matrix.h>
+#include <gsl/gsl_combination.h>
#include <math.h>
#include "data/case.h"
#include "language/lexer/lexer.h"
#include "language/lexer/value-parser.h"
#include "language/lexer/variable-parser.h"
+#include "libpspp/assertion.h"
#include "libpspp/ll.h"
#include "libpspp/message.h"
#include "libpspp/misc.h"
#include "math/covariance.h"
#include "math/interaction.h"
#include "math/moments.h"
-#include "output/tab.h"
+#include "output/pivot-table.h"
#include "gettext.h"
+#define N_(msgid) msgid
#define _(msgid) gettext (msgid)
struct glm_spec
const struct dictionary *dict;
+ int ss_type;
bool intercept;
double alpha;
+
+ bool dump_coding;
};
struct glm_workspace
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;
};
+
+/* Default design: all possible interactions */
+static void
+design_full (struct glm_spec *glm)
+{
+ int sz;
+ int i = 0;
+ glm->n_interactions = (1 << glm->n_factor_vars) - 1;
+
+ glm->interactions = xcalloc (glm->n_interactions, sizeof *glm->interactions);
+
+ /* All subsets, with exception of the empty set, of [0, glm->n_factor_vars) */
+ for (sz = 1; sz <= glm->n_factor_vars; ++sz)
+ {
+ gsl_combination *c = gsl_combination_calloc (glm->n_factor_vars, sz);
+
+ do
+ {
+ struct interaction *iact = interaction_create (NULL);
+ int e;
+ for (e = 0 ; e < gsl_combination_k (c); ++e)
+ interaction_add_variable (iact, glm->factor_vars [gsl_combination_get (c, e)]);
+
+ glm->interactions[i++] = iact;
+ }
+ while (gsl_combination_next (c) == GSL_SUCCESS);
+
+ gsl_combination_free (c);
+ }
+}
+
static void output_glm (const struct glm_spec *,
const struct glm_workspace *ws);
static void run_glm (struct glm_spec *cmd, struct casereader *input,
glm.intercept = true;
glm.wv = dict_get_weight (glm.dict);
glm.alpha = 0.05;
+ glm.dump_coding = false;
+ glm.ss_type = 3;
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 (! lex_force_match (lexer, T_BY))
+ goto error;
if (!parse_variables_const (lexer, glm.dict,
&glm.factor_vars, &glm.n_factor_vars,
lex_error (lexer, NULL);
goto error;
}
-
+
glm.alpha = lex_number (lexer);
lex_get (lexer);
if ( ! lex_force_match (lexer, T_RPAREN))
goto error;
}
- if (3 != lex_integer (lexer))
+ glm.ss_type = lex_integer (lexer);
+ if (1 > glm.ss_type || 3 < glm.ss_type )
{
- msg (ME, _("Only type 3 sum of squares are currently implemented"));
+ msg (ME, _("Only types 1, 2 & 3 sums of squares are currently implemented"));
goto error;
}
if (! parse_design_spec (lexer, &glm))
goto error;
-
- if ( glm.n_interactions == 0)
- {
- msg (ME, _("One or more design variables must be given"));
- goto error;
- }
-
- design = true;
+
+ if (glm.n_interactions > 0)
+ design = true;
+ }
+ else if (lex_match_id (lexer, "SHOWCODES"))
+ /* Undocumented debug option */
+ {
+ lex_match (lexer, T_EQUALS);
+
+ glm.dump_coding = true;
}
else
{
if ( ! design )
{
- lex_error (lexer, _("/DESIGN is mandatory in GLM"));
- goto error;
+ design_full (&glm);
}
{
free (glm.factor_vars);
for (i = 0 ; i < glm.n_interactions; ++i)
interaction_destroy (glm.interactions[i]);
+
free (glm.interactions);
free (glm.dep_vars);
return CMD_FAILURE;
}
-static void get_ssq (struct covariance *, gsl_vector *,
- const struct glm_spec *);
+static inline bool
+not_dropped (size_t j, const bool *ff)
+{
+ return ! ff[j];
+}
-static bool
-not_dropped (size_t j, size_t * dropped, size_t n_dropped)
+static void
+fill_submatrix (const gsl_matrix * cov, gsl_matrix * submatrix, bool *dropped_f)
{
size_t i;
+ size_t j;
+ size_t n = 0;
+ size_t m = 0;
- for (i = 0; i < n_dropped; i++)
+ for (i = 0; i < cov->size1; i++)
{
- if (j == dropped[i])
- return false;
+ if (not_dropped (i, dropped_f))
+ {
+ m = 0;
+ for (j = 0; j < cov->size2; j++)
+ {
+ if (not_dropped (j, dropped_f))
+ {
+ gsl_matrix_set (submatrix, n, m,
+ gsl_matrix_get (cov, i, j));
+ m++;
+ }
+ }
+ n++;
+ }
}
- return true;
}
+
+/*
+ Type 1 sums of squares.
+ Populate SSQ with the Type 1 sums of squares according to COV
+ */
static void
-get_ssq (struct covariance *cov, gsl_vector *ssq, const struct glm_spec *cmd)
+ssq_type1 (struct covariance *cov, gsl_vector *ssq, const struct glm_spec *cmd)
{
- gsl_matrix *cm = covariance_calculate_unnormalized (cov);
+ const gsl_matrix *cm = covariance_calculate_unnormalized (cov);
size_t i;
- size_t j;
size_t k;
- size_t *dropped = xcalloc (covariance_dim (cov), sizeof (*dropped));
+ bool *model_dropped = xcalloc (covariance_dim (cov), sizeof (*model_dropped));
+ bool *submodel_dropped = xcalloc (covariance_dim (cov), sizeof (*submodel_dropped));
const struct categoricals *cats = covariance_get_categoricals (cov);
+ size_t n_dropped_model = 0;
+ size_t n_dropped_submodel = 0;
+
+ for (i = cmd->n_dep_vars; i < covariance_dim (cov); i++)
+ {
+ n_dropped_model++;
+ n_dropped_submodel++;
+ model_dropped[i] = true;
+ submodel_dropped[i] = true;
+ }
+
for (k = 0; k < cmd->n_interactions; k++)
{
- size_t n = 0;
- size_t m = 0;
- gsl_matrix *small_cov = NULL;
- size_t n_dropped = 0;
+ gsl_matrix *model_cov = NULL;
+ gsl_matrix *submodel_cov = NULL;
+
+ n_dropped_submodel = n_dropped_model;
+ for (i = cmd->n_dep_vars; i < covariance_dim (cov); i++)
+ {
+ submodel_dropped[i] = model_dropped[i];
+ }
+
for (i = cmd->n_dep_vars; i < covariance_dim (cov); i++)
{
- if (categoricals_get_interaction_by_subscript (cats, i - cmd->n_dep_vars)
- == cmd->interactions[k])
+ const struct interaction * x =
+ categoricals_get_interaction_by_subscript (cats, i - cmd->n_dep_vars);
+
+ if ( x == cmd->interactions [k])
{
- assert (n_dropped < covariance_dim (cov));
- dropped[n_dropped++] = i;
+ model_dropped[i] = false;
+ n_dropped_model--;
}
}
- 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));
- for (i = 0; i < cm->size1; i++)
+
+ model_cov = gsl_matrix_alloc (cm->size1 - n_dropped_model, cm->size2 - n_dropped_model);
+ submodel_cov = gsl_matrix_alloc (cm->size1 - n_dropped_submodel, cm->size2 - n_dropped_submodel);
+
+ fill_submatrix (cm, model_cov, model_dropped);
+ fill_submatrix (cm, submodel_cov, submodel_dropped);
+
+ reg_sweep (model_cov, 0);
+ reg_sweep (submodel_cov, 0);
+
+ gsl_vector_set (ssq, k + 1,
+ gsl_matrix_get (submodel_cov, 0, 0) - gsl_matrix_get (model_cov, 0, 0)
+ );
+
+ gsl_matrix_free (model_cov);
+ gsl_matrix_free (submodel_cov);
+ }
+
+ free (model_dropped);
+ free (submodel_dropped);
+}
+
+/*
+ Type 2 sums of squares.
+ Populate SSQ with the Type 2 sums of squares according to COV
+ */
+static void
+ssq_type2 (struct covariance *cov, gsl_vector *ssq, const struct glm_spec *cmd)
+{
+ const gsl_matrix *cm = covariance_calculate_unnormalized (cov);
+ size_t i;
+ size_t k;
+ bool *model_dropped = xcalloc (covariance_dim (cov), sizeof (*model_dropped));
+ bool *submodel_dropped = xcalloc (covariance_dim (cov), sizeof (*submodel_dropped));
+ const struct categoricals *cats = covariance_get_categoricals (cov);
+
+ for (k = 0; k < cmd->n_interactions; k++)
+ {
+ gsl_matrix *model_cov = NULL;
+ gsl_matrix *submodel_cov = NULL;
+ size_t n_dropped_model = 0;
+ size_t n_dropped_submodel = 0;
+ for (i = cmd->n_dep_vars; i < covariance_dim (cov); i++)
{
- if (not_dropped (i, dropped, n_dropped))
+ const struct interaction * x =
+ categoricals_get_interaction_by_subscript (cats, i - cmd->n_dep_vars);
+
+ model_dropped[i] = false;
+ submodel_dropped[i] = false;
+ if (interaction_is_subset (cmd->interactions [k], x))
{
- m = 0;
- for (j = 0; j < cm->size2; j++)
+ assert (n_dropped_submodel < covariance_dim (cov));
+ n_dropped_submodel++;
+ submodel_dropped[i] = true;
+
+ if ( cmd->interactions [k]->n_vars < x->n_vars)
{
- if (not_dropped (j, dropped, n_dropped))
- {
- gsl_matrix_set (small_cov, n, m,
- gsl_matrix_get (cm, i, j));
- m++;
- }
+ assert (n_dropped_model < covariance_dim (cov));
+ n_dropped_model++;
+ model_dropped[i] = true;
}
- n++;
}
}
- reg_sweep (small_cov, 0);
+
+ model_cov = gsl_matrix_alloc (cm->size1 - n_dropped_model, cm->size2 - n_dropped_model);
+ submodel_cov = gsl_matrix_alloc (cm->size1 - n_dropped_submodel, cm->size2 - n_dropped_submodel);
+
+ fill_submatrix (cm, model_cov, model_dropped);
+ fill_submatrix (cm, submodel_cov, submodel_dropped);
+
+ reg_sweep (model_cov, 0);
+ reg_sweep (submodel_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);
+ gsl_matrix_get (submodel_cov, 0, 0) - gsl_matrix_get (model_cov, 0, 0)
+ );
+
+ gsl_matrix_free (model_cov);
+ gsl_matrix_free (submodel_cov);
}
- free (dropped);
- gsl_matrix_free (cm);
+ free (model_dropped);
+ free (submodel_dropped);
}
+/*
+ Type 3 sums of squares.
+ Populate SSQ with the Type 2 sums of squares according to COV
+ */
+static void
+ssq_type3 (struct covariance *cov, gsl_vector *ssq, const struct glm_spec *cmd)
+{
+ const gsl_matrix *cm = covariance_calculate_unnormalized (cov);
+ size_t i;
+ size_t k;
+ bool *model_dropped = xcalloc (covariance_dim (cov), sizeof (*model_dropped));
+ bool *submodel_dropped = xcalloc (covariance_dim (cov), sizeof (*submodel_dropped));
+ const struct categoricals *cats = covariance_get_categoricals (cov);
+
+ double ss0;
+ gsl_matrix *submodel_cov = gsl_matrix_alloc (cm->size1, cm->size2);
+ fill_submatrix (cm, submodel_cov, submodel_dropped);
+ reg_sweep (submodel_cov, 0);
+ ss0 = gsl_matrix_get (submodel_cov, 0, 0);
+ gsl_matrix_free (submodel_cov);
+ free (submodel_dropped);
+
+ for (k = 0; k < cmd->n_interactions; k++)
+ {
+ gsl_matrix *model_cov = NULL;
+ size_t n_dropped_model = 0;
+
+ for (i = cmd->n_dep_vars; i < covariance_dim (cov); i++)
+ {
+ const struct interaction * x =
+ categoricals_get_interaction_by_subscript (cats, i - cmd->n_dep_vars);
+
+ model_dropped[i] = false;
+
+ if ( cmd->interactions [k] == x)
+ {
+ assert (n_dropped_model < covariance_dim (cov));
+ n_dropped_model++;
+ model_dropped[i] = true;
+ }
+ }
+
+ model_cov = gsl_matrix_alloc (cm->size1 - n_dropped_model, cm->size2 - n_dropped_model);
+
+ fill_submatrix (cm, model_cov, model_dropped);
+
+ reg_sweep (model_cov, 0);
+
+ gsl_vector_set (ssq, k + 1,
+ gsl_matrix_get (model_cov, 0, 0) - ss0);
+
+ gsl_matrix_free (model_cov);
+ }
+ free (model_dropped);
+}
+
+
+
//static void dump_matrix (const gsl_matrix *m);
static void
struct glm_workspace ws;
struct covariance *cov;
+ input = casereader_create_filter_missing (input,
+ cmd->dep_vars, cmd->n_dep_vars,
+ cmd->exclude,
+ NULL, NULL);
+
+ input = casereader_create_filter_missing (input,
+ cmd->factor_vars, cmd->n_factor_vars,
+ cmd->exclude,
+ NULL, NULL);
+
ws.cats = categoricals_create (cmd->interactions, cmd->n_interactions,
- cmd->wv, cmd->exclude,
- NULL, NULL, NULL, NULL);
+ cmd->wv, MV_ANY);
cov = covariance_2pass_create (cmd->n_dep_vars, cmd->dep_vars,
- ws.cats, cmd->wv, cmd->exclude);
+ ws.cats, cmd->wv, cmd->exclude, true);
c = casereader_peek (input, 0);
}
casereader_destroy (reader);
- for (reader = input;
+ if (cmd->dump_coding)
+ reader = casereader_clone (input);
+ else
+ reader = input;
+
+ for (;
(c = casereader_read (reader)) != NULL; case_unref (c))
{
double weight = dict_get_case_weight (dict, c, &warn_bad_weight);
}
casereader_destroy (reader);
+
+ if (cmd->dump_coding)
+ {
+ struct pivot_table *t = covariance_dump_enc_header (cov);
+ for (reader = input;
+ (c = casereader_read (reader)) != NULL; case_unref (c))
+ {
+ covariance_dump_enc (cov, c, t);
+ }
+
+ pivot_table_submit (t);
+ }
+
{
- gsl_matrix *cm = covariance_calculate_unnormalized (cov);
+ const gsl_matrix *ucm = covariance_calculate_unnormalized (cov);
+ gsl_matrix *cm = gsl_matrix_alloc (ucm->size1, ucm->size2);
+ gsl_matrix_memcpy (cm, ucm);
// dump_matrix (cm);
*/
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);
+ switch (cmd->ss_type)
+ {
+ case 1:
+ ssq_type1 (cov, ws.ssq, cmd);
+ break;
+ case 2:
+ ssq_type2 (cov, ws.ssq, cmd);
+ break;
+ case 3:
+ ssq_type3 (cov, ws.ssq, cmd);
+ break;
+ default:
+ NOT_REACHED ();
+ break;
+ }
// dump_matrix (cm);
-
gsl_matrix_free (cm);
}
}
static void
-output_glm (const struct glm_spec *cmd, const struct glm_workspace *ws)
+put_glm_row (struct pivot_table *table, int row,
+ double a, double b, double c, double d, double e)
{
- 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;
-
- const int nc = 6;
- int nr = heading_rows + 4 + cmd->n_interactions;
- if (cmd->intercept)
- nr++;
-
- t = tab_create (nc, nr);
- tab_title (t, _("Tests of Between-Subjects Effects"));
-
- tab_headers (t, heading_columns, 0, heading_rows, 0);
+ double entries[] = { a, b, c, d, e };
- 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"));
+ for (size_t col = 0; col < sizeof entries / sizeof *entries; col++)
+ if (entries[col] != SYSMIS)
+ pivot_table_put2 (table, col, row,
+ pivot_value_new_number (entries[col]));
+}
- /* 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 (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."));
+static void
+output_glm (const struct glm_spec *cmd, const struct glm_workspace *ws)
+{
+ struct pivot_table *table = pivot_table_create (
+ N_("Tests of Between-Subjects Effects"));
+
+ pivot_dimension_create (table, PIVOT_AXIS_COLUMN, N_("Statistics"),
+ (cmd->ss_type == 1 ? N_("Type I Sum Of Squares")
+ : cmd->ss_type == 2 ? N_("Type II Sum Of Squares")
+ : N_("Type III Sum Of Squares")), PIVOT_RC_OTHER,
+ N_("df"), PIVOT_RC_COUNT,
+ N_("Mean Square"), PIVOT_RC_OTHER,
+ N_("F"), PIVOT_RC_OTHER,
+ N_("Sig."), PIVOT_RC_SIGNIFICANCE);
+
+ struct pivot_dimension *source = pivot_dimension_create (
+ table, PIVOT_AXIS_ROW, N_("Source"),
+ cmd->intercept ? N_("Corrected Model") : N_("Model"));
+ double n_total, mean;
moments_calculate (ws->totals, &n_total, &mean, NULL, NULL, NULL);
- if (cmd->intercept)
- df_corr += 1.0;
-
- df_corr += categoricals_df_total (ws->cats);
-
- 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"));
-
- r++;
+ double df_corr = 1.0 + categoricals_df_total (ws->cats);
+ double mse = gsl_vector_get (ws->ssq, 0) / (n_total - df_corr);
+ double intercept_ssq = pow2 (mean * n_total) / n_total;
if (cmd->intercept)
{
- 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 / 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++;
+ int row = pivot_category_create_leaf (
+ source->root, pivot_value_new_text (N_("Intercept")));
+
+ /* The intercept for unbalanced models is of limited use and
+ nobody knows how to calculate it properly */
+ if (categoricals_isbalanced (ws->cats))
+ {
+ const double df = 1.0;
+ const double F = intercept_ssq / df / mse;
+ put_glm_row (table, row, intercept_ssq, 1.0, intercept_ssq / df,
+ F, gsl_cdf_fdist_Q (F, df, n_total - df_corr));
+ }
}
- for (f = 0; f < cmd->n_interactions; ++f)
+ double ssq_effects = 0.0;
+ for (int f = 0; f < cmd->n_interactions; ++f)
{
+ double df = categoricals_df (ws->cats, f);
+ double ssq = gsl_vector_get (ws->ssq, f + 1);
+ ssq_effects += ssq;
+ if (!cmd->intercept)
+ {
+ df++;
+ ssq += intercept_ssq;
+ }
+ double F = ssq / df / mse;
+
struct string str = DS_EMPTY_INITIALIZER;
- const double df = categoricals_df (ws->cats, f);
- const double ssq = gsl_vector_get (ws->ssq, f + 1);
- const double F = ssq / df / mse;
interaction_to_string (cmd->interactions[f], &str);
- tab_text (t, 0, r, TAB_LEFT | TAT_TITLE, ds_cstr (&str));
- ds_destroy (&str);
-
- 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);
+ int row = pivot_category_create_leaf (
+ source->root, pivot_value_new_user_text_nocopy (ds_steal_cstr (&str)));
- tab_double (t, 5, r, 0, gsl_cdf_fdist_Q (F, df, n_total - df_corr),
- NULL);
- r++;
+ put_glm_row (table, row, ssq, df, ssq / df, F,
+ gsl_cdf_fdist_Q (F, df, n_total - df_corr));
}
{
- /* 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);
+ /* Model / Corrected Model */
+ double df = df_corr;
+ double ssq = ws->total_ssq - gsl_vector_get (ws->ssq, 0);
+ if (cmd->intercept)
+ df--;
+ else
+ ssq += intercept_ssq;
+ double F = ssq / df / mse;
+ put_glm_row (table, 0, ssq, df, ssq / df, F,
+ gsl_cdf_fdist_Q (F, df, n_total - df_corr));
}
{
+ int row = pivot_category_create_leaf (source->root,
+ pivot_value_new_text (N_("Error")));
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);
+ put_glm_row (table, row, ssq, df, mse, SYSMIS, SYSMIS);
+ }
+
+ {
+ int row = pivot_category_create_leaf (source->root,
+ pivot_value_new_text (N_("Total")));
+ put_glm_row (table, row, ws->total_ssq + intercept_ssq, n_total,
+ SYSMIS, SYSMIS, SYSMIS);
}
if (cmd->intercept)
{
- 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);
-
- r++;
+ int row = pivot_category_create_leaf (
+ source->root, pivot_value_new_text (N_("Corrected Total")));
+ put_glm_row (table, row, ws->total_ssq, n_total - 1.0, SYSMIS,
+ SYSMIS, SYSMIS);
}
- 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);
+ pivot_table_submit (table);
}
#if 0
\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, struct interaction **iact)
-{
- const struct variable *v = NULL;
- assert (iact);
-
- switch (lex_next_token (lexer, 1))
- {
- case T_ENDCMD:
- case T_SLASH:
- case T_COMMA:
- case T_ID:
- case T_BY:
- case T_ASTERISK:
- break;
- default:
- return false;
- break;
- }
-
- if (! lex_match_variable (lexer, glm, &v))
- {
- interaction_destroy (*iact);
- *iact = NULL;
- return false;
- }
-
- assert (v);
-
- if ( *iact == NULL)
- *iact = interaction_create (v);
- else
- interaction_add_variable (*iact, v);
-
- 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, iact);
- }
-
- return true;
-}
-
static bool
parse_nested_variable (struct lexer *lexer, struct glm_spec *glm)
{
const struct variable *v = NULL;
- if ( ! lex_match_variable (lexer, glm, &v))
+ if ( ! lex_match_variable (lexer, glm->dict, &v))
return false;
if (lex_match (lexer, T_LPAREN))
return false;
}
- lex_error (lexer, "Nested variables are not yet implemented"); return false;
+ lex_error (lexer, "Nested variables are not yet implemented"); return false;
return true;
}
parse_design_term (struct lexer *lexer, struct glm_spec *glm)
{
struct interaction *iact = NULL;
- if (parse_design_interaction (lexer, glm, &iact))
+ if (parse_design_interaction (lexer, glm->dict, &iact))
{
/* Interaction parsing successful. Add to list of interactions */
glm->interactions = xrealloc (glm->interactions, sizeof *glm->interactions * ++glm->n_interactions);
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