X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=src%2Flanguage%2Fstats%2Fglm.c;h=d01d41805758705adae7cf6f94f5ffcf20cc5c17;hb=bae3da89755d9503a674712303117dd76311b267;hp=862f49cea24300a7f4f158e72933cf8e613b0165;hpb=3c5fcaa67efcee56981c16b543fb9f679787a486;p=pspp-builds.git diff --git a/src/language/stats/glm.c b/src/language/stats/glm.c index 862f49ce..d01d4180 100644 --- a/src/language/stats/glm.c +++ b/src/language/stats/glm.c @@ -1,5 +1,5 @@ /* 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 @@ -16,38 +16,34 @@ #include -#include -#include -#include - -#include -#include -#include -#include -#include - -#include - -#include -#include -#include -#include - -#include -#include -#include - -#include -#include -#include - #include +#include +#include #include -#include - -#include -#include +#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/assertion.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/interaction.h" +#include "math/moments.h" +#include "output/tab.h" #include "gettext.h" #define _(msgid) gettext (msgid) @@ -60,67 +56,131 @@ struct glm_spec size_t n_factor_vars; const struct variable **factor_vars; + size_t n_interactions; + struct interaction **interactions; + enum mv_class exclude; /* The weight variable */ const struct variable *wv; + const struct dictionary *dict; + + int ss_type; bool intercept; + + double alpha; + + bool dump_coding; }; 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); + +/* 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, + 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 ; + int i; + 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_interactions = 0; + glm.interactions = NULL; glm.dep_vars = NULL; glm.factor_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; + glm.dump_coding = false; + glm.ss_type = 3; - - 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; @@ -131,16 +191,17 @@ cmd_glm (struct lexer *lexer, struct dataset *ds) } 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; @@ -151,18 +212,91 @@ cmd_glm (struct lexer *lexer, struct dataset *ds) } else { - lex_error (lexer, NULL); + lex_error (lexer, NULL); goto error; } } } + 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; + } + + glm.ss_type = lex_integer (lexer); + if (1 > glm.ss_type && 3 < glm.ss_type ) + { + msg (ME, _("Only types 1, 2 & 3 sums 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")) - { - size_t n_des; - const struct variable **des; - lex_match (lexer, T_EQUALS); + { + lex_match (lexer, T_EQUALS); + + if (! parse_design_spec (lexer, &glm)) + goto error; - parse_const_var_set_vars (lexer, factors, &des, &n_des, 0); + 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 { @@ -171,30 +305,281 @@ cmd_glm (struct lexer *lexer, struct dataset *ds) } } + if ( ! design ) + { + design_full (&glm); + } { 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); + for (i = 0 ; i < glm.n_interactions; ++i) + interaction_destroy (glm.interactions[i]); + free (glm.interactions); + free (glm.dep_vars); + + return CMD_SUCCESS; - error: +error: + + const_var_set_destroy (factors); + 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 dump_matrix (const gsl_matrix *m); +static inline bool +not_dropped (size_t j, const bool *ff) +{ + return ! ff[j]; +} + +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 < cov->size1; i++) + { + 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++; + } + } +} + + +/* + Type 1 sums of squares. + Populate SSQ with the Type 1 sums of squares according to COV + */ +static void +ssq_type1 (struct covariance *cov, gsl_vector *ssq, const struct glm_spec *cmd) +{ + 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); + + 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++) + { + 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++) + { + const struct interaction * x = + categoricals_get_interaction_by_subscript (cats, i - cmd->n_dep_vars); + + if ( x == cmd->interactions [k]) + { + model_dropped[i] = false; + n_dropped_model--; + } + } + + 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); + gsl_matrix_free (cm); +} + +/* + Type 2 sums of squares. + Populate SSQ with the Type 2 sums of squares according to COV + */ static void -run_glm (const struct glm_spec *cmd, struct casereader *input, const struct dataset *ds) +ssq_type2 (struct covariance *cov, gsl_vector *ssq, const struct glm_spec *cmd) { + 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++) + { + 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)) + { + assert (n_dropped_submodel < covariance_dim (cov)); + n_dropped_submodel++; + submodel_dropped[i] = true; + + if ( cmd->interactions [k]->n_vars < x->n_vars) + { + 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); + 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); + gsl_matrix_free (cm); +} + +/* + 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) +{ + 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); + + 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); @@ -202,15 +587,14 @@ run_glm (const struct glm_spec *cmd, struct casereader *input, const struct data struct ccase *c; struct glm_workspace ws; + struct covariance *cov; - 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); + ws.cats = categoricals_create (cmd->interactions, cmd->n_interactions, + cmd->wv, cmd->exclude, + NULL, NULL, NULL, NULL); + + cov = covariance_2pass_create (cmd->n_dep_vars, cmd->dep_vars, + ws.cats, cmd->wv, cmd->exclude); c = casereader_peek (input, 0); @@ -226,43 +610,82 @@ run_glm (const struct glm_spec *cmd, struct casereader *input, const struct data 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); + if (cmd->dump_coding) + reader = casereader_clone (input); + else + reader = input; - for (reader = casereader_clone (input); + for (; (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); } casereader_destroy (reader); + + if (cmd->dump_coding) + { + struct tab_table *t = + covariance_dump_enc_header (cov, + 1 + casereader_count_cases (input)); + for (reader = input; + (c = casereader_read (reader)) != NULL; case_unref (c)) + { + covariance_dump_enc (cov, c, t); + } + casereader_destroy (reader); + tab_submit (t); + } + { 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)); + 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); } @@ -270,35 +693,51 @@ run_glm (const struct glm_spec *cmd, struct casereader *input, const struct data 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 const char *roman[] = + { + "", /* The Romans had no concept of zero */ + "I", + "II", + "III", + "IV" + }; + 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_interactions; if (cmd->intercept) nr++; + msg (MW, "GLM is experimental. Do not rely on these results."); t = tab_create (nc, nr); tab_title (t, _("Tests of Between-Subjects Effects")); 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); @@ -306,42 +745,92 @@ output_glm (const struct glm_spec *cmd, const struct glm_workspace *ws) 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"), + roman[cmd->ss_type]); 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; + + 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")); + 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_interactions; ++f) { - tab_text (t, 0, r++, TAB_LEFT | TAT_TITLE, - var_to_string (cmd->factor_vars[f])); + 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); + + 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); @@ -351,14 +840,16 @@ output_glm (const struct glm_spec *cmd, const struct glm_workspace *ws) 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) @@ -372,3 +863,126 @@ void dump_matrix (const gsl_matrix *m) } printf ("\n"); } +#endif + + + + +/* 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)) + { + 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)) + return false; + + if (lex_match (lexer, T_LPAREN)) + { + if ( ! parse_nested_variable (lexer, glm)) + return false; + + if ( ! lex_force_match (lexer, T_RPAREN)) + return false; + } + + lex_error (lexer, "Nested variables are not yet implemented"); return false; + return true; +} + +/* A design term is an interaction OR a nested variable */ +static bool +parse_design_term (struct lexer *lexer, struct glm_spec *glm) +{ + struct interaction *iact = NULL; + if (parse_design_interaction (lexer, glm, &iact)) + { + /* Interaction parsing successful. Add to list of interactions */ + glm->interactions = xrealloc (glm->interactions, sizeof *glm->interactions * ++glm->n_interactions); + glm->interactions[glm->n_interactions - 1] = iact; + return true; + } + + if ( parse_nested_variable (lexer, glm)) + 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) +{ + 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); +} +