X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=src%2Flanguage%2Fstats%2Fglm.c;h=74e918b886b21f26d05b1fadf2d245faa60096bb;hb=edd5c738dfef01c90d02e06a33b93fc9d38320b8;hp=f8a7f133cc254d19493c8856f16a656bee4ad935;hpb=32ee0e0402d6d56674f53a47d879ec5c07dabe09;p=pspp diff --git a/src/language/stats/glm.c b/src/language/stats/glm.c index f8a7f133cc..74e918b886 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, 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 @@ -33,6 +33,7 @@ #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" @@ -65,9 +66,12 @@ struct glm_spec const struct dictionary *dict; + int ss_type; bool intercept; double alpha; + + bool dump_coding; }; struct glm_workspace @@ -77,7 +81,7 @@ 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. @@ -124,8 +128,6 @@ static void run_glm (struct glm_spec *cmd, struct casereader *input, static bool parse_design_spec (struct lexer *lexer, struct glm_spec *glm); -/* Define to 1 if the /DESIGN subcommand should not be optional */ -#define DESIGN_MANDATORY 1 int cmd_glm (struct lexer *lexer, struct dataset *ds) @@ -145,13 +147,16 @@ cmd_glm (struct lexer *lexer, struct dataset *ds) 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, @@ -225,7 +230,7 @@ cmd_glm (struct lexer *lexer, struct dataset *ds) lex_error (lexer, NULL); goto error; } - + glm.alpha = lex_number (lexer); lex_get (lexer); if ( ! lex_force_match (lexer, T_RPAREN)) @@ -262,9 +267,10 @@ cmd_glm (struct lexer *lexer, struct dataset *ds) 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; } @@ -283,18 +289,15 @@ cmd_glm (struct lexer *lexer, struct dataset *ds) if (! parse_design_spec (lexer, &glm)) goto error; -#if DESIGN_MANDATORY - if ( glm.n_interactions == 0) - { - msg (ME, _("One or more design variables must be given")); - goto error; - } - - design = true; -#else if (glm.n_interactions > 0) design = true; -#endif + } + else if (lex_match_id (lexer, "SHOWCODES")) + /* Undocumented debug option */ + { + lex_match (lexer, T_EQUALS); + + glm.dump_coding = true; } else { @@ -305,11 +308,6 @@ cmd_glm (struct lexer *lexer, struct dataset *ds) if ( ! design ) { -#if DESIGN_MANDATORY - lex_error (lexer, _("/DESIGN is mandatory in GLM")); - goto error; -#endif - design_full (&glm); } @@ -329,6 +327,7 @@ cmd_glm (struct lexer *lexer, struct dataset *ds) free (glm.factor_vars); for (i = 0 ; i < glm.n_interactions; ++i) interaction_destroy (glm.interactions[i]); + free (glm.interactions); free (glm.dep_vars); @@ -348,78 +347,230 @@ error: 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, const 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 @@ -436,12 +587,21 @@ run_glm (struct glm_spec *cmd, struct casereader *input, 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, cmd->exclude, 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); @@ -470,7 +630,12 @@ run_glm (struct glm_spec *cmd, struct casereader *input, } 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); @@ -483,8 +648,25 @@ run_glm (struct glm_spec *cmd, struct casereader *input, } 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); + 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); @@ -497,9 +679,22 @@ run_glm (struct glm_spec *cmd, struct casereader *input, */ 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); } @@ -514,14 +709,25 @@ run_glm (struct glm_spec *cmd, struct casereader *input, 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; + double intercept_ssq; + double ssq_effects; double n_total, mean; - double df_corr = 0.0; + double df_corr = 1.0; double mse = 0; int f; @@ -531,11 +737,12 @@ output_glm (const struct glm_spec *cmd, const struct glm_workspace *ws) struct tab_table *t; const int nc = 6; - int nr = heading_rows + 4 + cmd->n_interactions; + int nr = heading_rows + 3 + cmd->n_interactions; if (cmd->intercept) - nr++; + nr += 2; t = tab_create (nc, nr); + tab_set_format (t, RC_WEIGHT, wfmt); tab_title (t, _("Tests of Between-Subjects Effects")); tab_headers (t, heading_columns, 0, heading_rows, 0); @@ -549,7 +756,8 @@ output_glm (const struct glm_spec *cmd, const struct glm_workspace *ws) /* TRANSLATORS: The parameter is a roman numeral */ tab_text_format (t, 1, 0, TAB_CENTER | TAT_TITLE, - _("Type %s Sum of Squares"), "III"); + _("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")); @@ -557,65 +765,90 @@ output_glm (const struct glm_spec *cmd, const struct glm_workspace *ws) 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")); + if (cmd->intercept) + tab_text (t, 0, r, TAB_LEFT | TAT_TITLE, _("Corrected Model")); + else + tab_text (t, 0, r, TAB_LEFT | TAT_TITLE, _("Model")); r++; + mse = gsl_vector_get (ws->ssq, 0) / (n_total - df_corr); + + intercept_ssq = pow2 (mean * n_total) / n_total; + + ssq_effects = 0.0; if (cmd->intercept) { - const double intercept = pow2 (mean * n_total) / n_total; const double df = 1.0; - const double F = intercept / df / mse; + const double F = intercept_ssq / 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); + /* The intercept for unbalanced models is of limited use and + nobody knows how to calculate it properly */ + if (categoricals_isbalanced (ws->cats)) + { + tab_double (t, 1, r, 0, intercept_ssq, NULL, RC_OTHER); + tab_double (t, 2, r, 0, 1.00, NULL, RC_WEIGHT); + tab_double (t, 3, r, 0, intercept_ssq / df, NULL, RC_OTHER); + tab_double (t, 4, r, 0, F, NULL, RC_OTHER); + tab_double (t, 5, r, 0, gsl_cdf_fdist_Q (F, df, n_total - df_corr), + NULL, RC_PVALUE); + } r++; } for (f = 0; f < cmd->n_interactions; ++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; + double df = categoricals_df (ws->cats, f); + + double ssq = gsl_vector_get (ws->ssq, f + 1); + double F; + + ssq_effects += ssq; + + if (! cmd->intercept) + { + df++; + ssq += intercept_ssq; + } + + 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, 1, r, 0, ssq, NULL, RC_OTHER); + tab_double (t, 2, r, 0, df, NULL, RC_WEIGHT); + tab_double (t, 3, r, 0, ssq / df, NULL, RC_OTHER); + tab_double (t, 4, r, 0, F, NULL, RC_OTHER); tab_double (t, 5, r, 0, gsl_cdf_fdist_Q (F, df, n_total - df_corr), - NULL); + NULL, RC_PVALUE); r++; } { - /* 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); + /* Model / Corrected Model */ + double df = df_corr; + double ssq = ws->total_ssq - gsl_vector_get (ws->ssq, 0); + double F; + + if ( cmd->intercept ) + df --; + else + ssq += intercept_ssq; + + F = ssq / df / mse; + tab_double (t, 1, heading_rows, 0, ssq, NULL, RC_OTHER); + tab_double (t, 2, heading_rows, 0, df, NULL, RC_WEIGHT); + tab_double (t, 3, heading_rows, 0, ssq / df, NULL, RC_OTHER); + tab_double (t, 4, heading_rows, 0, F, NULL, RC_OTHER); tab_double (t, 5, heading_rows, 0, - gsl_cdf_fdist_Q (F, df, n_total - df_corr), NULL); + gsl_cdf_fdist_Q (F, df, n_total - df_corr), NULL, RC_PVALUE); } { @@ -623,29 +856,26 @@ output_glm (const struct glm_spec *cmd, const struct glm_workspace *ws) 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); + tab_double (t, 1, r, 0, ssq, NULL, RC_OTHER); + tab_double (t, 2, r, 0, df, NULL, RC_WEIGHT); + tab_double (t, 3, r++, 0, mse, NULL, RC_OTHER); } - 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, ws->total_ssq + intercept_ssq, NULL, RC_OTHER); + tab_double (t, 2, r, 0, n_total, NULL, RC_WEIGHT); - 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++; + } - r++; + if (cmd->intercept) + { + tab_text (t, 0, r, TAB_LEFT | TAT_TITLE, _("Corrected Total")); + tab_double (t, 1, r, 0, ws->total_ssq, NULL, RC_OTHER); + tab_double (t, 2, r, 0, n_total - 1.0, NULL, RC_WEIGHT); } - 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); } @@ -669,72 +899,11 @@ dump_matrix (const gsl_matrix * m) - -/* 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)) @@ -746,7 +915,7 @@ parse_nested_variable (struct lexer *lexer, struct glm_spec *glm) 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; } @@ -755,7 +924,7 @@ static bool 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);