X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=src%2Flanguage%2Fstats%2Ffactor.c;h=316b64a71e0b824d7b63710943b0b5a0fe8eb93d;hb=a58399ea2ce9421f72cc5771cd215b121bd8f9dd;hp=38c66cc751d7b920e698b540224cf6ee64241b46;hpb=55c55aa33d0f90d1b3b58f8b33b3fc54062c553e;p=pspp diff --git a/src/language/stats/factor.c b/src/language/stats/factor.c index 38c66cc751..316b64a71e 100644 --- a/src/language/stats/factor.c +++ b/src/language/stats/factor.c @@ -1,5 +1,6 @@ /* PSPP - a program for statistical analysis. - Copyright (C) 2009, 2010, 2011, 2012, 2014, 2015, 2016 Free Software Foundation, Inc. + Copyright (C) 2009, 2010, 2011, 2012, 2014, 2015, + 2016, 2017 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 @@ -19,11 +20,12 @@ #include #include #include -#include -#include +#include +#include #include #include +#include "data/any-reader.h" #include "data/casegrouper.h" #include "data/casereader.h" #include "data/casewriter.h" @@ -35,15 +37,17 @@ #include "language/lexer/lexer.h" #include "language/lexer/value-parser.h" #include "language/lexer/variable-parser.h" +#include "language/data-io/file-handle.h" +#include "language/data-io/matrix-reader.h" #include "libpspp/cast.h" #include "libpspp/message.h" #include "libpspp/misc.h" #include "math/correlation.h" #include "math/covariance.h" #include "math/moments.h" -#include "output/chart-item.h" #include "output/charts/scree.h" -#include "output/tab.h" +#include "output/pivot-table.h" + #include "gettext.h" #define _(msgid) gettext (msgid) @@ -87,7 +91,7 @@ enum print_opts PRINT_EXTRACTION = 0x0100, PRINT_INITIAL = 0x0200, PRINT_KMO = 0x0400, - PRINT_REPR = 0x0800, + PRINT_REPR = 0x0800, PRINT_FSCORE = 0x1000 }; @@ -102,13 +106,13 @@ enum rotation_type typedef void (*rotation_coefficients) (double *x, double *y, double a, double b, double c, double d, - const gsl_matrix *loadings ); + const gsl_matrix *loadings); static void varimax_coefficients (double *x, double *y, double a, double b, double c, double d, - const gsl_matrix *loadings ) + const gsl_matrix *loadings) { *x = d - 2 * a * b / loadings->size1; *y = c - (a * a - b * b) / loadings->size1; @@ -117,7 +121,7 @@ varimax_coefficients (double *x, double *y, static void equamax_coefficients (double *x, double *y, double a, double b, double c, double d, - const gsl_matrix *loadings ) + const gsl_matrix *loadings) { *x = d - loadings->size2 * a * b / loadings->size1; *y = c - loadings->size2 * (a * a - b * b) / (2 * loadings->size1); @@ -142,7 +146,7 @@ static const rotation_coefficients rotation_coeff[] = { /* return diag (C'C) ^ {-0.5} */ static gsl_matrix * -diag_rcp_sqrt (const gsl_matrix *C) +diag_rcp_sqrt (const gsl_matrix *C) { int j; gsl_matrix *d = gsl_matrix_calloc (C->size1, C->size2); @@ -170,7 +174,7 @@ diag_rcp_sqrt (const gsl_matrix *C) /* return diag ((C'C)^-1) ^ {-0.5} */ static gsl_matrix * -diag_rcp_inv_sqrt (const gsl_matrix *CCinv) +diag_rcp_inv_sqrt (const gsl_matrix *CCinv) { int j; gsl_matrix *r = gsl_matrix_calloc (CCinv->size1, CCinv->size2); @@ -191,7 +195,7 @@ diag_rcp_inv_sqrt (const gsl_matrix *CCinv) -struct cmd_factor +struct cmd_factor { size_t n_vars; const struct variable **vars; @@ -221,13 +225,13 @@ struct cmd_factor bool sort; }; + struct idata { /* Intermediate values used in calculation */ + struct matrix_material mm; - const gsl_matrix *corr ; /* The correlation matrix */ - gsl_matrix *cov ; /* The covariance matrix */ - const gsl_matrix *n ; /* Matrix of number of samples */ + gsl_matrix *analysis_matrix; /* A pointer to either mm.corr or mm.cov */ gsl_vector *eval ; /* The eigenvalues */ gsl_matrix *evec ; /* The eigenvectors */ @@ -237,12 +241,16 @@ struct idata gsl_vector *msr ; /* Multiple Squared Regressions */ double detR; /* The determinant of the correlation matrix */ + + gsl_matrix *ai_cov; /* The anti-image covariance matrix */ + gsl_matrix *ai_cor; /* The anti-image correlation matrix */ + struct covariance *cvm; }; static struct idata * idata_alloc (size_t n_vars) { - struct idata *id = xzalloc (sizeof (*id)); + struct idata *id = XZALLOC (struct idata); id->n_extractions = 0; id->msr = gsl_vector_alloc (n_vars); @@ -259,40 +267,32 @@ idata_free (struct idata *id) gsl_vector_free (id->msr); gsl_vector_free (id->eval); gsl_matrix_free (id->evec); - if (id->cov != NULL) - gsl_matrix_free (id->cov); - if (id->corr != NULL) - gsl_matrix_free (CONST_CAST (gsl_matrix *, id->corr)); + gsl_matrix_free (id->ai_cov); + gsl_matrix_free (id->ai_cor); free (id); } - -static gsl_matrix * -anti_image (const gsl_matrix *m) +/* Return the sum of squares of all the elements in row J excluding column J */ +static double +ssq_row_od_n (const gsl_matrix *m, int j) { - int i, j; - gsl_matrix *a; + int i; + double ss = 0; assert (m->size1 == m->size2); - a = gsl_matrix_alloc (m->size1, m->size2); - + assert (j < m->size1); + for (i = 0; i < m->size1; ++i) { - for (j = 0; j < m->size2; ++j) - { - double *p = gsl_matrix_ptr (a, i, j); - *p = gsl_matrix_get (m, i, j); - *p /= gsl_matrix_get (m, i, i); - *p /= gsl_matrix_get (m, j, j); - } + if (i == j) continue; + ss += pow2 (gsl_matrix_get (m, i, j)); } - return a; + return ss; } - -/* Return the sum of all the elements excluding row N */ +/* Return the sum of squares of all the elements excluding row N */ static double ssq_od_n (const gsl_matrix *m, int n) { @@ -301,12 +301,12 @@ ssq_od_n (const gsl_matrix *m, int n) assert (m->size1 == m->size2); assert (n < m->size1); - + for (i = 0; i < m->size1; ++i) { - if (i == n ) continue; for (j = 0; j < m->size2; ++j) { + if (i == j) continue; ss += pow2 (gsl_matrix_get (m, i, j)); } } @@ -315,6 +315,58 @@ ssq_od_n (const gsl_matrix *m, int n) } +static gsl_matrix * +anti_image_corr (const gsl_matrix *m, const struct idata *idata) +{ + int i, j; + gsl_matrix *a; + assert (m->size1 == m->size2); + + a = gsl_matrix_alloc (m->size1, m->size2); + + for (i = 0; i < m->size1; ++i) + { + for (j = 0; j < m->size2; ++j) + { + double *p = gsl_matrix_ptr (a, i, j); + *p = gsl_matrix_get (m, i, j); + *p /= sqrt (gsl_matrix_get (m, i, i) * + gsl_matrix_get (m, j, j)); + } + } + + for (i = 0; i < m->size1; ++i) + { + double r = ssq_row_od_n (idata->mm.corr, i); + double u = ssq_row_od_n (a, i); + gsl_matrix_set (a, i, i, r / (r + u)); + } + + return a; +} + +static gsl_matrix * +anti_image_cov (const gsl_matrix *m) +{ + int i, j; + gsl_matrix *a; + assert (m->size1 == m->size2); + + a = gsl_matrix_alloc (m->size1, m->size2); + + for (i = 0; i < m->size1; ++i) + { + for (j = 0; j < m->size2; ++j) + { + double *p = gsl_matrix_ptr (a, i, j); + *p = gsl_matrix_get (m, i, j); + *p /= gsl_matrix_get (m, i, i); + *p /= gsl_matrix_get (m, j, j); + } + } + + return a; +} #if 0 static void @@ -357,13 +409,13 @@ dump_vector (const gsl_vector *v) #endif -static int +static int n_extracted_factors (const struct cmd_factor *factor, struct idata *idata) { int i; - + /* If there is a cached value, then return that. */ - if ( idata->n_extractions != 0) + if (idata->n_extractions != 0) return idata->n_extractions; /* Otherwise, if the number of factors has been explicitly requested, @@ -373,7 +425,7 @@ n_extracted_factors (const struct cmd_factor *factor, struct idata *idata) idata->n_extractions = factor->n_factors; goto finish; } - + /* Use the MIN_EIGEN setting. */ for (i = 0 ; i < idata->eval->size; ++i) { @@ -391,7 +443,7 @@ n_extracted_factors (const struct cmd_factor *factor, struct idata *idata) /* Returns a newly allocated matrix identical to M. - It it the callers responsibility to free the returned value. + It is the callers responsibility to free the returned value. */ static gsl_matrix * matrix_dup (const gsl_matrix *m) @@ -408,7 +460,7 @@ struct smr_workspace { /* Copy of the subject */ gsl_matrix *m; - + gsl_matrix *inverse; gsl_permutation *perm; @@ -421,7 +473,7 @@ struct smr_workspace static struct smr_workspace *ws_create (const gsl_matrix *input) { struct smr_workspace *ws = xmalloc (sizeof (*ws)); - + ws->m = gsl_matrix_alloc (input->size1, input->size2); ws->inverse = gsl_matrix_calloc (input->size1 - 1, input->size2 - 1); ws->perm = gsl_permutation_alloc (input->size1 - 1); @@ -444,13 +496,13 @@ ws_destroy (struct smr_workspace *ws) } -/* +/* Return the square of the regression coefficient for VAR regressed against all other variables. */ static double squared_multiple_correlation (const gsl_matrix *corr, int var, struct smr_workspace *ws) { - /* For an explanation of what this is doing, see + /* For an explanation of what this is doing, see http://www.visualstatistics.net/Visual%20Statistics%20Multimedia/multiple_regression_analysis.htm */ @@ -462,7 +514,7 @@ squared_multiple_correlation (const gsl_matrix *corr, int var, struct smr_worksp gsl_matrix_swap_rows (ws->m, 0, var); gsl_matrix_swap_columns (ws->m, 0, var); - rxx = gsl_matrix_submatrix (ws->m, 1, 1, ws->m->size1 - 1, ws->m->size1 - 1); + rxx = gsl_matrix_submatrix (ws->m, 1, 1, ws->m->size1 - 1, ws->m->size1 - 1); gsl_linalg_LU_decomp (&rxx.matrix, ws->perm, &signum); @@ -511,7 +563,7 @@ factor_matrix_workspace_alloc (size_t n, size_t nf) ws->eval = gsl_vector_alloc (n); ws->evec = gsl_matrix_alloc (n, n); ws->r = gsl_matrix_alloc (n, n); - + return ws; } @@ -546,10 +598,10 @@ perm_shift_apply (gsl_permutation *target, const gsl_permutation *p, } -/* +/* Indirectly sort the rows of matrix INPUT, storing the sort order in PERM. The sort criteria are as follows: - + Rows are sorted on the first column, until the absolute value of an element in a subsequent column is greater than that of the first column. Thereafter, rows will be sorted on the second column, @@ -582,7 +634,7 @@ sort_matrix_indirect (const gsl_matrix *input, gsl_permutation *perm) } } - while (column_n < m && row_n < n) + while (column_n < m && row_n < n) { gsl_vector_const_view columni = gsl_matrix_const_column (mat, column_n); gsl_sort_vector_index (p, &columni.vector); @@ -591,8 +643,8 @@ sort_matrix_indirect (const gsl_matrix *input, gsl_permutation *perm) { gsl_vector_view row = gsl_matrix_row (mat, p->data[n - 1 - i]); size_t maxindex = gsl_vector_max_index (&row.vector); - - if ( maxindex > column_n ) + + if (maxindex > column_n) break; /* All subsequent elements of this row, are of no interest. @@ -609,11 +661,11 @@ sort_matrix_indirect (const gsl_matrix *input, gsl_permutation *perm) gsl_permutation_free (p); gsl_matrix_free (mat); - - assert ( 0 == gsl_permutation_valid (perm)); + + assert (0 == gsl_permutation_valid (perm)); /* We want the biggest value to be first */ - gsl_permutation_reverse (perm); + gsl_permutation_reverse (perm); } @@ -647,7 +699,7 @@ clone_matrix (const gsl_matrix *m) } -static double +static double initial_sv (const gsl_matrix *fm) { int j, k; @@ -667,7 +719,7 @@ initial_sv (const gsl_matrix *fm) l4s += lambda_4; l2s += lambda_sq; } - sv += ( fm->size1 * l4s - (l2s * l2s) ) / (fm->size1 * fm->size1 ); + sv += (fm->size1 * l4s - (l2s * l2s)) / (fm->size1 * fm->size1); } return sv; } @@ -738,7 +790,7 @@ rotate (const struct cmd_factor *cf, const gsl_matrix *unrot, { double jv = gsl_matrix_get (normalised, p, j); double kv = gsl_matrix_get (normalised, p, k); - + double u = jv * jv - kv * kv; double v = 2 * jv * kv; a += u; @@ -752,7 +804,7 @@ rotate (const struct cmd_factor *cf, const gsl_matrix *unrot, phi = atan2 (x, y) / 4.0 ; /* Don't bother rotating if the angle is small */ - if ( fabs (sin (phi) ) <= pow (10.0, -15.0)) + if (fabs (sin (phi)) <= pow (10.0, -15.0)) continue; for (p = 0; p < normalised->size1; ++p) @@ -772,10 +824,10 @@ rotate (const struct cmd_factor *cf, const gsl_matrix *unrot, l2s += lambda_sq; } } - sv += ( normalised->size1 * l4s - (l2s * l2s) ) / (normalised->size1 * normalised->size1 ); + sv += (normalised->size1 * l4s - (l2s * l2s)) / (normalised->size1 * normalised->size1); } - if ( fabs (sv - prev_sv) <= cf->rconverge) + if (fabs (sv - prev_sv) <= cf->rconverge) break; prev_sv = sv; @@ -787,10 +839,10 @@ rotate (const struct cmd_factor *cf, const gsl_matrix *unrot, gsl_matrix_free (h_sqrt); gsl_matrix_free (normalised); - if (cf->rotation == ROT_PROMAX) + if (cf->rotation == ROT_PROMAX) { /* general purpose m by m matrix, where m is the number of factors */ - gsl_matrix *mm1 = gsl_matrix_calloc (unrot->size2, unrot->size2); + gsl_matrix *mm1 = gsl_matrix_calloc (unrot->size2, unrot->size2); gsl_matrix *mm2 = gsl_matrix_calloc (unrot->size2, unrot->size2); /* general purpose m by p matrix, where p is the number of variables */ @@ -814,7 +866,7 @@ rotate (const struct cmd_factor *cf, const gsl_matrix *unrot, /* Vector of length p containing (indexed by i) \Sum^m_j {\lambda^2_{ij}} */ - gsl_vector *rssq = gsl_vector_calloc (unrot->size1); + gsl_vector *rssq = gsl_vector_calloc (unrot->size1); for (i = 0; i < P->size1; ++i) { @@ -823,9 +875,9 @@ rotate (const struct cmd_factor *cf, const gsl_matrix *unrot, { sum += gsl_matrix_get (result, i, j) * gsl_matrix_get (result, i, j); - + } - + gsl_vector_set (rssq, i, sqrt (sum)); } @@ -898,7 +950,7 @@ rotate (const struct cmd_factor *cf, const gsl_matrix *unrot, gsl_linalg_matmult_mod (mm1, GSL_LINALG_MOD_NONE, C, GSL_LINALG_MOD_TRANSPOSE, factor_correlation_matrix); - + gsl_linalg_matmult_mod (pattern_matrix, GSL_LINALG_MOD_NONE, factor_correlation_matrix, GSL_LINALG_MOD_NONE, pm1); @@ -938,7 +990,7 @@ rotate (const struct cmd_factor *cf, const gsl_matrix *unrot, gsl_vector_set (rotated_loadings, i, ssq); - if ( sum < 0 ) + if (sum < 0) for (j = 0 ; j < result->size1; ++j) { double *lambda = gsl_matrix_ptr (result, j, i); @@ -954,7 +1006,7 @@ rotate (const struct cmd_factor *cf, const gsl_matrix *unrot, WS is a pointer to a structure which must have been initialised with factor_matrix_workspace_init. */ static void -iterate_factor_matrix (const gsl_matrix *r, gsl_vector *communalities, gsl_matrix *factors, +iterate_factor_matrix (const gsl_matrix *r, gsl_vector *communalities, gsl_matrix *factors, struct factor_matrix_workspace *ws) { size_t i; @@ -1002,11 +1054,14 @@ iterate_factor_matrix (const gsl_matrix *r, gsl_vector *communalities, gsl_matri static bool run_factor (struct dataset *ds, const struct cmd_factor *factor); +static void do_factor_by_matrix (const struct cmd_factor *factor, struct idata *idata); + + int cmd_factor (struct lexer *lexer, struct dataset *ds) { - const struct dictionary *dict = dataset_dict (ds); + struct dictionary *dict = NULL; int n_iterations = 25; struct cmd_factor factor; factor.n_vars = 0; @@ -1026,26 +1081,78 @@ cmd_factor (struct lexer *lexer, struct dataset *ds) factor.sort = false; factor.plot = 0; factor.rotation = ROT_VARIMAX; + factor.wv = NULL; factor.rconverge = 0.0001; - factor.wv = dict_get_weight (dict); - lex_match (lexer, T_SLASH); - if (!lex_force_match_id (lexer, "VARIABLES")) + struct matrix_reader *mr = NULL; + struct casereader *matrix_reader = NULL; + + if (lex_match_id (lexer, "VARIABLES")) { - goto error; + lex_match (lexer, T_EQUALS); + dict = dataset_dict (ds); + factor.wv = dict_get_weight (dict); + + if (!parse_variables_const (lexer, dict, &factor.vars, &factor.n_vars, + PV_NO_DUPLICATE | PV_NUMERIC)) + goto error; } + else if (lex_match_id (lexer, "MATRIX")) + { + lex_match (lexer, T_EQUALS); + if (! lex_force_match_id (lexer, "IN")) + goto error; + if (!lex_force_match (lexer, T_LPAREN)) + { + goto error; + } + if (lex_match_id (lexer, "CORR")) + { + } + else if (lex_match_id (lexer, "COV")) + { + } + else + { + lex_error (lexer, _("Matrix input for %s must be either COV or CORR"), "FACTOR"); + goto error; + } + if (! lex_force_match (lexer, T_EQUALS)) + goto error; + if (lex_match (lexer, T_ASTERISK)) + { + dict = dataset_dict (ds); + matrix_reader = casereader_clone (dataset_source (ds)); + } + else + { + struct file_handle *fh = fh_parse (lexer, FH_REF_FILE, NULL); + if (fh == NULL) + goto error; - lex_match (lexer, T_EQUALS); + matrix_reader + = any_reader_open_and_decode (fh, NULL, &dict, NULL); - if (!parse_variables_const (lexer, dict, &factor.vars, &factor.n_vars, - PV_NO_DUPLICATE | PV_NUMERIC)) - goto error; + if (! (matrix_reader && dict)) + { + goto error; + } + } - if (factor.n_vars < 2) - msg (MW, _("Factor analysis on a single variable is not useful.")); + if (! lex_force_match (lexer, T_RPAREN)) + goto error; + + mr = matrix_reader_create (dict, matrix_reader); + factor.vars = xmemdup (mr->cvars, mr->n_cvars * sizeof *mr->cvars); + factor.n_vars = mr->n_cvars; + } + else + { + goto error; + } while (lex_token (lexer) != T_ENDCMD) { @@ -1071,6 +1178,13 @@ cmd_factor (struct lexer *lexer, struct dataset *ds) free (factor.vars); factor.vars = vars; factor.n_vars = n_vars; + + if (mr) + { + free (mr->cvars); + mr->cvars = xmemdup (vars, n_vars * sizeof *vars); + mr->n_cvars = n_vars; + } } else if (lex_match_id (lexer, "PLOT")) { @@ -1163,7 +1277,7 @@ cmd_factor (struct lexer *lexer, struct dataset *ds) { if (lex_match_id (lexer, "FACTORS")) { - if ( lex_force_match (lexer, T_LPAREN) + if (lex_force_match (lexer, T_LPAREN) && lex_force_int (lexer)) { factor.n_factors = lex_integer (lexer); @@ -1174,7 +1288,7 @@ cmd_factor (struct lexer *lexer, struct dataset *ds) } else if (lex_match_id (lexer, "MINEIGEN")) { - if ( lex_force_match (lexer, T_LPAREN) + if (lex_force_match (lexer, T_LPAREN) && lex_force_num (lexer)) { factor.min_eigen = lex_number (lexer); @@ -1185,7 +1299,7 @@ cmd_factor (struct lexer *lexer, struct dataset *ds) } else if (lex_match_id (lexer, "ECONVERGE")) { - if ( lex_force_match (lexer, T_LPAREN) + if (lex_force_match (lexer, T_LPAREN) && lex_force_num (lexer)) { factor.econverge = lex_number (lexer); @@ -1207,8 +1321,8 @@ cmd_factor (struct lexer *lexer, struct dataset *ds) } else if (lex_match_id (lexer, "ITERATE")) { - if ( lex_force_match (lexer, T_LPAREN) - && lex_force_int (lexer)) + if (lex_force_match (lexer, T_LPAREN) + && lex_force_int_range (lexer, "ITERATE", 0, INT_MAX)) { n_iterations = lex_integer (lexer); lex_get (lexer); @@ -1269,7 +1383,7 @@ cmd_factor (struct lexer *lexer, struct dataset *ds) } else if (lex_match_id (lexer, "BLANK")) { - if ( lex_force_match (lexer, T_LPAREN) + if (lex_force_match (lexer, T_LPAREN) && lex_force_num (lexer)) { factor.blank = lex_number (lexer); @@ -1308,10 +1422,11 @@ cmd_factor (struct lexer *lexer, struct dataset *ds) else if (lex_match_id (lexer, "INV")) { } +#endif else if (lex_match_id (lexer, "AIC")) { + factor.print |= PRINT_AIC; } -#endif else if (lex_match_id (lexer, "SIG")) { factor.print |= PRINT_SIG; @@ -1320,11 +1435,10 @@ cmd_factor (struct lexer *lexer, struct dataset *ds) { factor.print |= PRINT_CORRELATION; } -#if FACTOR_FULLY_IMPLEMENTED else if (lex_match_id (lexer, "COVARIANCE")) { + factor.print |= PRINT_COVARIANCE; } -#endif else if (lex_match_id (lexer, "ROTATION")) { factor.print |= PRINT_ROTATION; @@ -1405,16 +1519,46 @@ cmd_factor (struct lexer *lexer, struct dataset *ds) } } - if ( factor.rotation == ROT_NONE ) + if (factor.rotation == ROT_NONE) factor.print &= ~PRINT_ROTATION; - if ( ! run_factor (ds, &factor)) - goto error; + if (factor.n_vars < 2) + msg (MW, _("Factor analysis on a single variable is not useful.")); + + if (factor.n_vars < 1) + { + msg (ME, _("Factor analysis without variables is not possible.")); + goto error; + } + + if (matrix_reader) + { + struct idata *id = idata_alloc (factor.n_vars); + + while (matrix_reader_next (&id->mm, mr, NULL)) + { + do_factor_by_matrix (&factor, id); + + gsl_matrix_free (id->ai_cov); + id->ai_cov = NULL; + gsl_matrix_free (id->ai_cor); + id->ai_cor = NULL; + + matrix_material_uninit (&id->mm); + } + + idata_free (id); + } + else + if (! run_factor (ds, &factor)) + goto error; + matrix_reader_destroy (mr); free (factor.vars); return CMD_SUCCESS; error: + matrix_reader_destroy (mr); free (factor.vars); return CMD_FAILURE; } @@ -1433,7 +1577,7 @@ run_factor (struct dataset *ds, const struct cmd_factor *factor) while (casegrouper_get_next_group (grouper, &group)) { - if ( factor->missing_type == MISS_LISTWISE ) + if (factor->missing_type == MISS_LISTWISE) group = casereader_create_filter_missing (group, factor->vars, factor->n_vars, factor->exclude, NULL, NULL); @@ -1474,19 +1618,19 @@ the_communality (const gsl_matrix *evec, const gsl_vector *eval, int n, int n_fa /* Return the communality of variable N, calculated to N_FACTORS */ static double -communality (struct idata *idata, int n, int n_factors) +communality (const struct idata *idata, int n, int n_factors) { return the_communality (idata->evec, idata->eval, n, n_factors); } static void -show_scree (const struct cmd_factor *f, struct idata *idata) +show_scree (const struct cmd_factor *f, const struct idata *idata) { struct scree *s; const char *label ; - if ( !(f->plot & PLOT_SCREE) ) + if (!(f->plot & PLOT_SCREE)) return; @@ -1501,576 +1645,419 @@ static void show_communalities (const struct cmd_factor * factor, const gsl_vector *initial, const gsl_vector *extracted) { - int i; - int c = 0; - const int heading_columns = 1; - int nc = heading_columns; - const int heading_rows = 1; - const int nr = heading_rows + factor->n_vars; - struct tab_table *t; - - if (factor->print & PRINT_EXTRACTION) - nc++; - - if (factor->print & PRINT_INITIAL) - nc++; - - /* No point having a table with only headings */ - if (nc <= 1) + if (!(factor->print & (PRINT_INITIAL | PRINT_EXTRACTION))) return; - t = tab_create (nc, nr); - - tab_title (t, _("Communalities")); - - tab_headers (t, heading_columns, 0, heading_rows, 0); + struct pivot_table *table = pivot_table_create (N_("Communalities")); - c = 1; + struct pivot_dimension *communalities = pivot_dimension_create ( + table, PIVOT_AXIS_COLUMN, N_("Communalities")); if (factor->print & PRINT_INITIAL) - tab_text (t, c++, 0, TAB_CENTER | TAT_TITLE, _("Initial")); - + pivot_category_create_leaves (communalities->root, N_("Initial")); if (factor->print & PRINT_EXTRACTION) - tab_text (t, c++, 0, TAB_CENTER | TAT_TITLE, _("Extraction")); - - /* Outline the box */ - tab_box (t, - TAL_2, TAL_2, - -1, -1, - 0, 0, - nc - 1, nr - 1); - - /* Vertical lines */ - tab_box (t, - -1, -1, - -1, TAL_1, - heading_columns, 0, - nc - 1, nr - 1); - - tab_hline (t, TAL_1, 0, nc - 1, heading_rows); - tab_vline (t, TAL_2, heading_columns, 0, nr - 1); - - for (i = 0 ; i < factor->n_vars; ++i) + pivot_category_create_leaves (communalities->root, N_("Extraction")); + + struct pivot_dimension *variables = pivot_dimension_create ( + table, PIVOT_AXIS_ROW, N_("Variables")); + + for (size_t i = 0 ; i < factor->n_vars; ++i) { - c = 0; - tab_text (t, c++, i + heading_rows, TAT_TITLE, var_to_string (factor->vars[i])); + int row = pivot_category_create_leaf ( + variables->root, pivot_value_new_variable (factor->vars[i])); + int col = 0; if (factor->print & PRINT_INITIAL) - tab_double (t, c++, i + heading_rows, 0, gsl_vector_get (initial, i), NULL, RC_OTHER); - + pivot_table_put2 (table, col++, row, pivot_value_new_number ( + gsl_vector_get (initial, i))); if (factor->print & PRINT_EXTRACTION) - tab_double (t, c++, i + heading_rows, 0, gsl_vector_get (extracted, i), NULL, RC_OTHER); + pivot_table_put2 (table, col++, row, pivot_value_new_number ( + gsl_vector_get (extracted, i))); } - tab_submit (t); + pivot_table_submit (table); } +static struct pivot_dimension * +create_numeric_dimension (struct pivot_table *table, + enum pivot_axis_type axis_type, const char *name, + size_t n, bool show_label) +{ + struct pivot_dimension *d = pivot_dimension_create (table, axis_type, name); + d->root->show_label = show_label; + for (int i = 0 ; i < n; ++i) + pivot_category_create_leaf (d->root, pivot_value_new_integer (i + 1)); + return d; +} static void -show_factor_matrix (const struct cmd_factor *factor, struct idata *idata, const char *title, const gsl_matrix *fm) +show_factor_matrix (const struct cmd_factor *factor, const struct idata *idata, const char *title, const gsl_matrix *fm) { - int i; + struct pivot_table *table = pivot_table_create (title); const int n_factors = idata->n_extractions; + create_numeric_dimension ( + table, PIVOT_AXIS_COLUMN, + factor->extraction == EXTRACTION_PC ? N_("Component") : N_("Factor"), + n_factors, true); - const int heading_columns = 1; - const int heading_rows = 2; - const int nr = heading_rows + factor->n_vars; - const int nc = heading_columns + n_factors; - gsl_permutation *perm; - - struct tab_table *t = tab_create (nc, nr); - - /* - if ( factor->extraction == EXTRACTION_PC ) - tab_title (t, _("Component Matrix")); - else - tab_title (t, _("Factor Matrix")); - */ - - tab_title (t, "%s", title); - - tab_headers (t, heading_columns, 0, heading_rows, 0); - - if ( factor->extraction == EXTRACTION_PC ) - tab_joint_text (t, - 1, 0, - nc - 1, 0, - TAB_CENTER | TAT_TITLE, _("Component")); - else - tab_joint_text (t, - 1, 0, - nc - 1, 0, - TAB_CENTER | TAT_TITLE, _("Factor")); - - - tab_hline (t, TAL_1, heading_columns, nc - 1, 1); - - - /* Outline the box */ - tab_box (t, - TAL_2, TAL_2, - -1, -1, - 0, 0, - nc - 1, nr - 1); - - /* Vertical lines */ - tab_box (t, - -1, -1, - -1, TAL_1, - heading_columns, 1, - nc - 1, nr - 1); - - tab_hline (t, TAL_1, 0, nc - 1, heading_rows); - tab_vline (t, TAL_2, heading_columns, 0, nr - 1); - + struct pivot_dimension *variables = pivot_dimension_create ( + table, PIVOT_AXIS_ROW, N_("Variables")); /* Initialise to the identity permutation */ - perm = gsl_permutation_calloc (factor->n_vars); + gsl_permutation *perm = gsl_permutation_calloc (factor->n_vars); - if ( factor->sort) + if (factor->sort) sort_matrix_indirect (fm, perm); - for (i = 0 ; i < n_factors; ++i) - { - tab_text_format (t, heading_columns + i, 1, TAB_CENTER | TAT_TITLE, _("%d"), i + 1); - } - - for (i = 0 ; i < factor->n_vars; ++i) + for (size_t i = 0 ; i < factor->n_vars; ++i) { - int j; const int matrix_row = perm->data[i]; - tab_text (t, 0, i + heading_rows, TAT_TITLE, var_to_string (factor->vars[matrix_row])); - for (j = 0 ; j < n_factors; ++j) + int var_idx = pivot_category_create_leaf ( + variables->root, pivot_value_new_variable (factor->vars[matrix_row])); + + for (size_t j = 0 ; j < n_factors; ++j) { double x = gsl_matrix_get (fm, matrix_row, j); - - if ( fabs (x) < factor->blank) + if (fabs (x) < factor->blank) continue; - tab_double (t, heading_columns + j, heading_rows + i, 0, x, NULL, RC_OTHER); + pivot_table_put2 (table, j, var_idx, pivot_value_new_number (x)); } } gsl_permutation_free (perm); - tab_submit (t); + pivot_table_submit (table); } +static void +put_variance (struct pivot_table *table, int row, int phase_idx, + double lambda, double percent, double cum) +{ + double entries[] = { lambda, percent, cum }; + for (size_t i = 0; i < sizeof entries / sizeof *entries; i++) + pivot_table_put3 (table, i, phase_idx, row, + pivot_value_new_number (entries[i])); +} static void -show_explained_variance (const struct cmd_factor * factor, struct idata *idata, +show_explained_variance (const struct cmd_factor * factor, + const struct idata *idata, const gsl_vector *initial_eigenvalues, const gsl_vector *extracted_eigenvalues, const gsl_vector *rotated_loadings) { - size_t i; - int c = 0; - const int heading_columns = 1; - const int heading_rows = 2; - const int nr = heading_rows + factor->n_vars; - - struct tab_table *t ; - - double i_total = 0.0; - double i_cum = 0.0; - - double e_total = 0.0; - double e_cum = 0.0; - - double r_cum = 0.0; - - int nc = heading_columns; - - if (factor->print & PRINT_EXTRACTION) - nc += 3; - - if (factor->print & PRINT_INITIAL) - nc += 3; - - if (factor->print & PRINT_ROTATION) - { - nc += factor->rotation == ROT_PROMAX ? 1 : 3; - } - - /* No point having a table with only headings */ - if ( nc <= heading_columns) + if (!(factor->print & (PRINT_INITIAL | PRINT_EXTRACTION | PRINT_ROTATION))) return; - t = tab_create (nc, nr); - - tab_title (t, _("Total Variance Explained")); - - tab_headers (t, heading_columns, 0, heading_rows, 0); - - /* Outline the box */ - tab_box (t, - TAL_2, TAL_2, - -1, -1, - 0, 0, - nc - 1, nr - 1); - - /* Vertical lines */ - tab_box (t, - -1, -1, - -1, TAL_1, - heading_columns, 0, - nc - 1, nr - 1); - - tab_hline (t, TAL_1, 0, nc - 1, heading_rows); - tab_hline (t, TAL_1, 1, nc - 1, 1); - - tab_vline (t, TAL_2, heading_columns, 0, nr - 1); - + struct pivot_table *table = pivot_table_create ( + N_("Total Variance Explained")); - if ( factor->extraction == EXTRACTION_PC) - tab_text (t, 0, 1, TAB_LEFT | TAT_TITLE, _("Component")); - else - tab_text (t, 0, 1, TAB_LEFT | TAT_TITLE, _("Factor")); + pivot_dimension_create (table, PIVOT_AXIS_COLUMN, N_("Statistics"), + N_("Total"), PIVOT_RC_OTHER, + /* xgettext:no-c-format */ + N_("% of Variance"), PIVOT_RC_PERCENT, + /* xgettext:no-c-format */ + N_("Cumulative %"), PIVOT_RC_PERCENT); - c = 1; + struct pivot_dimension *phase = pivot_dimension_create ( + table, PIVOT_AXIS_COLUMN, N_("Phase")); if (factor->print & PRINT_INITIAL) - { - tab_joint_text (t, c, 0, c + 2, 0, TAB_CENTER | TAT_TITLE, _("Initial Eigenvalues")); - c += 3; - } + pivot_category_create_leaves (phase->root, N_("Initial Eigenvalues")); if (factor->print & PRINT_EXTRACTION) - { - tab_joint_text (t, c, 0, c + 2, 0, TAB_CENTER | TAT_TITLE, _("Extraction Sums of Squared Loadings")); - c += 3; - } + pivot_category_create_leaves (phase->root, + N_("Extraction Sums of Squared Loadings")); if (factor->print & PRINT_ROTATION) - { - const int width = factor->rotation == ROT_PROMAX ? 0 : 2; - tab_joint_text (t, c, 0, c + width, 0, TAB_CENTER | TAT_TITLE, _("Rotation Sums of Squared Loadings")); - c += width + 1; - } - - for (i = 0; i < (nc - heading_columns + 2) / 3 ; ++i) - { - tab_text (t, i * 3 + 1, 1, TAB_CENTER | TAT_TITLE, _("Total")); + pivot_category_create_leaves (phase->root, + N_("Rotation Sums of Squared Loadings")); - tab_vline (t, TAL_2, heading_columns + i * 3, 0, nr - 1); - - if (i == 2 && factor->rotation == ROT_PROMAX) - continue; - - /* xgettext:no-c-format */ - tab_text (t, i * 3 + 2, 1, TAB_CENTER | TAT_TITLE, _("% of Variance")); - tab_text (t, i * 3 + 3, 1, TAB_CENTER | TAT_TITLE, _("Cumulative %")); - } + struct pivot_dimension *components = pivot_dimension_create ( + table, PIVOT_AXIS_ROW, + factor->extraction == EXTRACTION_PC ? N_("Component") : N_("Factor")); - for (i = 0 ; i < initial_eigenvalues->size; ++i) + double i_total = 0.0; + for (size_t i = 0 ; i < initial_eigenvalues->size; ++i) i_total += gsl_vector_get (initial_eigenvalues, i); - if ( factor->extraction == EXTRACTION_PAF) - { - e_total = factor->n_vars; - } - else - { - e_total = i_total; - } + double e_total = (factor->extraction == EXTRACTION_PAF + ? factor->n_vars + : i_total); - for (i = 0 ; i < factor->n_vars; ++i) + double i_cum = 0.0; + double e_cum = 0.0; + double r_cum = 0.0; + for (size_t i = 0 ; i < factor->n_vars; ++i) { const double i_lambda = gsl_vector_get (initial_eigenvalues, i); double i_percent = 100.0 * i_lambda / i_total ; + i_cum += i_percent; const double e_lambda = gsl_vector_get (extracted_eigenvalues, i); double e_percent = 100.0 * e_lambda / e_total ; + e_cum += e_percent; - c = 0; - - tab_text_format (t, c++, i + heading_rows, TAB_LEFT | TAT_TITLE, _("%zu"), i + 1); + int row = pivot_category_create_leaf ( + components->root, pivot_value_new_integer (i + 1)); - i_cum += i_percent; - e_cum += e_percent; + int phase_idx = 0; /* Initial Eigenvalues */ if (factor->print & PRINT_INITIAL) - { - tab_double (t, c++, i + heading_rows, 0, i_lambda, NULL, RC_OTHER); - tab_double (t, c++, i + heading_rows, 0, i_percent, NULL, RC_OTHER); - tab_double (t, c++, i + heading_rows, 0, i_cum, NULL, RC_OTHER); - } - - - if (factor->print & PRINT_EXTRACTION) - { - if (i < idata->n_extractions) - { - /* Sums of squared loadings */ - tab_double (t, c++, i + heading_rows, 0, e_lambda, NULL, RC_OTHER); - tab_double (t, c++, i + heading_rows, 0, e_percent, NULL, RC_OTHER); - tab_double (t, c++, i + heading_rows, 0, e_cum, NULL, RC_OTHER); - } - } + put_variance (table, row, phase_idx++, i_lambda, i_percent, i_cum); - if (rotated_loadings != NULL) + if (i < idata->n_extractions) { - const double r_lambda = gsl_vector_get (rotated_loadings, i); - double r_percent = 100.0 * r_lambda / e_total ; + if (factor->print & PRINT_EXTRACTION) + put_variance (table, row, phase_idx++, e_lambda, e_percent, e_cum); - if (factor->print & PRINT_ROTATION) + if (rotated_loadings != NULL && factor->print & PRINT_ROTATION) { - if (i < idata->n_extractions) - { - r_cum += r_percent; - tab_double (t, c++, i + heading_rows, 0, r_lambda, NULL, RC_OTHER); - if (factor->rotation != ROT_PROMAX) - { - tab_double (t, c++, i + heading_rows, 0, r_percent, NULL, RC_OTHER); - tab_double (t, c++, i + heading_rows, 0, r_cum, NULL, RC_OTHER); - } - } + double r_lambda = gsl_vector_get (rotated_loadings, i); + double r_percent = 100.0 * r_lambda / e_total ; + if (factor->rotation == ROT_PROMAX) + r_lambda = r_percent = SYSMIS; + + r_cum += r_percent; + put_variance (table, row, phase_idx++, r_lambda, r_percent, + r_cum); } } } - tab_submit (t); + pivot_table_submit (table); } - static void show_factor_correlation (const struct cmd_factor * factor, const gsl_matrix *fcm) { - size_t i, j; - const int heading_columns = 1; - const int heading_rows = 1; - const int nr = heading_rows + fcm->size2; - const int nc = heading_columns + fcm->size1; - struct tab_table *t = tab_create (nc, nr); - - tab_title (t, _("Factor Correlation Matrix")); + struct pivot_table *table = pivot_table_create ( + N_("Factor Correlation Matrix")); - tab_headers (t, heading_columns, 0, heading_rows, 0); + create_numeric_dimension ( + table, PIVOT_AXIS_ROW, + factor->extraction == EXTRACTION_PC ? N_("Component") : N_("Factor"), + fcm->size2, true); - /* Outline the box */ - tab_box (t, - TAL_2, TAL_2, - -1, -1, - 0, 0, - nc - 1, nr - 1); + create_numeric_dimension (table, PIVOT_AXIS_COLUMN, N_("Factor 2"), + fcm->size1, false); - /* Vertical lines */ - tab_box (t, - -1, -1, - -1, TAL_1, - heading_columns, 0, - nc - 1, nr - 1); + for (size_t i = 0 ; i < fcm->size1; ++i) + for (size_t j = 0 ; j < fcm->size2; ++j) + pivot_table_put2 (table, j, i, + pivot_value_new_number (gsl_matrix_get (fcm, i, j))); - tab_hline (t, TAL_1, 0, nc - 1, heading_rows); - tab_hline (t, TAL_1, 1, nc - 1, 1); - - tab_vline (t, TAL_2, heading_columns, 0, nr - 1); - - - if ( factor->extraction == EXTRACTION_PC) - tab_text (t, 0, 0, TAB_LEFT | TAT_TITLE, _("Component")); - else - tab_text (t, 0, 0, TAB_LEFT | TAT_TITLE, _("Factor")); - - for (i = 0 ; i < fcm->size1; ++i) - { - tab_text_format (t, heading_columns + i, 0, TAB_CENTER | TAT_TITLE, _("%zu"), i + 1); - } + pivot_table_submit (table); +} - for (i = 0 ; i < fcm->size2; ++i) +static void +add_var_dims (struct pivot_table *table, const struct cmd_factor *factor) +{ + for (int i = 0; i < 2; i++) { - tab_text_format (t, 0, heading_rows + i, TAB_CENTER | TAT_TITLE, _("%zu"), i + 1); - } - + struct pivot_dimension *d = pivot_dimension_create ( + table, i ? PIVOT_AXIS_ROW : PIVOT_AXIS_COLUMN, + N_("Variables")); - for (i = 0 ; i < fcm->size1; ++i) - { - for (j = 0 ; j < fcm->size2; ++j) - tab_double (t, heading_columns + i, heading_rows +j, 0, - gsl_matrix_get (fcm, i, j), NULL, RC_OTHER); + for (size_t j = 0; j < factor->n_vars; j++) + pivot_category_create_leaf ( + d->root, pivot_value_new_variable (factor->vars[j])); } - - tab_submit (t); } - static void -show_correlation_matrix (const struct cmd_factor *factor, const struct idata *idata) +show_aic (const struct cmd_factor *factor, const struct idata *idata) { - struct tab_table *t ; - size_t i, j; - int y_pos_corr = -1; - int y_pos_sig = -1; - int suffix_rows = 0; + if ((factor->print & PRINT_AIC) == 0) + return; - const int heading_rows = 1; - const int heading_columns = 2; + struct pivot_table *table = pivot_table_create (N_("Anti-Image Matrices")); - int nc = heading_columns ; - int nr = heading_rows ; - int n_data_sets = 0; + add_var_dims (table, factor); - if (factor->print & PRINT_CORRELATION) - { - y_pos_corr = n_data_sets; - n_data_sets++; - nc = heading_columns + factor->n_vars; - } + pivot_dimension_create (table, PIVOT_AXIS_ROW, N_("Statistics"), + N_("Anti-image Covariance"), + N_("Anti-image Correlation")); - if (factor->print & PRINT_SIG) - { - y_pos_sig = n_data_sets; - n_data_sets++; - nc = heading_columns + factor->n_vars; - } + for (size_t i = 0; i < factor->n_vars; ++i) + for (size_t j = 0; j < factor->n_vars; ++j) + { + double cov = gsl_matrix_get (idata->ai_cov, i, j); + pivot_table_put3 (table, i, j, 0, pivot_value_new_number (cov)); - nr += n_data_sets * factor->n_vars; + double corr = gsl_matrix_get (idata->ai_cor, i, j); + pivot_table_put3 (table, i, j, 1, pivot_value_new_number (corr)); + } - if (factor->print & PRINT_DETERMINANT) - suffix_rows = 1; + pivot_table_submit (table); +} - /* If the table would contain only headings, don't bother rendering it */ - if (nr <= heading_rows && suffix_rows == 0) +static void +show_correlation_matrix (const struct cmd_factor *factor, const struct idata *idata) +{ + if (!(factor->print & (PRINT_CORRELATION | PRINT_SIG | PRINT_DETERMINANT))) return; - t = tab_create (nc, nr + suffix_rows); - - tab_title (t, _("Correlation Matrix")); + struct pivot_table *table = pivot_table_create (N_("Correlation Matrix")); - tab_hline (t, TAL_1, 0, nc - 1, heading_rows); - - if (nr > heading_rows) + if (factor->print & (PRINT_CORRELATION | PRINT_SIG)) { - tab_headers (t, heading_columns, 0, heading_rows, 0); - - tab_vline (t, TAL_2, 2, 0, nr - 1); - - /* Outline the box */ - tab_box (t, - TAL_2, TAL_2, - -1, -1, - 0, 0, - nc - 1, nr - 1); - - /* Vertical lines */ - tab_box (t, - -1, -1, - -1, TAL_1, - heading_columns, 0, - nc - 1, nr - 1); - - - for (i = 0; i < factor->n_vars; ++i) - tab_text (t, heading_columns + i, 0, TAT_TITLE, var_to_string (factor->vars[i])); - - - for (i = 0 ; i < n_data_sets; ++i) - { - int y = heading_rows + i * factor->n_vars; - size_t v; - for (v = 0; v < factor->n_vars; ++v) - tab_text (t, 1, y + v, TAT_TITLE, var_to_string (factor->vars[v])); - - tab_hline (t, TAL_1, 0, nc - 1, y); - } + add_var_dims (table, factor); + struct pivot_dimension *statistics = pivot_dimension_create ( + table, PIVOT_AXIS_ROW, N_("Statistics")); if (factor->print & PRINT_CORRELATION) - { - const double y = heading_rows + y_pos_corr; - tab_text (t, 0, y, TAT_TITLE, _("Correlations")); - - for (i = 0; i < factor->n_vars; ++i) - { - for (j = 0; j < factor->n_vars; ++j) - tab_double (t, heading_columns + i, y + j, 0, gsl_matrix_get (idata->corr, i, j), NULL, RC_OTHER); - } - } - + pivot_category_create_leaves (statistics->root, N_("Correlation"), + PIVOT_RC_CORRELATION); if (factor->print & PRINT_SIG) - { - const double y = heading_rows + y_pos_sig * factor->n_vars; - tab_text (t, 0, y, TAT_TITLE, _("Sig. (1-tailed)")); + pivot_category_create_leaves (statistics->root, N_("Sig. (1-tailed)"), + PIVOT_RC_SIGNIFICANCE); - for (i = 0; i < factor->n_vars; ++i) - { - for (j = 0; j < factor->n_vars; ++j) - { - double rho = gsl_matrix_get (idata->corr, i, j); - double w = gsl_matrix_get (idata->n, i, j); - - if (i == j) - continue; + int stat_idx = 0; + if (factor->print & PRINT_CORRELATION) + { + for (int i = 0; i < factor->n_vars; ++i) + for (int j = 0; j < factor->n_vars; ++j) + { + double corr = gsl_matrix_get (idata->mm.corr, i, j); + pivot_table_put3 (table, j, i, stat_idx, + pivot_value_new_number (corr)); + } + stat_idx++; + } - tab_double (t, heading_columns + i, y + j, 0, significance_of_correlation (rho, w), NULL, RC_PVALUE); - } - } - } + if (factor->print & PRINT_SIG) + { + for (int i = 0; i < factor->n_vars; ++i) + for (int j = 0; j < factor->n_vars; ++j) + if (i != j) + { + double rho = gsl_matrix_get (idata->mm.corr, i, j); + double w = gsl_matrix_get (idata->mm.n, i, j); + double sig = significance_of_correlation (rho, w); + pivot_table_put3 (table, j, i, stat_idx, + pivot_value_new_number (sig)); + } + stat_idx++; + } } if (factor->print & PRINT_DETERMINANT) { - tab_text (t, 0, nr, TAB_LEFT | TAT_TITLE, _("Determinant")); - - tab_double (t, 1, nr, 0, idata->detR, NULL, RC_OTHER); + struct pivot_value *caption = pivot_value_new_user_text_nocopy ( + xasprintf ("%s: %.2f", _("Determinant"), idata->detR)); + pivot_table_set_caption (table, caption); } - tab_submit (t); + pivot_table_submit (table); } +static void +show_covariance_matrix (const struct cmd_factor *factor, const struct idata *idata) +{ + if (!(factor->print & PRINT_COVARIANCE)) + return; + + struct pivot_table *table = pivot_table_create (N_("Covariance Matrix")); + add_var_dims (table, factor); + + for (int i = 0; i < factor->n_vars; ++i) + for (int j = 0; j < factor->n_vars; ++j) + { + double cov = gsl_matrix_get (idata->mm.cov, i, j); + pivot_table_put2 (table, j, i, pivot_value_new_number (cov)); + } + + pivot_table_submit (table); +} static void do_factor (const struct cmd_factor *factor, struct casereader *r) { struct ccase *c; - const gsl_matrix *var_matrix; - const gsl_matrix *mean_matrix; - - const gsl_matrix *analysis_matrix; struct idata *idata = idata_alloc (factor->n_vars); - struct covariance *cov = covariance_1pass_create (factor->n_vars, factor->vars, - factor->wv, factor->exclude); + idata->cvm = covariance_1pass_create (factor->n_vars, factor->vars, + factor->wv, factor->exclude, true); - for ( ; (c = casereader_read (r) ); case_unref (c)) + for (; (c = casereader_read (r)); case_unref (c)) { - covariance_accumulate (cov, c); + covariance_accumulate (idata->cvm, c); } - idata->cov = covariance_calculate (cov); + idata->mm.cov = covariance_calculate (idata->cvm); - if (idata->cov == NULL) + if (idata->mm.cov == NULL) { msg (MW, _("The dataset contains no complete observations. No analysis will be performed.")); - covariance_destroy (cov); + covariance_destroy (idata->cvm); goto finish; } - var_matrix = covariance_moments (cov, MOMENT_VARIANCE); - mean_matrix = covariance_moments (cov, MOMENT_MEAN); - idata->n = covariance_moments (cov, MOMENT_NONE); - + idata->mm.var_matrix = covariance_moments (idata->cvm, MOMENT_VARIANCE); + idata->mm.mean_matrix = covariance_moments (idata->cvm, MOMENT_MEAN); + idata->mm.n = covariance_moments (idata->cvm, MOMENT_NONE); + + do_factor_by_matrix (factor, idata); + + finish: + gsl_matrix_free (idata->mm.corr); + gsl_matrix_free (idata->mm.cov); + + idata_free (idata); + casereader_destroy (r); +} - if ( factor->method == METHOD_CORR) +static void +do_factor_by_matrix (const struct cmd_factor *factor, struct idata *idata) +{ + if (!idata->mm.cov && !(idata->mm.corr && idata->mm.var_matrix)) { - idata->corr = correlation_from_covariance (idata->cov, var_matrix); - - analysis_matrix = idata->corr; + msg (ME, _("The dataset has no covariance matrix or a " + "correlation matrix along with standard deviations.")); + return; } + + if (idata->mm.cov && !idata->mm.corr) + idata->mm.corr = correlation_from_covariance (idata->mm.cov, idata->mm.var_matrix); + if (idata->mm.corr && !idata->mm.cov) + idata->mm.cov = covariance_from_correlation (idata->mm.corr, idata->mm.var_matrix); + if (factor->method == METHOD_CORR) + idata->analysis_matrix = idata->mm.corr; else - analysis_matrix = idata->cov; + idata->analysis_matrix = idata->mm.cov; + + gsl_matrix *r_inv; + r_inv = clone_matrix (idata->mm.corr); + gsl_linalg_cholesky_decomp (r_inv); + gsl_linalg_cholesky_invert (r_inv); + idata->ai_cov = anti_image_cov (r_inv); + idata->ai_cor = anti_image_corr (r_inv, idata); + + int i; + double sum_ssq_r = 0; + double sum_ssq_a = 0; + for (i = 0; i < r_inv->size1; ++i) + { + sum_ssq_r += ssq_od_n (idata->mm.corr, i); + sum_ssq_a += ssq_od_n (idata->ai_cor, i); + } + + gsl_matrix_free (r_inv); if (factor->print & PRINT_DETERMINANT || factor->print & PRINT_KMO) { int sign = 0; - const int size = idata->corr->size1; + const int size = idata->mm.corr->size1; gsl_permutation *p = gsl_permutation_calloc (size); gsl_matrix *tmp = gsl_matrix_calloc (size, size); - gsl_matrix_memcpy (tmp, idata->corr); + gsl_matrix_memcpy (tmp, idata->mm.corr); gsl_linalg_LU_decomp (tmp, p, &sign); idata->detR = gsl_linalg_LU_det (tmp, sign); @@ -2078,144 +2065,87 @@ do_factor (const struct cmd_factor *factor, struct casereader *r) gsl_matrix_free (tmp); } - if ( factor->print & PRINT_UNIVARIATE) + if (factor->print & PRINT_UNIVARIATE + && idata->mm.n && idata->mm.mean_matrix && idata->mm.var_matrix) { - const struct fmt_spec *wfmt = factor->wv ? var_get_print_format (factor->wv) : & F_8_0; - const int nc = 4; - int i; - - const int heading_columns = 1; - const int heading_rows = 1; + struct pivot_table *table = pivot_table_create ( + N_("Descriptive Statistics")); + pivot_table_set_weight_var (table, factor->wv); - const int nr = heading_rows + factor->n_vars; + pivot_dimension_create (table, PIVOT_AXIS_COLUMN, N_("Statistics"), + N_("Mean"), PIVOT_RC_OTHER, + N_("Std. Deviation"), PIVOT_RC_OTHER, + N_("Analysis N"), PIVOT_RC_COUNT); - struct tab_table *t = tab_create (nc, nr); - tab_set_format (t, RC_WEIGHT, wfmt); - tab_title (t, _("Descriptive Statistics")); - - tab_headers (t, heading_columns, 0, heading_rows, 0); - - /* Outline the box */ - tab_box (t, - TAL_2, TAL_2, - -1, -1, - 0, 0, - nc - 1, nr - 1); - - /* Vertical lines */ - tab_box (t, - -1, -1, - -1, TAL_1, - heading_columns, 0, - nc - 1, nr - 1); - - tab_hline (t, TAL_1, 0, nc - 1, heading_rows); - tab_vline (t, TAL_2, heading_columns, 0, nr - 1); - - tab_text (t, 1, 0, TAB_CENTER | TAT_TITLE, _("Mean")); - tab_text (t, 2, 0, TAB_CENTER | TAT_TITLE, _("Std. Deviation")); - tab_text (t, 3, 0, TAB_CENTER | TAT_TITLE, _("Analysis N")); + struct pivot_dimension *variables = pivot_dimension_create ( + table, PIVOT_AXIS_ROW, N_("Variables")); for (i = 0 ; i < factor->n_vars; ++i) { const struct variable *v = factor->vars[i]; - tab_text (t, 0, i + heading_rows, TAB_LEFT | TAT_TITLE, var_to_string (v)); - tab_double (t, 1, i + heading_rows, 0, gsl_matrix_get (mean_matrix, i, i), NULL, RC_OTHER); - tab_double (t, 2, i + heading_rows, 0, sqrt (gsl_matrix_get (var_matrix, i, i)), NULL, RC_OTHER); - tab_double (t, 3, i + heading_rows, 0, gsl_matrix_get (idata->n, i, i), NULL, RC_WEIGHT); + int row = pivot_category_create_leaf ( + variables->root, pivot_value_new_variable (v)); + + double entries[] = { + gsl_matrix_get (idata->mm.mean_matrix, i, i), + sqrt (gsl_matrix_get (idata->mm.var_matrix, i, i)), + gsl_matrix_get (idata->mm.n, i, i), + }; + for (size_t j = 0; j < sizeof entries / sizeof *entries; j++) + pivot_table_put2 (table, j, row, + pivot_value_new_number (entries[j])); } - tab_submit (t); + pivot_table_submit (table); } - if (factor->print & PRINT_KMO) + if (factor->print & PRINT_KMO && idata->mm.n) { - int i; - double sum_ssq_r = 0; - double sum_ssq_a = 0; - - double df = factor->n_vars * ( factor->n_vars - 1) / 2; - - double w = 0; - - - double xsq; - - const int heading_columns = 2; - const int heading_rows = 0; - - const int nr = heading_rows + 4; - const int nc = heading_columns + 1; - - gsl_matrix *a, *x; - - struct tab_table *t = tab_create (nc, nr); - tab_title (t, _("KMO and Bartlett's Test")); - - x = clone_matrix (idata->corr); - gsl_linalg_cholesky_decomp (x); - gsl_linalg_cholesky_invert (x); - - a = anti_image (x); - - for (i = 0; i < x->size1; ++i) - { - sum_ssq_r += ssq_od_n (x, i); - sum_ssq_a += ssq_od_n (a, i); - } - - gsl_matrix_free (a); - gsl_matrix_free (x); - - tab_headers (t, heading_columns, 0, heading_rows, 0); - - /* Outline the box */ - tab_box (t, - TAL_2, TAL_2, - -1, -1, - 0, 0, - nc - 1, nr - 1); - - tab_vline (t, TAL_2, heading_columns, 0, nr - 1); - - tab_text (t, 0, 0, TAT_TITLE | TAB_LEFT, _("Kaiser-Meyer-Olkin Measure of Sampling Adequacy")); - - tab_double (t, 2, 0, 0, sum_ssq_r / (sum_ssq_r + sum_ssq_a), NULL, RC_OTHER); - - tab_text (t, 0, 1, TAT_TITLE | TAB_LEFT, _("Bartlett's Test of Sphericity")); - - tab_text (t, 1, 1, TAT_TITLE, _("Approx. Chi-Square")); - tab_text (t, 1, 2, TAT_TITLE, _("df")); - tab_text (t, 1, 3, TAT_TITLE, _("Sig.")); - - - /* The literature doesn't say what to do for the value of W when + struct pivot_table *table = pivot_table_create ( + N_("KMO and Bartlett's Test")); + + struct pivot_dimension *statistics = pivot_dimension_create ( + table, PIVOT_AXIS_ROW, N_("Statistics"), + N_("Kaiser-Meyer-Olkin Measure of Sampling Adequacy"), PIVOT_RC_OTHER); + pivot_category_create_group ( + statistics->root, N_("Bartlett's Test of Sphericity"), + N_("Approx. Chi-Square"), PIVOT_RC_OTHER, + N_("df"), PIVOT_RC_INTEGER, + N_("Sig."), PIVOT_RC_SIGNIFICANCE); + + /* The literature doesn't say what to do for the value of W when missing values are involved. The best thing I can think of is to take the mean average. */ - w = 0; - for (i = 0; i < idata->n->size1; ++i) - w += gsl_matrix_get (idata->n, i, i); - w /= idata->n->size1; - - xsq = w - 1 - (2 * factor->n_vars + 5) / 6.0; - xsq *= -log (idata->detR); - - tab_double (t, 2, 1, 0, xsq, NULL, RC_OTHER); - tab_double (t, 2, 2, 0, df, NULL, RC_INTEGER); - tab_double (t, 2, 3, 0, gsl_cdf_chisq_Q (xsq, df), NULL, RC_PVALUE); - - - tab_submit (t); + double w = 0; + for (i = 0; i < idata->mm.n->size1; ++i) + w += gsl_matrix_get (idata->mm.n, i, i); + w /= idata->mm.n->size1; + + double xsq = ((w - 1 - (2 * factor->n_vars + 5) / 6.0) + * -log (idata->detR)); + double df = factor->n_vars * (factor->n_vars - 1) / 2; + double entries[] = { + sum_ssq_r / (sum_ssq_r + sum_ssq_a), + xsq, + df, + gsl_cdf_chisq_Q (xsq, df) + }; + for (size_t i = 0; i < sizeof entries / sizeof *entries; i++) + pivot_table_put1 (table, i, pivot_value_new_number (entries[i])); + + pivot_table_submit (table); } show_correlation_matrix (factor, idata); - covariance_destroy (cov); + show_covariance_matrix (factor, idata); + if (idata->cvm) + covariance_destroy (idata->cvm); { - gsl_matrix *am = matrix_dup (analysis_matrix); + gsl_matrix *am = matrix_dup (idata->analysis_matrix); gsl_eigen_symmv_workspace *workspace = gsl_eigen_symmv_alloc (factor->n_vars); - + gsl_eigen_symmv (am, idata->eval, idata->evec, workspace); gsl_eigen_symmv_free (workspace); @@ -2229,17 +2159,17 @@ do_factor (const struct cmd_factor *factor, struct casereader *r) if (idata->n_extractions == 0) { msg (MW, _("The %s criteria result in zero factors extracted. Therefore no analysis will be performed."), "FACTOR"); - goto finish; + return; } if (idata->n_extractions > factor->n_vars) { - msg (MW, - _("The %s criteria result in more factors than variables, which is not meaningful. No analysis will be performed."), + msg (MW, + _("The %s criteria result in more factors than variables, which is not meaningful. No analysis will be performed."), "FACTOR"); - goto finish; + return; } - + { gsl_matrix *rotated_factors = NULL; gsl_matrix *pattern_matrix = NULL; @@ -2253,14 +2183,14 @@ do_factor (const struct cmd_factor *factor, struct casereader *r) struct factor_matrix_workspace *fmw = factor_matrix_workspace_alloc (idata->msr->size, idata->n_extractions); gsl_matrix *factor_matrix = gsl_matrix_calloc (factor->n_vars, fmw->n_factors); - if ( factor->extraction == EXTRACTION_PAF) + if (factor->extraction == EXTRACTION_PAF) { gsl_vector *diff = gsl_vector_alloc (idata->msr->size); - struct smr_workspace *ws = ws_create (analysis_matrix); + struct smr_workspace *ws = ws_create (idata->analysis_matrix); for (i = 0 ; i < factor->n_vars ; ++i) { - double r2 = squared_multiple_correlation (analysis_matrix, i, ws); + double r2 = squared_multiple_correlation (idata->analysis_matrix, i, ws); gsl_vector_set (idata->msr, i, r2); } @@ -2273,13 +2203,13 @@ do_factor (const struct cmd_factor *factor, struct casereader *r) double min, max; gsl_vector_memcpy (diff, idata->msr); - iterate_factor_matrix (analysis_matrix, idata->msr, factor_matrix, fmw); - + iterate_factor_matrix (idata->analysis_matrix, idata->msr, factor_matrix, fmw); + gsl_vector_sub (diff, idata->msr); gsl_vector_minmax (diff, &min, &max); - - if ( fabs (min) < factor->econverge && fabs (max) < factor->econverge) + + if (fabs (min) < factor->econverge && fabs (max) < factor->econverge) break; } gsl_vector_free (diff); @@ -2296,17 +2226,17 @@ do_factor (const struct cmd_factor *factor, struct casereader *r) gsl_vector_memcpy (extracted_communalities, initial_communalities); - iterate_factor_matrix (analysis_matrix, extracted_communalities, factor_matrix, fmw); + iterate_factor_matrix (idata->analysis_matrix, extracted_communalities, factor_matrix, fmw); extracted_eigenvalues = idata->eval; } + show_aic (factor, idata); show_communalities (factor, initial_communalities, extracted_communalities); - - if ( factor->rotation != ROT_NONE) + if (factor->rotation != ROT_NONE) { rotated_factors = gsl_matrix_calloc (factor_matrix->size1, factor_matrix->size2); rotated_loadings = gsl_vector_calloc (factor_matrix->size2); @@ -2315,11 +2245,11 @@ do_factor (const struct cmd_factor *factor, struct casereader *r) pattern_matrix = gsl_matrix_calloc (factor_matrix->size1, factor_matrix->size2); fcm = gsl_matrix_calloc (factor_matrix->size2, factor_matrix->size2); } - + rotate (factor, factor_matrix, extracted_communalities, rotated_factors, rotated_loadings, pattern_matrix, fcm); } - + show_explained_variance (factor, idata, idata->eval, extracted_eigenvalues, rotated_loadings); factor_matrix_workspace_free (fmw); @@ -2327,26 +2257,31 @@ do_factor (const struct cmd_factor *factor, struct casereader *r) show_scree (factor, idata); show_factor_matrix (factor, idata, - factor->extraction == EXTRACTION_PC ? _("Component Matrix") : _("Factor Matrix"), + (factor->extraction == EXTRACTION_PC + ? N_("Component Matrix") : N_("Factor Matrix")), factor_matrix); - if ( factor->rotation == ROT_PROMAX) + if (factor->rotation == ROT_PROMAX) { - show_factor_matrix (factor, idata, _("Pattern Matrix"), pattern_matrix); + show_factor_matrix (factor, idata, N_("Pattern Matrix"), + pattern_matrix); gsl_matrix_free (pattern_matrix); } - if ( factor->rotation != ROT_NONE) + if (factor->rotation != ROT_NONE) { show_factor_matrix (factor, idata, - (factor->rotation == ROT_PROMAX) ? _("Structure Matrix") : - (factor->extraction == EXTRACTION_PC ? _("Rotated Component Matrix") : _("Rotated Factor Matrix")), + (factor->rotation == ROT_PROMAX + ? N_("Structure Matrix") + : factor->extraction == EXTRACTION_PC + ? N_("Rotated Component Matrix") + : N_("Rotated Factor Matrix")), rotated_factors); gsl_matrix_free (rotated_factors); } - if ( factor->rotation == ROT_PROMAX) + if (factor->rotation == ROT_PROMAX) { show_factor_correlation (factor, fcm); gsl_matrix_free (fcm); @@ -2357,12 +2292,6 @@ do_factor (const struct cmd_factor *factor, struct casereader *r) gsl_vector_free (initial_communalities); gsl_vector_free (extracted_communalities); } - - finish: - - idata_free (idata); - - casereader_destroy (r); }