/* 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
#include <gsl/gsl_sort_vector.h>
#include <gsl/gsl_cdf.h>
+#include "data/any-reader.h"
#include "data/casegrouper.h"
#include "data/casereader.h"
#include "data/casewriter.h"
#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 "output/charts/scree.h"
#include "output/tab.h"
+
#include "gettext.h"
#define _(msgid) gettext (msgid)
#define N_(msgid) msgid
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 */
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 *
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 */
static double
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
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;
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 = create_matrix_reader_from_case_reader (dict, matrix_reader,
+ &factor.vars, &factor.n_vars);
+ }
+ else
+ {
+ goto error;
+ }
while (lex_token (lexer) != T_ENDCMD)
{
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;
{
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;
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 (next_matrix_from_reader (&id->mm, mr,
+ factor.vars, factor.n_vars))
+ {
+ do_factor_by_matrix (&factor, id);
+
+ gsl_matrix_free (id->mm.corr);
+ id->mm.corr = NULL;
+ gsl_matrix_free (id->mm.cov);
+ id->mm.cov = NULL;
+ }
+
+ idata_free (id);
+ }
+ else
+ if ( ! run_factor (ds, &factor))
+ goto error;
+
+ destroy_matrix_reader (mr);
free (factor.vars);
return CMD_SUCCESS;
error:
+ destroy_matrix_reader (mr);
free (factor.vars);
return CMD_FAILURE;
}
/* 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 ;
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;
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)
for (i = 0 ; i < fcm->size1; ++i)
{
for (j = 0 ; j < fcm->size2; ++j)
- tab_double (t, heading_columns + i, heading_rows +j, 0,
+ tab_double (t, heading_columns + j, heading_rows + i, 0,
gsl_matrix_get (fcm, i, j), NULL, RC_OTHER);
}
tab_submit (t);
}
+static void
+show_aic (const struct cmd_factor *factor, const struct idata *idata)
+{
+ struct tab_table *t ;
+ size_t i;
+
+ const int heading_rows = 1;
+ const int heading_columns = 2;
+
+ const int nc = heading_columns + factor->n_vars;
+ const int nr = heading_rows + 2 * factor->n_vars;
+
+ if ((factor->print & PRINT_AIC) == 0)
+ return;
+
+ t = tab_create (nc, nr);
+
+ tab_title (t, _("Anti-Image Matrices"));
+
+ tab_hline (t, TAL_1, 0, nc - 1, heading_rows);
+
+ 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]));
+
+ tab_text (t, 0, heading_rows, TAT_TITLE, _("Anti-image Covariance"));
+ tab_hline (t, TAL_1, 0, nc - 1, heading_rows + factor->n_vars);
+ tab_text (t, 0, heading_rows + factor->n_vars, TAT_TITLE, _("Anti-image Correlation"));
+
+ for (i = 0; i < factor->n_vars; ++i)
+ {
+ tab_text (t, 1, i + heading_rows, TAT_TITLE,
+ var_to_string (factor->vars[i]));
+
+ tab_text (t, 1, factor->n_vars + i + heading_rows, TAT_TITLE,
+ var_to_string (factor->vars[i]));
+ }
+
+ for (i = 0; i < factor->n_vars; ++i)
+ {
+ int j;
+ for (j = 0; j < factor->n_vars; ++j)
+ {
+ tab_double (t, heading_columns + i, heading_rows + j, 0,
+ gsl_matrix_get (idata->ai_cov, i, j), NULL, RC_OTHER);
+ }
+
+
+ for (j = 0; j < factor->n_vars; ++j)
+ {
+ tab_double (t, heading_columns + i, factor->n_vars + heading_rows + j, 0,
+ gsl_matrix_get (idata->ai_cor, i, j), NULL, RC_OTHER);
+ }
+ }
+
+ tab_submit (t);
+}
static void
show_correlation_matrix (const struct cmd_factor *factor, const struct idata *idata)
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);
+ tab_double (t, heading_columns + j, y + i, 0, gsl_matrix_get (idata->mm.corr, i, j), NULL, RC_OTHER);
}
}
{
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);
+ double rho = gsl_matrix_get (idata->mm.corr, i, j);
+ double w = gsl_matrix_get (idata->mm.n, i, j);
if (i == j)
continue;
- tab_double (t, heading_columns + i, y + j, 0, significance_of_correlation (rho, w), NULL, RC_PVALUE);
+ tab_double (t, heading_columns + j, y + i, 0, significance_of_correlation (rho, w), NULL, RC_PVALUE);
}
}
}
tab_submit (t);
}
+static void
+show_covariance_matrix (const struct cmd_factor *factor, const struct idata *idata)
+{
+ struct tab_table *t ;
+ size_t i, j;
+ int y_pos_corr = -1;
+ int suffix_rows = 0;
+
+ const int heading_rows = 1;
+ const int heading_columns = 1;
+
+ int nc = heading_columns ;
+ int nr = heading_rows ;
+ int n_data_sets = 0;
+
+ if (factor->print & PRINT_COVARIANCE)
+ {
+ y_pos_corr = n_data_sets;
+ n_data_sets++;
+ nc = heading_columns + factor->n_vars;
+ }
+
+ nr += n_data_sets * factor->n_vars;
+
+ /* If the table would contain only headings, don't bother rendering it */
+ if (nr <= heading_rows && suffix_rows == 0)
+ return;
+
+ t = tab_create (nc, nr + suffix_rows);
+
+ tab_title (t, _("Covariance Matrix"));
+
+ tab_hline (t, TAL_1, 0, nc - 1, heading_rows);
+
+ if (nr > heading_rows)
+ {
+ tab_headers (t, heading_columns, 0, heading_rows, 0);
+
+ tab_vline (t, TAL_2, heading_columns, 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, heading_columns -1, y + v, TAT_TITLE, var_to_string (factor->vars[v]));
+
+ tab_hline (t, TAL_1, 0, nc - 1, y);
+ }
+
+ if (factor->print & PRINT_COVARIANCE)
+ {
+ const double y = heading_rows + y_pos_corr;
+
+ for (i = 0; i < factor->n_vars; ++i)
+ {
+ for (j = 0; j < factor->n_vars; ++j)
+ tab_double (t, heading_columns + j, y + i, 0, gsl_matrix_get (idata->mm.cov, i, j), NULL, RC_OTHER);
+ }
+ }
+ }
+
+ tab_submit (t);
+}
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))
{
- 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);
- if ( factor->method == METHOD_CORR)
- {
- idata->corr = correlation_from_covariance (idata->cov, var_matrix);
+ finish:
+ gsl_matrix_free (idata->mm.corr);
+ gsl_matrix_free (idata->mm.cov);
+
+ idata_free (idata);
+ casereader_destroy (r);
+}
- analysis_matrix = idata->corr;
+static void
+do_factor_by_matrix (const struct cmd_factor *factor, struct idata *idata)
+{
+ if (!idata->mm.cov && !idata->mm.corr)
+ {
+ msg (ME, _("The dataset has no complete covariance or correlation matrix."));
+ 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 (r_inv, i);
+ sum_ssq_a += ssq_od_n (idata->ai_cov, 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);
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);
+ tab_double (t, 1, i + heading_rows, 0, gsl_matrix_get (idata->mm.mean_matrix, i, i), NULL, RC_OTHER);
+ tab_double (t, 2, i + heading_rows, 0, sqrt (gsl_matrix_get (idata->mm.var_matrix, i, i)), NULL, RC_OTHER);
+ tab_double (t, 3, i + heading_rows, 0, gsl_matrix_get (idata->mm.n, i, i), NULL, RC_WEIGHT);
}
tab_submit (t);
if (factor->print & PRINT_KMO)
{
int i;
- double sum_ssq_r = 0;
- double sum_ssq_a = 0;
-
- double df = factor->n_vars * ( factor->n_vars - 1) / 2;
+ double df = factor->n_vars * (factor->n_vars - 1) / 2;
double w = 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);
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;
+ for (i = 0; i < idata->mm.n->size1; ++i)
+ w += gsl_matrix_get (idata->mm.n, i, i);
+ w /= idata->mm.n->size1;
xsq = w - 1 - (2 * factor->n_vars + 5) / 6.0;
xsq *= -log (idata->detR);
}
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);
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."),
"FACTOR");
- goto finish;
+ return;
}
{
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);
}
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_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)
{
rotated_factors = gsl_matrix_calloc (factor_matrix->size1, factor_matrix->size2);
{
show_factor_matrix (factor, idata,
(factor->rotation == ROT_PROMAX) ? _("Structure Matrix") :
- (factor->extraction == EXTRACTION_PC ? _("Rotated Component Matrix") : _("Rotated Factor Matrix")),
+ (factor->extraction == EXTRACTION_PC ? _("Rotated Component Matrix") :
+ _("Rotated Factor Matrix")),
rotated_factors);
gsl_matrix_free (rotated_factors);
gsl_vector_free (initial_communalities);
gsl_vector_free (extracted_communalities);
}
-
- finish:
-
- idata_free (idata);
-
- casereader_destroy (r);
}
projects / pspp / blobdiff