Fixes bug #44264.
be used to save the cases' cluster membership and/or their distance
from the cluster centre to the active file.
+ * The MEANS command has been re-implemented.
+
* A bug where the GUI would crash when T-TEST was executed whilst
a filter was set has been fixed.
close \
configmake \
count-one-bits \
+ count-leading-zeros \
crc \
crypto/md4 \
crypto/rijndael \
[ALL]
[NONE] ]
- [/MISSING = [TABLE] [INCLUDE] [DEPENDENT]]
+ [/MISSING = [INCLUDE] [DEPENDENT]]
@end display
You can use the @cmd{MEANS} command to calculate the arithmetic mean and similar
This behaviour can be modified with the @subcmd{/MISSING} subcommand.
Three options are possible: @subcmd{TABLE}, @subcmd{INCLUDE} and @subcmd{DEPENDENT}.
-@subcmd{/MISSING = TABLE} causes cases to be dropped if any variable is missing
-in the table specification currently being processed, regardless of
-whether it is needed to calculate the statistic.
-
@subcmd{/MISSING = INCLUDE} says that user missing values, either in the dependent
variables or in the categorical variables should be taken at their face
value, and not excluded.
src/language/stats/mann-whitney.c \
src/language/stats/mann-whitney.h \
src/language/stats/means.c \
+ src/language/stats/means.h \
+ src/language/stats/means-calc.c \
+ src/language/stats/means-parser.c \
src/language/stats/mcnemar.c \
src/language/stats/mcnemar.h \
src/language/stats/median.c \
--- /dev/null
+/* PSPP - a program for statistical analysis.
+ Copyright (C) 2011, 2012, 2013, 2019 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
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see <http://www.gnu.org/licenses/>. */
+
+#include <config.h>
+
+#include "data/case.h"
+#include "data/format.h"
+#include "data/variable.h"
+
+#include "libpspp/bt.h"
+#include "libpspp/hmap.h"
+#include "libpspp/misc.h"
+#include "libpspp/pool.h"
+
+#include "math/moments.h"
+#include "output/pivot-table.h"
+
+#include <math.h>
+
+#include "means.h"
+
+#include "gettext.h"
+#define _(msgid) gettext (msgid)
+#define N_(msgid) (msgid)
+
+struct statistic
+{
+};
+
+struct statistic_simple
+{
+ struct statistic parent;
+ double acc;
+};
+
+struct statistic_moment
+{
+ struct statistic parent;
+ struct moments1 *mom;
+};
+
+static struct statistic *
+default_create (struct pool *pool)
+{
+ struct statistic_moment *pvd = pool_alloc (pool, sizeof *pvd);
+
+ pvd->mom = moments1_create (MOMENT_KURTOSIS);
+
+ return (struct statistic *) pvd;
+}
+
+static void
+default_update (struct statistic *stat, double w, double x)
+{
+ struct statistic_moment *pvd = (struct statistic_moment *) stat;
+
+ moments1_add (pvd->mom, x, w);
+}
+
+static void
+default_destroy (struct statistic *stat)
+{
+ struct statistic_moment *pvd = (struct statistic_moment *) stat;
+ moments1_destroy (pvd->mom);
+}
+
+static void
+simple_destroy (struct statistic *stat UNUSED)
+{
+}
+
+\f
+
+/* HARMONIC MEAN: The reciprocal of the sum of the reciprocals:
+ 1 / ( 1/(x_0) + 1/(x_1) + ... + 1/(x_{n-1}) ) */
+
+struct harmonic_mean
+{
+ struct statistic parent;
+ double rsum;
+ double n;
+};
+
+static struct statistic *
+harmonic_create (struct pool *pool)
+{
+ struct harmonic_mean *hm = pool_alloc (pool, sizeof *hm);
+
+ hm->rsum = 0;
+ hm->n = 0;
+
+ return (struct statistic *) hm;
+}
+
+
+static void
+harmonic_update (struct statistic *stat, double w, double x)
+{
+ struct harmonic_mean *hm = (struct harmonic_mean *) stat;
+ hm->rsum += w / x;
+ hm->n += w;
+}
+
+
+static double
+harmonic_get (const struct statistic *pvd)
+{
+ const struct harmonic_mean *hm = (const struct harmonic_mean *) pvd;
+
+ return hm->n / hm->rsum;
+}
+
+\f
+
+/* GEOMETRIC MEAN: The nth root of the product of all n observations
+ pow ((x_0 * x_1 * ... x_{n - 1}), 1/n) */
+struct geometric_mean
+{
+ struct statistic parent;
+ double prod;
+ double n;
+};
+
+static struct statistic *
+geometric_create (struct pool *pool)
+{
+ struct geometric_mean *gm = pool_alloc (pool, sizeof *gm);
+
+ gm->prod = 1.0;
+ gm->n = 0;
+
+ return (struct statistic *) gm;
+}
+
+static void
+geometric_update (struct statistic *pvd, double w, double x)
+{
+ struct geometric_mean *gm = (struct geometric_mean *)pvd;
+ gm->prod *= pow (x, w);
+ gm->n += w;
+}
+
+
+static double
+geometric_get (const struct statistic *pvd)
+{
+ const struct geometric_mean *gm = (const struct geometric_mean *)pvd;
+ return pow (gm->prod, 1.0 / gm->n);
+}
+
+\f
+
+static double
+sum_get (const struct statistic *pvd)
+{
+ double n, mean;
+
+ moments1_calculate (((struct statistic_moment *)pvd)->mom, &n, &mean, 0, 0, 0);
+
+ return mean * n;
+}
+
+
+static double
+n_get (const struct statistic *pvd)
+{
+ double n;
+
+ moments1_calculate (((struct statistic_moment *)pvd)->mom, &n, 0, 0, 0, 0);
+
+ return n;
+}
+
+static double
+arithmean_get (const struct statistic *pvd)
+{
+ double n, mean;
+
+ moments1_calculate (((struct statistic_moment *)pvd)->mom, &n, &mean, 0, 0, 0);
+
+ return mean;
+}
+
+static double
+variance_get (const struct statistic *pvd)
+{
+ double n, mean, variance;
+
+ moments1_calculate (((struct statistic_moment *)pvd)->mom, &n, &mean, &variance, 0, 0);
+
+ return variance;
+}
+
+
+static double
+stddev_get (const struct statistic *pvd)
+{
+ return sqrt (variance_get (pvd));
+}
+
+
+\f
+
+static double
+skew_get (const struct statistic *pvd)
+{
+ double skew;
+
+ moments1_calculate (((struct statistic_moment *)pvd)->mom, NULL, NULL, NULL, &skew, 0);
+
+ return skew;
+}
+
+static double
+sekurt_get (const struct statistic *pvd)
+{
+ double n;
+
+ moments1_calculate (((struct statistic_moment *)pvd)->mom, &n, NULL, NULL, NULL, NULL);
+
+ return calc_sekurt (n);
+}
+
+static double
+seskew_get (const struct statistic *pvd)
+{
+ double n;
+
+ moments1_calculate (((struct statistic_moment *)pvd)->mom, &n, NULL, NULL, NULL, NULL);
+
+ return calc_seskew (n);
+}
+
+static double
+kurt_get (const struct statistic *pvd)
+{
+ double kurt;
+
+ moments1_calculate (((struct statistic_moment *)pvd)->mom, NULL, NULL, NULL, NULL, &kurt);
+
+ return kurt;
+}
+
+static double
+semean_get (const struct statistic *pvd)
+{
+ double n, var;
+
+ moments1_calculate (((struct statistic_moment *)pvd)->mom, &n, NULL, &var, NULL, NULL);
+
+ return sqrt (var / n);
+}
+
+\f
+
+/* MIN: The smallest (closest to minus infinity) value. */
+
+static struct statistic *
+min_create (struct pool *pool)
+{
+ struct statistic_simple *pvd = pool_alloc (pool, sizeof *pvd);
+
+ pvd->acc = DBL_MAX;
+
+ return (struct statistic *) pvd;
+}
+
+static void
+min_update (struct statistic *pvd, double w UNUSED, double x)
+{
+ double *r = &((struct statistic_simple *)pvd)->acc;
+
+ if (x < *r)
+ *r = x;
+}
+
+static double
+min_get (const struct statistic *pvd)
+{
+ double *r = &((struct statistic_simple *)pvd)->acc;
+
+ return *r;
+}
+
+/* MAX: The largest (closest to plus infinity) value. */
+
+static struct statistic *
+max_create (struct pool *pool)
+{
+ struct statistic_simple *pvd = pool_alloc (pool, sizeof *pvd);
+
+ pvd->acc = -DBL_MAX;
+
+ return (struct statistic *) pvd;
+}
+
+static void
+max_update (struct statistic *pvd, double w UNUSED, double x)
+{
+ double *r = &((struct statistic_simple *)pvd)->acc;
+
+ if (x > *r)
+ *r = x;
+}
+
+static double
+max_get (const struct statistic *pvd)
+{
+ double *r = &((struct statistic_simple *)pvd)->acc;
+
+ return *r;
+}
+
+\f
+
+struct range
+{
+ struct statistic parent;
+ double min;
+ double max;
+};
+
+static struct statistic *
+range_create (struct pool *pool)
+{
+ struct range *r = pool_alloc (pool, sizeof *r);
+
+ r->min = DBL_MAX;
+ r->max = -DBL_MAX;
+
+ return (struct statistic *) r;
+}
+
+static void
+range_update (struct statistic *pvd, double w UNUSED, double x)
+{
+ struct range *r = (struct range *) pvd;
+
+ if (x > r->max)
+ r->max = x;
+
+ if (x < r->min)
+ r->min = x;
+}
+
+static double
+range_get (const struct statistic *pvd)
+{
+ const struct range *r = (struct range *) pvd;
+
+ return r->max - r->min;
+}
+
+\f
+
+/* LAST: The last value (the one closest to the end of the file). */
+
+static struct statistic *
+last_create (struct pool *pool)
+{
+ struct statistic_simple *pvd = pool_alloc (pool, sizeof *pvd);
+
+ return (struct statistic *) pvd;
+}
+
+static void
+last_update (struct statistic *pvd, double w UNUSED, double x)
+{
+ struct statistic_simple *stat = (struct statistic_simple *) pvd;
+
+ stat->acc = x;
+}
+
+static double
+last_get (const struct statistic *pvd)
+{
+ const struct statistic_simple *stat = (struct statistic_simple *) pvd;
+
+ return stat->acc;
+}
+
+/* FIRST: The first value (the one closest to the start of the file). */
+
+static struct statistic *
+first_create (struct pool *pool)
+{
+ struct statistic_simple *pvd = pool_alloc (pool, sizeof *pvd);
+
+ pvd->acc = SYSMIS;
+
+ return (struct statistic *) pvd;
+}
+
+static void
+first_update (struct statistic *pvd, double w UNUSED, double x)
+{
+ struct statistic_simple *stat = (struct statistic_simple *) pvd;
+
+ if (stat->acc == SYSMIS)
+ stat->acc = x;
+}
+
+static double
+first_get (const struct statistic *pvd)
+{
+ const struct statistic_simple *stat = (struct statistic_simple *) pvd;
+
+ return stat->acc;
+}
+
+/* Table of cell_specs */
+const struct cell_spec cell_spec[n_MEANS_STATISTICS] = {
+ {N_("Mean"), "MEAN", PIVOT_RC_OTHER, default_create, default_update, arithmean_get, default_destroy},
+ {N_("N"), "COUNT", PIVOT_RC_COUNT, default_create, default_update, n_get, default_destroy},
+ {N_("Std. Deviation"), "STDDEV", PIVOT_RC_OTHER, default_create, default_update, stddev_get, default_destroy},
+#if 0
+ {N_("Median"), "MEDIAN", PIVOT_RC_OTHER, default_create, default_update, NULL, default_destroy},
+ {N_("Group Median"), "GMEDIAN", PIVOT_RC_OTHER, default_create, default_update, NULL, default_destroy},
+#endif
+ {N_("S.E. Mean"), "SEMEAN", PIVOT_RC_OTHER, default_create, default_update, semean_get, default_destroy},
+ {N_("Sum"), "SUM", PIVOT_RC_OTHER, default_create, default_update, sum_get, default_destroy},
+ {N_("Minimum"), "MIN", PIVOT_RC_OTHER, min_create, min_update, min_get, simple_destroy},
+ {N_("Maximum"), "MAX", PIVOT_RC_OTHER, max_create, max_update, max_get, simple_destroy},
+ {N_("Range"), "RANGE", PIVOT_RC_OTHER, range_create, range_update, range_get, simple_destroy},
+ {N_("Variance"), "VARIANCE", PIVOT_RC_OTHER, default_create, default_update, variance_get, default_destroy},
+ {N_("Kurtosis"), "KURT", PIVOT_RC_OTHER, default_create, default_update, kurt_get, default_destroy},
+ {N_("S.E. Kurt"), "SEKURT", PIVOT_RC_OTHER, default_create, default_update, sekurt_get, default_destroy},
+ {N_("Skewness"), "SKEW", PIVOT_RC_OTHER, default_create, default_update, skew_get, default_destroy},
+ {N_("S.E. Skew"), "SESKEW", PIVOT_RC_OTHER, default_create, default_update, seskew_get, default_destroy},
+ {N_("First"), "FIRST", PIVOT_RC_OTHER, first_create, first_update, first_get, simple_destroy},
+ {N_("Last"), "LAST", PIVOT_RC_OTHER, last_create, last_update, last_get, simple_destroy},
+#if 0
+ {N_("Percent N"), "NPCT", PIVOT_RC_PERCENT, default_create, default_update, NULL, default_destroy},
+ {N_("Percent Sum"), "SPCT", PIVOT_RC_PERCENT, default_create, default_update, NULL, default_destroy},
+#endif
+ {N_("Harmonic Mean"), "HARMONIC", PIVOT_RC_OTHER, harmonic_create, harmonic_update, harmonic_get, simple_destroy},
+ {N_("Geom. Mean"), "GEOMETRIC", PIVOT_RC_OTHER, geometric_create, geometric_update, geometric_get, simple_destroy}
+};
--- /dev/null
+/* PSPP - a program for statistical analysis.
+ Copyright (C) 2011, 2012, 2013, 2019 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
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see <http://www.gnu.org/licenses/>. */
+
+#include <config.h>
+
+#include "data/case.h"
+#include "data/casegrouper.h"
+#include "data/casereader.h"
+#include "data/dataset.h"
+#include "data/dictionary.h"
+#include "data/format.h"
+#include "data/variable.h"
+
+#include "language/command.h"
+#include "language/lexer/lexer.h"
+#include "language/lexer/variable-parser.h"
+
+#include "libpspp/hmap.h"
+#include "libpspp/bt.h"
+#include "libpspp/misc.h"
+#include "libpspp/pool.h"
+
+#include "means.h"
+
+/* Parse the /TABLES stanza of the command. */
+static bool
+parse_means_table_syntax (struct lexer *lexer, const struct means *cmd,
+ struct mtable *table)
+{
+ memset (table, 0, sizeof *table);
+
+ /* Dependent variable (s) */
+ if (!parse_variables_const_pool (lexer, cmd->pool, cmd->dict,
+ &table->dep_vars, &table->n_dep_vars,
+ PV_NO_DUPLICATE | PV_NUMERIC))
+ return false;
+
+ /* Factor variable (s) */
+ while (lex_match (lexer, T_BY))
+ {
+ struct layer *layer = pool_zalloc (cmd->pool, sizeof *layer);
+
+ table->layers =
+ pool_nrealloc (cmd->pool, table->layers, table->n_layers + 1,
+ sizeof *table->layers);
+ table->layers[table->n_layers] = layer;
+ table->n_layers++;
+
+ if (!parse_variables_const_pool
+ (lexer, cmd->pool, cmd->dict,
+ &layer->factor_vars,
+ &layer->n_factor_vars,
+ PV_NO_DUPLICATE))
+ return false;
+ }
+
+ return true;
+}
+
+/* Match a variable.
+ If the match succeeds, the variable will be placed in VAR.
+ Returns true if successful */
+static bool
+lex_is_variable (struct lexer *lexer, const struct dictionary *dict,
+ int n)
+{
+ const char *tstr;
+ if (lex_next_token (lexer, n) != T_ID)
+ return false;
+
+ tstr = lex_next_tokcstr (lexer, n);
+
+ if (NULL == dict_lookup_var (dict, tstr) )
+ return false;
+
+ return true;
+}
+
+static bool
+means_parse (struct lexer *lexer, struct means *means)
+{
+ /* Optional TABLES = */
+ if (lex_match_id (lexer, "TABLES"))
+ {
+ if (! lex_force_match (lexer, T_EQUALS))
+ return false;
+ }
+
+ bool more_tables = true;
+ /* Parse the "tables" */
+ while (more_tables)
+ {
+ means->table = pool_realloc (means->pool, means->table,
+ (means->n_tables + 1) * sizeof (*means->table));
+
+ if (! parse_means_table_syntax (lexer, means,
+ &means->table[means->n_tables]))
+ {
+ return false;
+ }
+ means->n_tables ++;
+
+ /* Look ahead to see if there are more tables to be parsed */
+ more_tables = false;
+ if ( T_SLASH == lex_next_token (lexer, 0) )
+ {
+ if (lex_is_variable (lexer, means->dict, 1) )
+ {
+ more_tables = true;
+ lex_match (lexer, T_SLASH);
+ }
+ }
+ }
+
+ /* /MISSING subcommand */
+ while (lex_token (lexer) != T_ENDCMD)
+ {
+ lex_match (lexer, T_SLASH);
+
+ if (lex_match_id (lexer, "MISSING"))
+ {
+ /*
+ If no MISSING subcommand is specified, each combination of
+ a dependent variable and categorical variables is handled
+ separately.
+ */
+ lex_match (lexer, T_EQUALS);
+ if (lex_match_id (lexer, "INCLUDE"))
+ {
+ /*
+ Use the subcommand "/MISSING=INCLUDE" to include user-missing
+ values in the analysis.
+ */
+
+ means->ctrl_exclude = MV_SYSTEM;
+ means->dep_exclude = MV_SYSTEM;
+ }
+ else if (lex_match_id (lexer, "DEPENDENT"))
+ /*
+ Use the command "/MISSING=DEPENDENT" to
+ include user-missing values for the categorical variables,
+ while excluding them for the dependent variables.
+
+ Cases are dropped only when user-missing values
+ appear in dependent variables. User-missing
+ values for categorical variables are treated according to
+ their face value.
+
+ Cases are ALWAYS dropped when System Missing values appear
+ in the categorical variables.
+ */
+ {
+ means->dep_exclude = MV_ANY;
+ means->ctrl_exclude = MV_SYSTEM;
+ }
+ else
+ {
+ lex_error (lexer, NULL);
+ return false;
+ }
+ }
+ else if (lex_match_id (lexer, "CELLS"))
+ {
+ lex_match (lexer, T_EQUALS);
+
+ /* The default values become overwritten */
+ means->n_statistics = 0;
+ pool_free (means->pool, means->statistics);
+ means->statistics = 0;
+ while (lex_token (lexer) != T_ENDCMD
+ && lex_token (lexer) != T_SLASH)
+ {
+ if (lex_match (lexer, T_ALL))
+ {
+ pool_free (means->pool, means->statistics);
+ means->statistics = pool_calloc (means->pool,
+ n_MEANS_STATISTICS,
+ sizeof (*means->statistics));
+ means->n_statistics = n_MEANS_STATISTICS;
+ int i;
+ for (i = 0; i < n_MEANS_STATISTICS; ++i)
+ {
+ means->statistics[i] = i;
+ }
+ }
+ else if (lex_match_id (lexer, "NONE"))
+ {
+ means->n_statistics = 0;
+ pool_free (means->pool, means->statistics);
+ means->statistics = 0;
+ }
+ else if (lex_match_id (lexer, "DEFAULT"))
+ {
+ pool_free (means->pool, means->statistics);
+ means->statistics = pool_calloc (means->pool,
+ 3,
+ sizeof *means->statistics);
+ means->statistics[0] = MEANS_MEAN;
+ means->statistics[1] = MEANS_N;
+ means->statistics[2] = MEANS_STDDEV;
+ }
+ else
+ {
+ int i;
+ for (i = 0; i < n_MEANS_STATISTICS; ++i)
+ {
+ const struct cell_spec *cs = cell_spec + i;
+ if (lex_match_id (lexer, cs->keyword))
+ {
+ means->statistics
+ = pool_realloc (means->pool,
+ means->statistics,
+ (means->n_statistics + 1)
+ * sizeof (*means->statistics));
+
+ means->statistics[means->n_statistics] = i;
+ means->n_statistics++;
+ break;
+ }
+ }
+
+ if (i >= n_MEANS_STATISTICS)
+ {
+ lex_error (lexer, NULL);
+ return false;
+ }
+ }
+ }
+ }
+ else
+ {
+ lex_error (lexer, NULL);
+ return false;
+ }
+ }
+ return true;
+}
+
+
+int
+cmd_means (struct lexer *lexer, struct dataset *ds)
+{
+ struct means means;
+ means.pool = pool_create ();
+
+ means.ctrl_exclude = MV_ANY;
+ means.dep_exclude = MV_ANY;
+ means.table = NULL;
+ means.n_tables = 0;
+
+ means.dict = dataset_dict (ds);
+
+ means.n_statistics = 3;
+ means.statistics = pool_calloc (means.pool, 3, sizeof *means.statistics);
+ means.statistics[0] = MEANS_MEAN;
+ means.statistics[1] = MEANS_N;
+ means.statistics[2] = MEANS_STDDEV;
+
+ if (! means_parse (lexer, &means))
+ goto error;
+
+ {
+ struct casegrouper *grouper;
+ struct casereader *group;
+ bool ok;
+
+ grouper = casegrouper_create_splits (proc_open (ds), means.dict);
+ while (casegrouper_get_next_group (grouper, &group))
+ {
+ run_means (&means, group, ds);
+ }
+ ok = casegrouper_destroy (grouper);
+ ok = proc_commit (ds) && ok;
+ }
+
+ for (int t = 0; t < means.n_tables; ++t)
+ {
+ const struct mtable *table = means.table + t;
+
+ means_case_processing_summary (table);
+ means_shipout (table, &means);
+ }
+ destroy_means (&means);
+ pool_destroy (means.pool);
+ return CMD_SUCCESS;
+
+ error:
+
+ destroy_means (&means);
+ pool_destroy (means.pool);
+ return CMD_FAILURE;
+}
/* PSPP - a program for statistical analysis.
- Copyright (C) 2011, 2012, 2013 Free Software Foundation, Inc.
+ Copyright (C) 2019 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 "data/format.h"
#include "data/variable.h"
-#include "language/command.h"
-#include "language/lexer/lexer.h"
-#include "language/lexer/variable-parser.h"
+#include "libpspp/hmap.h"
+#include "libpspp/bt.h"
+#include "libpspp/hash-functions.h"
-#include "libpspp/misc.h"
-#include "libpspp/pool.h"
-
-#include "math/categoricals.h"
-#include "math/interaction.h"
-#include "math/moments.h"
+#include "count-one-bits.h"
+#include "count-leading-zeros.h"
#include "output/pivot-table.h"
-#include <math.h>
+#include "means.h"
+
#include "gettext.h"
#define _(msgid) gettext (msgid)
#define N_(msgid) (msgid)
-struct means;
+/* A "cell" in this procedure represents a distinct value of the
+ procedure's categorical variables, and a set of summary statistics
+ of all cases which whose categorical variables have that set of
+ values. For example, the dataset
-struct per_var_data
-{
- void **cell_stats;
- struct moments1 *mom;
-};
+ v1 v2 cat1 cat2
+ 100 202 0 1
+ 100 202 0 2
+ 100 202 1 0
+ 100 202 0 1
-typedef void *stat_create (struct pool *pool);
-typedef void stat_update (void *stat, double w, double x);
-typedef double stat_get (const struct per_var_data *, void *aux);
+ has three cells in layer 0 and two cells in layer 1 in addition
+ to a "grand summary" cell to which all (non-missing) cases
+ contribute.
-struct cell_spec
+ The cells form a n-ary tree structure with the "grand summary"
+ cell at the root.
+*/
+struct cell
{
- /* Printable title for output */
- const char *title;
-
- /* Keyword for syntax */
- const char *keyword;
-
- stat_create *sc;
- stat_update *su;
- stat_get *sd;
-};
-
-struct harmonic_mean
-{
- double rsum;
- double n;
-};
-
-static void *
-harmonic_create (struct pool *pool)
-{
- struct harmonic_mean *hm = pool_alloc (pool, sizeof *hm);
-
- hm->rsum = 0;
- hm->n = 0;
+ struct hmap_node hmap_node; /* Element in hash table. */
+ struct bt_node bt_node; /* Element in binary tree */
- return hm;
-}
+ int n_children;
+ struct cell_container *children;
+ /* The statistics to be calculated for the cell. */
+ struct statistic **stat;
-static void
-harmonic_update (void *stat, double w, double x)
-{
- struct harmonic_mean *hm = stat;
- hm->rsum += w / x;
- hm->n += w;
-}
+ /* The parent of this cell, or NULL if this is the root cell. */
+ const struct cell *parent_cell;
+ /* A bit-field variable which indicates which control variables
+ are allocated a fixed value (for this cell), and which are
+ "wildcards".
-static double
-harmonic_get (const struct per_var_data *pvd UNUSED, void *stat)
-{
- struct harmonic_mean *hm = stat;
-
- return hm->n / hm->rsum;
-}
+ A one indicates a fixed value. A zero indicates a wildcard.
+ Wildcard values are used to calculate totals and sub-totals.
+ */
+ unsigned int not_wild;
-\f
+ /* The value(s). */
+ union value *values;
-struct geometric_mean
-{
- double prod;
- double n;
+ /* The variables corresponding to the above values. */
+ const struct variable **vars;
};
-
-static void *
-geometric_create (struct pool *pool)
+/* A structure used to find the union of all values used
+ within a layer, and to sort those values. */
+struct instance
{
- struct geometric_mean *gm = pool_alloc (pool, sizeof *gm);
+ struct hmap_node hmap_node; /* Element in hash table. */
+ struct bt_node bt_node; /* Element in binary tree */
- gm->prod = 1.0;
- gm->n = 0;
+ /* A unique, consecutive, zero based index identifying this
+ instance. */
+ int index;
- return gm;
-}
+ /* The top level value of this instance. */
+ union value value;
+ const struct variable *var;
+};
static void
-geometric_update (void *stat, double w, double x)
+destroy_workspace (const struct mtable *mt, struct workspace *ws)
{
- struct geometric_mean *gm = stat;
- gm->prod *= pow (x, w);
- gm->n += w;
+ for (int l = 0; l < mt->n_layers; ++l)
+ {
+ struct cell_container *instances = ws->instances + l;
+ struct instance *inst;
+ struct instance *next;
+ HMAP_FOR_EACH_SAFE (inst, next, struct instance, hmap_node,
+ &instances->map)
+ {
+ free (inst);
+ }
+ hmap_destroy (&instances->map);
+ }
+ free (ws->control_idx);
+ free (ws->instances);
}
-
-static double
-geometric_get (const struct per_var_data *pvd UNUSED, void *stat)
+/* Destroy CELL. */
+static void
+destroy_cell (const struct means *means,
+ const struct mtable *mt, struct cell *cell)
{
- struct geometric_mean *gm = stat;
-
- return pow (gm->prod, 1.0 / gm->n);
-}
+ int idx = 0;
+ for (int i = 0; i < mt->n_layers; ++i)
+ {
+ if (0 == ((cell->not_wild >> i) & 0x1))
+ continue;
-\f
+ const struct layer *layer = mt->layers[i];
+ for (int cmb = 0; cmb < mt->n_combinations; ++cmb)
+ {
+ struct workspace *ws = mt->ws + cmb;
+ const struct variable *var
+ = layer->factor_vars[ws->control_idx[i]];
-static double
-sum_get (const struct per_var_data *pvd, void *stat UNUSED)
-{
- double n, mean;
+ int width = var_get_width (var);
+ value_destroy (&cell->values[idx++], width);
+ }
+ }
+ for (int i = 0; i < cell->n_children; ++i)
+ {
+ struct cell_container *container = cell->children + i;
+ hmap_destroy (&container->map);
+ }
- moments1_calculate (pvd->mom, &n, &mean, 0, 0, 0);
+ for (int v = 0; v < mt->n_dep_vars; ++v)
+ {
+ for (int s = 0; s < means->n_statistics; ++s)
+ {
+ stat_destroy *des = cell_spec[means->statistics[s]].sf;
+ des (cell->stat[s + v * means->n_statistics]);
+ }
+ }
+ free (cell->stat);
- return mean * n;
+ free (cell->children);
+ free (cell->values);
+ free (cell->vars);
+ free (cell);
}
-static double
-n_get (const struct per_var_data *pvd, void *stat UNUSED)
-{
- double n;
-
- moments1_calculate (pvd->mom, &n, 0, 0, 0, 0);
-
- return n;
-}
-
-static double
-arithmean_get (const struct per_var_data *pvd, void *stat UNUSED)
+/* Walk the tree in postorder starting from CELL and destroy all the
+ cells. */
+static void
+means_destroy_cells (const struct means *means, struct cell *cell,
+ const struct mtable *table)
{
- double n, mean;
-
- moments1_calculate (pvd->mom, &n, &mean, 0, 0, 0);
+ for (int i = 0; i < cell->n_children; ++i)
+ {
+ struct cell_container *container = cell->children + i;
+ struct cell *sub_cell;
+ struct cell *next;
+ HMAP_FOR_EACH_SAFE (sub_cell, next, struct cell, hmap_node,
+ &container->map)
+ {
+ means_destroy_cells (means, sub_cell, table);
+ }
+ }
- return mean;
+ destroy_cell (means, table, cell);
}
-static double
-variance_get (const struct per_var_data *pvd, void *stat UNUSED)
+static void
+dump_cell (const struct cell *cell, const struct mtable *mt, int level)
{
- double n, mean, variance;
-
- moments1_calculate (pvd->mom, &n, &mean, &variance, 0, 0);
+ for (int l = 0; l < level; ++l)
+ putchar (' ');
+ printf ("%p: ", cell);
+ for (int i = 0; i < mt->n_layers; ++i)
+ {
+ putchar (((cell->not_wild >> i) & 0x1) ? 'w' : '.');
+ }
+ printf (" - ");
+ int x = 0;
+ for (int i = 0; i < mt->n_layers; ++i)
+ {
+ if ((cell->not_wild >> i) & 0x1)
+ {
+ printf ("%s: ", var_get_name (cell->vars[x]));
+ printf ("%g ", cell->values[x++].f);
+ }
+ else
+ printf ("x ");
+ }
+ stat_get *sg = cell_spec[MEANS_N].sd;
+ printf ("--- S1: %g", sg (cell->stat[0]));
- return variance;
+ printf ("--- N Children: %d", cell->n_children);
+ // printf ("--- Level: %d", level);
+ printf ("--- Parent: %p", cell->parent_cell);
+ printf ("\n");
}
-
-static double
-stddev_get (const struct per_var_data *pvd, void *stat)
+static void
+dump_indeces (const size_t *indexes, int n)
{
- return sqrt (variance_get (pvd, stat));
+ for (int i = 0 ; i < n; ++i)
+ {
+ printf ("%ld; ", indexes[i]);
+ }
+ printf ("\n");
}
-
-\f
-
-static double
-skew_get (const struct per_var_data *pvd, void *stat UNUSED)
+/* Dump the tree in pre-order. */
+static void
+dump_tree (const struct cell *cell, const struct mtable *table,
+ int level, const struct cell *parent)
{
- double skew;
-
- moments1_calculate (pvd->mom, NULL, NULL, NULL, &skew, 0);
+ assert (cell->parent_cell == parent);
+ dump_cell (cell, table, level);
- return skew;
+ for (int i = 0; i < cell->n_children; ++i)
+ {
+ struct cell_container *container = cell->children + i;
+ struct cell *sub_cell;
+ BT_FOR_EACH (sub_cell, struct cell, bt_node, &container->bt)
+ {
+ dump_tree (sub_cell, table, level + 1, cell);
+ }
+ }
}
-static double
-sekurt_get (const struct per_var_data *pvd, void *stat UNUSED)
+/* Generate a hash based on the values of the N variables in
+ the array VARS which are taken from the case C. */
+static unsigned int
+generate_hash (const struct mtable *mt,
+ const struct ccase *c,
+ unsigned int not_wild,
+ const struct workspace *ws)
{
- double n;
-
- moments1_calculate (pvd->mom, &n, NULL, NULL, NULL, NULL);
+ unsigned int hash = 0;
+ for (int i = 0; i < mt->n_layers; ++i)
+ {
+ if (0 == ((not_wild >> i) & 0x1))
+ continue;
+
+ const struct layer *layer = mt->layers[i];
+ const struct variable *var = layer->factor_vars[ws->control_idx[i]];
+ const union value *vv = case_data (c, var);
+ int width = var_get_width (var);
+ hash = hash_int (i, hash);
+ hash = value_hash (vv, width, hash);
+ }
- return calc_sekurt (n);
+ return hash;
}
-static double
-seskew_get (const struct per_var_data *pvd, void *stat UNUSED)
+/* Create a cell based on the N variables in the array VARS,
+ which are indeces into the case C.
+ The caller is responsible for destroying this cell when
+ no longer needed. */
+static struct cell *
+generate_cell (const struct means *means,
+ const struct mtable *mt,
+ const struct ccase *c,
+ unsigned int not_wild,
+ const struct cell *pcell,
+ const struct workspace *ws)
{
- double n;
-
- moments1_calculate (pvd->mom, &n, NULL, NULL, NULL, NULL);
-
- return calc_seskew (n);
-}
+ int n_vars = count_one_bits (not_wild);
+ struct cell *cell = xzalloc ((sizeof *cell));
+ cell->values = xcalloc (n_vars, sizeof *cell->values);
+ cell->vars = xcalloc (n_vars, sizeof *cell->vars);
+ cell->not_wild = not_wild;
+
+ cell->parent_cell = pcell;
+ cell->n_children = mt->n_layers -
+ (sizeof (cell->not_wild) * CHAR_BIT) +
+ count_leading_zeros (cell->not_wild);
+
+ int idx = 0;
+ for (int i = 0; i < mt->n_layers; ++i)
+ {
+ if (0 == ((not_wild >> i) & 0x1))
+ continue;
+
+ const struct layer *layer = mt->layers[i];
+ const struct variable *var = layer->factor_vars[ws->control_idx[i]];
+ const union value *vv = case_data (c, var);
+ int width = var_get_width (var);
+ cell->vars[idx] = var;
+ value_clone (&cell->values[idx++], vv, width);
+ }
+ assert (idx == n_vars);
-static double
-kurt_get (const struct per_var_data *pvd, void *stat UNUSED)
-{
- double kurt;
+ cell->children = xcalloc (cell->n_children, sizeof *cell->children);
+ for (int i = 0; i < cell->n_children; ++i)
+ {
+ struct cell_container *container = cell->children + i;
+ hmap_init (&container->map);
+ }
- moments1_calculate (pvd->mom, NULL, NULL, NULL, NULL, &kurt);
+ cell->stat = xcalloc (means->n_statistics * mt->n_dep_vars, sizeof *cell->stat);
+ for (int v = 0; v < mt->n_dep_vars; ++v)
+ {
+ for (int stat = 0; stat < means->n_statistics; ++stat)
+ {
+ stat_create *sc = cell_spec[means->statistics[stat]].sc;
- return kurt;
+ cell->stat[stat + v * means->n_statistics] = sc (means->pool);
+ }
+ }
+ return cell;
}
-static double
-semean_get (const struct per_var_data *pvd, void *stat UNUSED)
-{
- double n, var;
- moments1_calculate (pvd->mom, &n, NULL, &var, NULL, NULL);
+/* If a cell based on the N variables in the array VARS,
+ which are indeces into the case C and whose hash is HASH,
+ exists in HMAP, then return that cell.
+ Otherwise, return NULL. */
+static struct cell *
+lookup_cell (const struct mtable *mt,
+ struct hmap *hmap, unsigned int hash,
+ const struct ccase *c,
+ unsigned int not_wild,
+ const struct workspace *ws)
+{
+ struct cell *cell = NULL;
+ HMAP_FOR_EACH_WITH_HASH (cell, struct cell, hmap_node, hash, hmap)
+ {
+ bool match = true;
+ int idx = 0;
+ if (cell->not_wild != not_wild)
+ continue;
+ for (int i = 0; i < mt->n_layers; ++i)
+ {
+ if (0 == ((cell->not_wild >> i) & 0x1))
+ continue;
- return sqrt (var / n);
+ const struct layer *layer = mt->layers[i];
+ const struct variable *var = layer->factor_vars[ws->control_idx[i]];
+ const union value *vv = case_data (c, var);
+ int width = var_get_width (var);
+ assert (var == cell->vars[idx]);
+ if (!value_equal (vv, &cell->values[idx++], width))
+ {
+ match = false;
+ break;
+ }
+ }
+ if (match)
+ return cell;
+ }
+ return NULL;
}
-\f
-static void *
-min_create (struct pool *pool)
+/* A comparison function used to sort cells in a binary tree.
+ Only the innermost value needs to be compared, because no
+ two cells with similar outer values will appear in the same
+ tree/map. */
+static int
+cell_compare_3way (const struct bt_node *a,
+ const struct bt_node *b,
+ const void *aux UNUSED)
{
- double *r = pool_alloc (pool, sizeof *r);
+ const struct cell *fa = BT_DATA (a, struct cell, bt_node);
+ const struct cell *fb = BT_DATA (b, struct cell, bt_node);
- *r = DBL_MAX;
+ assert (fa->not_wild == fb->not_wild);
+ int vidx = count_one_bits (fa->not_wild) - 1;
+ assert (fa->vars[vidx] == fb->vars[vidx]);
- return r;
+ return value_compare_3way (&fa->values[vidx],
+ &fb->values[vidx],
+ var_get_width (fa->vars[vidx]));
}
-static void
-min_update (void *stat, double w UNUSED, double x)
+/* A comparison function used to sort cells in a binary tree. */
+static int
+compare_instance_3way (const struct bt_node *a,
+ const struct bt_node *b,
+ const void *aux UNUSED)
{
- double *r = stat;
+ const struct instance *fa = BT_DATA (a, struct instance, bt_node);
+ const struct instance *fb = BT_DATA (b, struct instance, bt_node);
- if (x < *r)
- *r = x;
-}
+ assert (fa->var == fb->var);
-static double
-min_get (const struct per_var_data *pvd UNUSED, void *stat)
-{
- double *r = stat;
-
- return *r;
+ return value_compare_3way (&fa->value,
+ &fb->value,
+ var_get_width (fa->var));
}
-static void *
-max_create (struct pool *pool)
-{
- double *r = pool_alloc (pool, sizeof *r);
- *r = -DBL_MAX;
+static void arrange_cells (struct workspace *ws,
+ struct cell *cell, const struct mtable *table);
- return r;
-}
+/* Iterate CONTAINER's map inserting a copy of its elements into
+ CONTAINER's binary tree. Also, for each layer in TABLE, create
+ an instance container, containing the union of all elements in
+ CONTAINER. */
static void
-max_update (void *stat, double w UNUSED, double x)
+arrange_cell (struct workspace *ws, struct cell_container *container,
+ const struct mtable *mt)
{
- double *r = stat;
+ struct bt *bt = &container->bt;
+ struct hmap *map = &container->map;
+ bt_init (bt, cell_compare_3way, NULL);
- if (x > *r)
- *r = x;
-}
-
-static double
-max_get (const struct per_var_data *pvd UNUSED, void *stat)
-{
- double *r = stat;
-
- return *r;
-}
+ struct cell *cell;
+ HMAP_FOR_EACH (cell, struct cell, hmap_node, map)
+ {
+ bt_insert (bt, &cell->bt_node);
-\f
+ int idx = 0;
+ for (int i = 0; i < mt->n_layers; ++i)
+ {
+ if (0 == ((cell->not_wild >> i) & 0x1))
+ continue;
-struct range
-{
- double min;
- double max;
-};
+ struct cell_container *instances = ws->instances + i;
+ const struct variable *var = cell->vars[idx];
+ int width = var_get_width (var);
+ unsigned int hash
+ = value_hash (&cell->values[idx], width, 0);
+
+ struct instance *inst = NULL;
+ struct instance *next = NULL;
+ HMAP_FOR_EACH_WITH_HASH_SAFE (inst, next, struct instance,
+ hmap_node,
+ hash, &instances->map)
+ {
+ assert (cell->vars[idx] == var);
+ if (value_equal (&inst->value,
+ &cell->values[idx],
+ width))
+ {
+ break;
+ }
+ }
-static void *
-range_create (struct pool *pool)
-{
- struct range *r = pool_alloc (pool, sizeof *r);
+ if (!inst)
+ {
+ inst = xzalloc (sizeof *inst);
+ inst->index = -1;
+ inst->var = var;
+ value_clone (&inst->value, &cell->values[idx],
+ width);
+ hmap_insert (&instances->map, &inst->hmap_node, hash);
+ }
- r->min = DBL_MAX;
- r->max = -DBL_MAX;
+ idx++;
+ }
- return r;
+ arrange_cells (ws, cell, mt);
+ }
}
+/* Arrange the children and then all the subtotals. */
static void
-range_update (void *stat, double w UNUSED, double x)
+arrange_cells (struct workspace *ws, struct cell *cell,
+ const struct mtable *table)
{
- struct range *r = stat;
-
- if (x > r->max)
- r->max = x;
-
- if (x < r->min)
- r->min = x;
+ for (int i = 0; i < cell->n_children; ++i)
+ {
+ struct cell_container *container = cell->children + i;
+ arrange_cell (ws, container, table);
+ }
}
-static double
-range_get (const struct per_var_data *pvd UNUSED, void *stat)
-{
- struct range *r = stat;
-
- return r->max - r->min;
-}
\f
-static void *
-last_create (struct pool *pool)
+/* If the top level value in CELL, has an instance in the L_IDX'th layer,
+ then return that instance. Otherwise return NULL. */
+static const struct instance *
+lookup_instance (const struct mtable *mt, const struct workspace *ws,
+ int l_idx, const struct cell *cell)
{
- double *l = pool_alloc (pool, sizeof *l);
-
- return l;
+ const struct layer *layer = mt->layers[l_idx];
+ int n_vals = count_one_bits (cell->not_wild);
+ const struct variable *var = layer->factor_vars[ws->control_idx[l_idx]];
+ const union value *val = cell->values + n_vals - 1;
+ int width = var_get_width (var);
+ unsigned int hash = value_hash (val, width, 0);
+ const struct cell_container *instances = ws->instances + l_idx;
+ struct instance *inst = NULL;
+ struct instance *next;
+ HMAP_FOR_EACH_WITH_HASH_SAFE (inst, next,
+ struct instance, hmap_node,
+ hash, &instances->map)
+ {
+ if (value_equal (val, &inst->value, width))
+ break;
+ }
+ return inst;
}
+/* Enter the values into PT. */
static void
-last_update (void *stat, double w UNUSED, double x)
+populate_table (const struct means *means, const struct mtable *mt,
+ const struct workspace *ws,
+ const struct cell *cell,
+ struct pivot_table *pt)
{
- double *l = stat;
+ size_t *indexes = xcalloc (pt->n_dimensions, sizeof *indexes);
+ for (int v = 0; v < mt->n_dep_vars; ++v)
+ {
+ for (int s = 0; s < means->n_statistics; ++s)
+ {
+ int i = 0;
+ if (mt->n_dep_vars > 1)
+ indexes[i++] = v;
+ indexes[i++] = s;
+ int stat = means->statistics[s];
+ stat_get *sg = cell_spec[stat].sd;
+ {
+ const struct cell *pc = cell;
+ for (; i < pt->n_dimensions; ++i)
+ {
+ int l_idx = pt->n_dimensions - i - 1;
+ const struct cell_container *instances = ws->instances + l_idx;
+ if (0 == (cell->not_wild >> l_idx & 0x1U))
+ {
+ indexes [i] = hmap_count (&instances->map);
+ }
+ else
+ {
+ assert (pc);
+ const struct instance *inst
+ = lookup_instance (mt, ws, l_idx, pc);
+ assert (inst);
+ indexes [i] = inst->index;
+ pc = pc->parent_cell;
+ }
+ }
+ }
+
+ int idx = s + v * means->n_statistics;
+ pivot_table_put (pt, indexes, pt->n_dimensions,
+ pivot_value_new_number (sg (cell->stat[idx])));
+ }
+ }
+ free (indexes);
- *l = x;
+ for (int i = 0; i < cell->n_children; ++i)
+ {
+ struct cell_container *container = cell->children + i;
+ struct cell *child = NULL;
+ BT_FOR_EACH (child, struct cell, bt_node, &container->bt)
+ {
+ populate_table (means, mt, ws, child, pt);
+ }
+ }
}
-static double
-last_get (const struct per_var_data *pvd UNUSED, void *stat)
+static void
+create_table_structure (const struct mtable *mt, struct pivot_table *pt,
+ const struct workspace *ws)
{
- double *l = stat;
+ int * lindexes = ws->control_idx;
+ /* The inner layers are situated rightmost in the table.
+ So this iteration is in reverse order. */
+ for (int l = mt->n_layers -1; l >=0 ; --l)
+ {
+ const struct layer *layer = mt->layers[l];
+ const struct cell_container *instances = ws->instances + l;
+ const struct variable *var = layer->factor_vars[lindexes[l]];
+ struct pivot_dimension *dim_layer
+ = pivot_dimension_create (pt, PIVOT_AXIS_ROW,
+ var_to_string (var));
+ dim_layer->root->show_label = true;
+
+ /* Place the values of the control variables as table headings. */
+ {
+ struct instance *inst = NULL;
+ BT_FOR_EACH (inst, struct instance, bt_node, &instances->bt)
+ {
+ struct substring space = SS_LITERAL_INITIALIZER ("\t ");
+ struct string str;
+ ds_init_empty (&str);
+ var_append_value_name (var,
+ &inst->value,
+ &str);
- return *l;
-}
+ ds_ltrim (&str, space);
+ pivot_category_create_leaf (dim_layer->root,
+ pivot_value_new_text (ds_cstr (&str)));
-static void *
-first_create (struct pool *pool)
-{
- double *f = pool_alloc (pool, sizeof *f);
-
- *f = SYSMIS;
+ ds_destroy (&str);
+ }
+ }
- return f;
+ pivot_category_create_leaf (dim_layer->root,
+ pivot_value_new_text ("Total"));
+ }
}
+/* Initialise C_DES with a string describing the control variable
+ relating to MT, LINDEXES. */
static void
-first_update (void *stat, double w UNUSED, double x)
+layers_to_string (const struct mtable *mt, const int *lindexes,
+ struct string *c_des)
{
- double *f = stat;
-
- if (*f == SYSMIS)
- *f = x;
+ for (int l = 0; l < mt->n_layers; ++l)
+ {
+ const struct layer *layer = mt->layers[l];
+ const struct variable *ctrl_var = layer->factor_vars[lindexes[l]];
+ if (l > 0)
+ ds_put_cstr (c_des, " * ");
+ ds_put_cstr (c_des, var_get_name (ctrl_var));
+ }
}
-static double
-first_get (const struct per_var_data *pvd UNUSED, void *stat)
+static void
+populate_case_processing_summary (struct pivot_category *pc,
+ const struct mtable *mt,
+ const int *lindexes)
{
- double *f = stat;
+ struct string ds;
+ ds_init_empty (&ds);
+ int l = 0;
+ for (l = 0; l < mt->n_layers; ++l)
+ {
+ const struct layer *layer = mt->layers[l];
+ const struct variable *ctrl_var = layer->factor_vars[lindexes[l]];
+ if (l > 0)
+ ds_put_cstr (&ds, " * ");
+ ds_put_cstr (&ds, var_get_name (ctrl_var));
+ }
+ for (int dv = 0; dv < mt->n_dep_vars; ++dv)
+ {
+ struct string dss;
+ ds_init_empty (&dss);
+ ds_put_cstr (&dss, var_get_name (mt->dep_vars[dv]));
+ if (mt->n_layers > 0)
+ {
+ ds_put_cstr (&dss, " * ");
+ ds_put_substring (&dss, ds.ss);
+ }
+ pivot_category_create_leaf (pc,
+ pivot_value_new_text (ds_cstr (&dss)));
+ ds_destroy (&dss);
+ }
- return *f;
+ ds_destroy (&ds);
}
-enum
- {
- MEANS_MEAN = 0,
- MEANS_N,
- MEANS_STDDEV
- };
-
-/* Table of cell_specs */
-static const struct cell_spec cell_spec[] = {
- {N_("Mean"), "MEAN", NULL, NULL, arithmean_get},
- {N_("N"), "COUNT", NULL, NULL, n_get},
- {N_("Std. Deviation"), "STDDEV", NULL, NULL, stddev_get},
-#if 0
- {N_("Median"), "MEDIAN", NULL, NULL, NULL},
- {N_("Group Median"), "GMEDIAN", NULL, NULL, NULL},
-#endif
- {N_("S.E. Mean"), "SEMEAN", NULL, NULL, semean_get},
- {N_("Sum"), "SUM", NULL, NULL, sum_get},
- {N_("Min"), "MIN", min_create, min_update, min_get},
- {N_("Max"), "MAX", max_create, max_update, max_get},
- {N_("Range"), "RANGE", range_create, range_update, range_get},
- {N_("Variance"), "VARIANCE", NULL, NULL, variance_get},
- {N_("Kurtosis"), "KURT", NULL, NULL, kurt_get},
- {N_("S.E. Kurt"), "SEKURT", NULL, NULL, sekurt_get},
- {N_("Skewness"), "SKEW", NULL, NULL, skew_get},
- {N_("S.E. Skew"), "SESKEW", NULL, NULL, seskew_get},
- {N_("First"), "FIRST", first_create, first_update, first_get},
- {N_("Last"), "LAST", last_create, last_update, last_get},
-#if 0
- {N_("Percent N"), "NPCT", NULL, NULL, NULL},
- {N_("Percent Sum"), "SPCT", NULL, NULL, NULL},
-#endif
- {N_("Harmonic Mean"), "HARMONIC", harmonic_create, harmonic_update, harmonic_get},
- {N_("Geom. Mean"), "GEOMETRIC", geometric_create, geometric_update, geometric_get}
-};
-
-#define n_C (sizeof (cell_spec) / sizeof (struct cell_spec))
-
-
-struct summary
+/* Create the "Case Processing Summary" table. */
+void
+means_case_processing_summary (const struct mtable *mt)
{
- casenumber missing;
- casenumber non_missing;
-};
-
-
-struct layer
-{
- size_t n_factor_vars;
- const struct variable **factor_vars;
-};
-
-/* The thing parsed after TABLES= */
-struct mtable
-{
- size_t n_dep_vars;
- const struct variable **dep_vars;
-
- int n_layers;
- struct layer *layers;
-
- struct interaction **interactions;
- struct summary *summary;
-
- int ii;
-
- struct categoricals *cats;
-};
-
-struct means
-{
- const struct dictionary *dict;
-
- struct mtable *table;
- size_t n_tables;
-
- /* Missing value class for categorical variables */
- enum mv_class exclude;
-
- /* Missing value class for dependent variables */
- enum mv_class dep_exclude;
-
- bool listwise_exclude;
-
- /* an array indicating which statistics are to be calculated */
- int *cells;
-
- /* Size of cells */
- int n_cells;
-
- /* Pool on which cell functions may allocate data */
- struct pool *pool;
-};
-
-
-static void
-run_means (struct means *cmd, struct casereader *input,
- const struct dataset *ds);
-
-
-
-static bool
-parse_means_table_syntax (struct lexer *lexer, const struct means *cmd, struct mtable *table)
-{
- table->ii = 0;
- table->n_layers = 0;
- table->layers = NULL;
- table->interactions = NULL;
-
- /* Dependent variable (s) */
- if (!parse_variables_const_pool (lexer, cmd->pool, cmd->dict,
- &table->dep_vars, &table->n_dep_vars,
- PV_NO_DUPLICATE | PV_NUMERIC))
- return false;
-
- /* Factor variable (s) */
- while (lex_match (lexer, T_BY))
+ struct pivot_table *pt = pivot_table_create (N_("Case Processing Summary"));
+
+ struct pivot_dimension *dim_cases =
+ pivot_dimension_create (pt, PIVOT_AXIS_COLUMN, N_("Cases"));
+ dim_cases->root->show_label = true;
+
+ struct pivot_category *cats[3];
+ cats[0] = pivot_category_create_group (dim_cases->root,
+ N_("Included"), NULL);
+ cats[1] = pivot_category_create_group (dim_cases->root,
+ N_("Excluded"), NULL);
+ cats[2] = pivot_category_create_group (dim_cases->root,
+ N_("Total"), NULL);
+ for (int i = 0; i < 3; ++i)
{
- table->n_layers++;
- table->layers =
- pool_realloc (cmd->pool, table->layers,
- sizeof (*table->layers) * table->n_layers);
-
- if (!parse_variables_const_pool
- (lexer, cmd->pool, cmd->dict,
- &table->layers[table->n_layers - 1].factor_vars,
- &table->layers[table->n_layers - 1].n_factor_vars,
- PV_NO_DUPLICATE))
- return false;
+ pivot_category_create_leaf_rc (cats[i],
+ pivot_value_new_text (N_("N")),
+ PIVOT_RC_COUNT);
+ pivot_category_create_leaf_rc (cats[i],
+ pivot_value_new_text (N_("Percent")),
+ PIVOT_RC_PERCENT);
}
- /* There is always at least one layer.
- However the final layer is the total, and not
- normally considered by the user as a
- layer.
- */
+ struct pivot_dimension *rows =
+ pivot_dimension_create (pt, PIVOT_AXIS_ROW, N_("Variables"));
- table->n_layers++;
- table->layers =
- pool_realloc (cmd->pool, table->layers,
- sizeof (*table->layers) * table->n_layers);
- table->layers[table->n_layers - 1].factor_vars = NULL;
- table->layers[table->n_layers - 1].n_factor_vars = 0;
+ for (int cmb = 0; cmb < mt->n_combinations; ++cmb)
+ {
+ const struct workspace *ws = mt->ws + cmb;
+ populate_case_processing_summary (rows->root, mt, ws->control_idx);
+ for (int dv = 0; dv < mt->n_dep_vars; ++dv)
+ {
+ int idx = cmb * mt->n_dep_vars + dv;
+ const struct summary *summ = mt->summ + idx;
+ double n_included = summ->n_total - summ->n_missing;
+ pivot_table_put2 (pt, 5, idx,
+ pivot_value_new_number (100.0 * summ->n_total / summ->n_total));
+ pivot_table_put2 (pt, 4, idx,
+ pivot_value_new_number (summ->n_total));
+
+ pivot_table_put2 (pt, 3, idx,
+ pivot_value_new_number (100.0 * summ->n_missing / summ->n_total));
+ pivot_table_put2 (pt, 2, idx,
+ pivot_value_new_number (summ->n_missing));
+
+ pivot_table_put2 (pt, 1, idx,
+ pivot_value_new_number (100.0 * n_included / summ->n_total));
+ pivot_table_put2 (pt, 0, idx,
+ pivot_value_new_number (n_included));
+ }
+ }
- return true;
+ pivot_table_submit (pt);
}
-/* Match a variable.
- If the match succeeds, the variable will be placed in VAR.
- Returns true if successful */
-static bool
-lex_is_variable (struct lexer *lexer, const struct dictionary *dict,
- int n)
+static void
+means_shipout_single (const struct mtable *mt, const struct means *means,
+ const struct workspace *ws)
{
- const char *tstr;
- if (lex_next_token (lexer, n) != T_ID)
- return false;
+ struct pivot_table *pt = pivot_table_create (N_("Report"));
+ pt->omit_empty = true;
- tstr = lex_next_tokcstr (lexer, n);
+ struct pivot_dimension *dim_cells =
+ pivot_dimension_create (pt, PIVOT_AXIS_COLUMN, N_("Statistics"));
- if (NULL == dict_lookup_var (dict, tstr) )
- return false;
+ /* Set the statistics headings, eg "Mean", "Std. Dev" etc. */
+ for (int i = 0; i < means->n_statistics; ++i)
+ {
+ const struct cell_spec *cs = cell_spec + means->statistics[i];
+ pivot_category_create_leaf_rc
+ (dim_cells->root,
+ pivot_value_new_text (gettext (cs->title)), cs->rc);
+ }
- return true;
+ create_table_structure (mt, pt, ws);
+ populate_table (means, mt, ws, ws->root_cell, pt);
+ pivot_table_submit (pt);
}
-int
-cmd_means (struct lexer *lexer, struct dataset *ds)
+static void
+means_shipout_multivar (const struct mtable *mt, const struct means *means,
+ const struct workspace *ws)
{
- int t;
- int i;
- int l;
- struct means means;
- bool more_tables = true;
-
- means.pool = pool_create ();
-
- means.exclude = MV_ANY;
- means.dep_exclude = MV_ANY;
- means.listwise_exclude = false;
- means.table = NULL;
- means.n_tables = 0;
-
- means.dict = dataset_dict (ds);
-
- means.n_cells = 3;
- means.cells = pool_calloc (means.pool, means.n_cells, sizeof (*means.cells));
+ struct string dss;
+ ds_init_empty (&dss);
+ for (int dv = 0; dv < mt->n_dep_vars; ++dv)
+ {
+ ds_put_cstr (&dss, var_get_name (mt->dep_vars[dv]));
+ if (mt->n_layers > 0)
+ ds_put_cstr (&dss, " * ");
+ }
+ for (int l = 0; l < mt->n_layers; ++l)
+ {
+ const struct layer *layer = mt->layers[l];
+ const struct variable *var = layer->factor_vars[ws->control_idx[l]];
+ ds_put_cstr (&dss, var_get_name (var));
+ if (l < mt->n_layers - 1)
+ ds_put_cstr (&dss, " * ");
+ }
- /* The first three items (MEAN, COUNT, STDDEV) are the default */
- for (i = 0; i < 3; ++i)
- means.cells[i] = i;
+ struct pivot_table *pt = pivot_table_create (ds_cstr (&dss));
+ pt->omit_empty = true;
+ ds_destroy (&dss);
+ struct pivot_dimension *dim_cells =
+ pivot_dimension_create (pt, PIVOT_AXIS_COLUMN, N_("Variables"));
- /* Optional TABLES = */
- if (lex_match_id (lexer, "TABLES"))
+ for (int i = 0; i < mt->n_dep_vars; ++i)
{
- if (! lex_force_match (lexer, T_EQUALS))
- goto error;
+ pivot_category_create_leaf
+ (dim_cells->root,
+ pivot_value_new_variable (mt->dep_vars[i]));
}
+ struct pivot_dimension *dim_stats
+ = pivot_dimension_create (pt, PIVOT_AXIS_ROW,
+ N_ ("Statistics"));
+ dim_stats->root->show_label = false;
- more_tables = true;
- /* Parse the "tables" */
- while (more_tables)
+ for (int i = 0; i < means->n_statistics; ++i)
{
- means.n_tables ++;
- means.table = pool_realloc (means.pool, means.table, means.n_tables * sizeof (*means.table));
-
- if (! parse_means_table_syntax (lexer, &means,
- &means.table[means.n_tables - 1]))
- {
- goto error;
- }
-
- /* Look ahead to see if there are more tables to be parsed */
- more_tables = false;
- if ( T_SLASH == lex_next_token (lexer, 0) )
- {
- if (lex_is_variable (lexer, means.dict, 1) )
- {
- more_tables = true;
- lex_match (lexer, T_SLASH);
- }
- }
+ const struct cell_spec *cs = cell_spec + means->statistics[i];
+ pivot_category_create_leaf_rc
+ (dim_stats->root,
+ pivot_value_new_text (gettext (cs->title)), cs->rc);
}
- /* /MISSING subcommand */
- while (lex_token (lexer) != T_ENDCMD)
- {
- lex_match (lexer, T_SLASH);
+ create_table_structure (mt, pt, ws);
+ populate_table (means, mt, ws, ws->root_cell, pt);
+ pivot_table_submit (pt);
+}
- if (lex_match_id (lexer, "MISSING"))
+void
+means_shipout (const struct mtable *mt, const struct means *means)
+{
+ for (int cmb = 0; cmb < mt->n_combinations; ++cmb)
+ {
+ const struct workspace *ws = mt->ws + cmb;
+ if (ws->root_cell == NULL)
{
- /*
- If no MISSING subcommand is specified, each combination of
- a dependent variable and categorical variables is handled
- separately.
- */
- lex_match (lexer, T_EQUALS);
- if (lex_match_id (lexer, "INCLUDE"))
- {
- /*
- Use the subcommand "/MISSING=INCLUDE" to include user-missing
- values in the analysis.
- */
-
- means.exclude = MV_SYSTEM;
- means.dep_exclude = MV_SYSTEM;
- }
- else if (lex_match_id (lexer, "TABLE"))
- /*
- This is the default. (I think).
- Every case containing a complete set of variables for a given
- table. If any variable, categorical or dependent for in a table
- is missing (as defined by what?), then that variable will
- be dropped FOR THAT TABLE ONLY.
- */
- {
- means.listwise_exclude = true;
- }
- else if (lex_match_id (lexer, "DEPENDENT"))
- /*
- Use the command "/MISSING=DEPENDENT" to
- include user-missing values for the categorical variables,
- while excluding them for the dependent variables.
-
- Cases are dropped only when user-missing values
- appear in dependent variables. User-missing
- values for categorical variables are treated according to
- their face value.
-
- Cases are ALWAYS dropped when System Missing values appear
- in the categorical variables.
- */
- {
- means.dep_exclude = MV_ANY;
- means.exclude = MV_SYSTEM;
- }
- else
- {
- lex_error (lexer, NULL);
- goto error;
- }
- }
- else if (lex_match_id (lexer, "CELLS"))
- {
- lex_match (lexer, T_EQUALS);
-
- /* The default values become overwritten */
- means.n_cells = 0;
- while (lex_token (lexer) != T_ENDCMD
- && lex_token (lexer) != T_SLASH)
- {
- int k = 0;
- if (lex_match (lexer, T_ALL))
- {
- int x;
- means.cells =
- pool_realloc (means.pool, means.cells,
- (means.n_cells += n_C) * sizeof (*means.cells));
-
- for (x = 0; x < n_C; ++x)
- means.cells[means.n_cells - (n_C - 1 - x) - 1] = x;
- }
- else if (lex_match_id (lexer, "NONE"))
- {
- /* Do nothing */
- }
- else if (lex_match_id (lexer, "DEFAULT"))
- {
- means.cells =
- pool_realloc (means.pool, means.cells,
- (means.n_cells += 3) * sizeof (*means.cells));
-
- means.cells[means.n_cells - 2 - 1] = MEANS_MEAN;
- means.cells[means.n_cells - 1 - 1] = MEANS_N;
- means.cells[means.n_cells - 0 - 1] = MEANS_STDDEV;
- }
- else
- {
- for (; k < n_C; ++k)
- {
- if (lex_match_id (lexer, cell_spec[k].keyword))
- {
- means.cells =
- pool_realloc (means.pool, means.cells,
- ++means.n_cells * sizeof (*means.cells));
-
- means.cells[means.n_cells - 1] = k;
- break;
- }
- }
- }
- if (k >= n_C)
- {
- lex_error (lexer, NULL);
- goto error;
- }
- }
+ struct string des;
+ ds_init_empty (&des);
+ layers_to_string (mt, ws->control_idx, &des);
+ msg (MW, _("The table \"%s\" has no non-empty control variables."
+ " No result for this table will be displayed."),
+ ds_cstr (&des));
+ ds_destroy (&des);
+ continue;
}
+ if (mt->n_dep_vars > 1)
+ means_shipout_multivar (mt, means, ws);
else
- {
- lex_error (lexer, NULL);
- goto error;
- }
+ means_shipout_single (mt, means, ws);
}
+}
+\f
- for (t = 0; t < means.n_tables; ++t)
+static bool
+control_var_missing (const struct means *means,
+ const struct mtable *mt,
+ unsigned int not_wild UNUSED,
+ const struct ccase *c,
+ const struct workspace *ws)
+{
+ bool miss = false;
+ for (int l = 0; l < mt->n_layers; ++l)
{
- struct mtable *table = &means.table[t];
-
- table->interactions =
- pool_calloc (means.pool, table->n_layers, sizeof (*table->interactions));
+ /* if (0 == ((not_wild >> l) & 0x1)) */
+ /* { */
+ /* continue; */
+ /* } */
+
+ const struct layer *layer = mt->layers[l];
+ const struct variable *var = layer->factor_vars[ws->control_idx[l]];
+ const union value *vv = case_data (c, var);
+
+ miss = var_is_value_missing (var, vv, means->ctrl_exclude);
+ if (miss)
+ break;
+ }
- table->summary =
- pool_calloc (means.pool, table->n_dep_vars * table->n_layers, sizeof (*table->summary));
+ return miss;
+}
- for (l = 0; l < table->n_layers; ++l)
+/* Lookup the set of control variables described by MT, C and NOT_WILD,
+ in the hash table MAP. If there is no such entry, then create a
+ cell with these paremeters and add is to MAP.
+ If the generated cell has childen, repeat for all the children.
+ Returns the root cell.
+*/
+static struct cell *
+service_cell_map (const struct means *means, const struct mtable *mt,
+ const struct ccase *c,
+ unsigned int not_wild,
+ struct hmap *map,
+ const struct cell *pcell,
+ int level,
+ const struct workspace *ws)
+{
+ struct cell *cell = NULL;
+ if (map)
+ {
+ if (!control_var_missing (means, mt, not_wild, c, ws))
{
- int v;
- const struct layer *lyr = &table->layers[l];
- const int n_vars = lyr->n_factor_vars;
- table->interactions[l] = interaction_create (NULL);
- for (v = 0; v < n_vars ; ++v)
+ /* Lookup this set of values in the cell's hash table. */
+ unsigned int hash = generate_hash (mt, c, not_wild, ws);
+ cell = lookup_cell (mt, map, hash, c, not_wild, ws);
+
+ /* If it has not been seen before, then create a new
+ subcell, with this set of values, and insert it
+ into the table. */
+ if (cell == NULL)
{
- interaction_add_variable (table->interactions[l],
- lyr->factor_vars[v]);
+ cell = generate_cell (means, mt, c, not_wild, pcell, ws);
+ hmap_insert (map, &cell->hmap_node, hash);
}
}
}
-
- {
- struct casegrouper *grouper;
- struct casereader *group;
- bool ok;
-
- grouper = casegrouper_create_splits (proc_open (ds), means.dict);
- while (casegrouper_get_next_group (grouper, &group))
- {
- run_means (&means, group, ds);
- }
- ok = casegrouper_destroy (grouper);
- ok = proc_commit (ds) && ok;
- }
-
- for (t = 0; t < means.n_tables; ++t)
+ else
{
- int l;
- struct mtable *table = &means.table[t];
- if (table->interactions)
- for (l = 0; l < table->n_layers; ++l)
- {
- interaction_destroy (table->interactions[l]);
- }
+ /* This condition should only happen in the root node case. */
+ cell = ws->root_cell;
+ if (cell == NULL &&
+ !control_var_missing (means, mt, not_wild, c, ws))
+ cell = generate_cell (means, mt, c, not_wild, pcell, ws);
}
- pool_destroy (means.pool);
- return CMD_SUCCESS;
-
- error:
-
- for (t = 0; t < means.n_tables; ++t)
+ if (cell)
{
- int l;
- struct mtable *table = &means.table[t];
- if (table->interactions)
- for (l = 0; l < table->n_layers; ++l)
- {
- interaction_destroy (table->interactions[l]);
- }
- }
-
- pool_destroy (means.pool);
- return CMD_FAILURE;
-}
-
-
-static bool
-is_missing (const struct means *cmd,
- const struct variable *dvar,
- const struct interaction *iact,
- const struct ccase *c)
-{
- if ( interaction_case_is_missing (iact, c, cmd->exclude) )
- return true;
-
+ /* Here is where the business really happens! After
+ testing for missing values, the cell's statistics
+ are accumulated. */
+ if (!control_var_missing (means, mt, not_wild, c, ws))
+ {
+ for (int v = 0; v < mt->n_dep_vars; ++v)
+ {
+ const struct variable *dep_var = mt->dep_vars[v];
+ const union value *vv = case_data (c, dep_var);
+ if (var_is_value_missing (dep_var, vv, means->dep_exclude))
+ continue;
+
+ for (int stat = 0; stat < means->n_statistics; ++stat)
+ {
+ const double weight = dict_get_case_weight (means->dict, c,
+ NULL);
+ stat_update *su = cell_spec[means->statistics[stat]].su;
+ su (cell->stat[stat + v * means->n_statistics], weight,
+ case_data (c, dep_var)->f);
+ }
+ }
+ }
- if (var_is_value_missing (dvar,
- case_data (c, dvar),
- cmd->dep_exclude))
- return true;
+ /* Recurse into all the children (if there are any). */
+ for (int i = 0; i < cell->n_children; ++i)
+ {
+ struct cell_container *cc = cell->children + i;
+ service_cell_map (means, mt, c,
+ not_wild | (0x1U << (i + level)),
+ &cc->map, cell, level + i + 1, ws);
+ }
+ }
- return false;
+ return cell;
}
-static void output_case_processing_summary (const struct mtable *,
- const struct variable *wv);
-
-static void output_report (const struct means *, int, const struct mtable *);
-
-
-struct per_cat_data
-{
- struct per_var_data *pvd;
-
- bool warn;
-};
-
-
+/* Do all the necessary preparation and pre-calculation that
+ needs to be done before iterating the data. */
static void
-destroy_n (const void *aux1 UNUSED, void *aux2, void *user_data)
-{
- struct mtable *table = aux2;
- int v;
- struct per_cat_data *per_cat_data = user_data;
- struct per_var_data *pvd = per_cat_data->pvd;
-
- for (v = 0; v < table->n_dep_vars; ++v)
- {
- struct per_var_data *pp = &pvd[v];
- moments1_destroy (pp->mom);
- }
-}
-
-static void *
-create_n (const void *aux1, void *aux2)
+prepare_means (struct means *cmd)
{
- int i, v;
- const struct means *means = aux1;
- struct mtable *table = aux2;
- struct per_cat_data *per_cat_data = pool_malloc (means->pool, sizeof *per_cat_data);
-
- struct per_var_data *pvd = pool_calloc (means->pool, table->n_dep_vars, sizeof *pvd);
-
- for (v = 0; v < table->n_dep_vars; ++v)
+ for (int t = 0; t < cmd->n_tables; ++t)
{
- enum moment maxmom = MOMENT_KURTOSIS;
- struct per_var_data *pp = &pvd[v];
-
- pp->cell_stats = pool_calloc (means->pool, means->n_cells, sizeof *pp->cell_stats);
+ struct mtable *mt = cmd->table + t;
+ mt->n_combinations = 1;
+ for (int l = 0; l < mt->n_layers; ++l)
+ mt->n_combinations *= mt->layers[l]->n_factor_vars;
- for (i = 0; i < means->n_cells; ++i)
- {
- int csi = means->cells[i];
- const struct cell_spec *cs = &cell_spec[csi];
- if (cs->sc)
- {
- pp->cell_stats[i] = cs->sc (means->pool);
- }
- }
- pp->mom = moments1_create (maxmom);
+ mt->ws = xzalloc (mt->n_combinations * sizeof (*mt->ws));
+ mt->summ = xzalloc (mt->n_combinations * mt->n_dep_vars
+ * sizeof (*mt->summ));
+ for (int i = 0; i < mt->n_combinations; ++i)
+ {
+ struct workspace *ws = mt->ws + i;
+ ws->root_cell = NULL;
+ ws->control_idx = xzalloc (mt->n_layers
+ * sizeof *ws->control_idx);
+ ws->instances = xzalloc (mt->n_layers
+ * sizeof *ws->instances);
+ int cmb = i;
+ for (int l = mt->n_layers - 1; l >= 0; --l)
+ {
+ struct cell_container *instances = ws->instances + l;
+ const struct layer *layer = mt->layers[l];
+ ws->control_idx[l] = cmb % layer->n_factor_vars;
+ cmb /= layer->n_factor_vars;
+ hmap_init (&instances->map);
+ }
+ }
}
-
-
- per_cat_data->pvd = pvd;
- per_cat_data->warn = true;
- return per_cat_data;
}
+
+/* Do all the necessary calculations that occur AFTER iterating
+ the data. */
static void
-update_n (const void *aux1, void *aux2, void *user_data, const struct ccase *c, double weight)
+post_means (struct means *cmd)
{
- int i;
- int v = 0;
- const struct means *means = aux1;
- struct mtable *table = aux2;
- struct per_cat_data *per_cat_data = user_data;
-
- for (v = 0; v < table->n_dep_vars; ++v)
+ for (int t = 0; t < cmd->n_tables; ++t)
{
- struct per_var_data *pvd = &per_cat_data->pvd[v];
-
- const double x = case_data (c, table->dep_vars[v])->f;
-
- for (i = 0; i < table->n_layers; ++i)
+ struct mtable *mt = cmd->table + t;
+ for (int cmb = 0; cmb < mt->n_combinations; ++cmb)
{
- if ( is_missing (means, table->dep_vars[v],
- table->interactions[i], c))
- goto end;
- }
-
- for (i = 0; i < means->n_cells; ++i)
- {
- const int csi = means->cells[i];
- const struct cell_spec *cs = &cell_spec[csi];
+ struct workspace *ws = mt->ws + cmb;
+ if (ws->root_cell == NULL)
+ continue;
+ arrange_cells (ws, ws->root_cell, mt);
+ /* The root cell should have no parent. */
+ assert (ws->root_cell->parent_cell == 0);
+ for (int l = 0; l < mt->n_layers; ++l)
+ {
+ struct cell_container *instances = ws->instances + l;
+ bt_init (&instances->bt, compare_instance_3way, NULL);
+
+ /* Iterate the instance hash table, and insert each instance
+ into the binary tree BT. */
+ struct instance *inst;
+ HMAP_FOR_EACH (inst, struct instance, hmap_node,
+ &instances->map)
+ {
+ bt_insert (&instances->bt, &inst->bt_node);
+ }
- if (cs->su)
- cs->su (pvd->cell_stats[i],
- weight, x);
+ /* Iterate the binary tree (in order) and assign the index
+ member accordingly. */
+ int index = 0;
+ BT_FOR_EACH (inst, struct instance, bt_node, &instances->bt)
+ {
+ inst->index = index++;
+ }
+ }
}
-
- moments1_add (pvd->mom, x, weight);
-
- end:
- continue;
}
}
+
+/* Update the summary information (the missings and the totals). */
static void
-calculate_n (const void *aux1, void *aux2, void *user_data)
+update_summaries (const struct means *means, struct mtable *mt,
+ const struct ccase *c, double weight)
{
- int i;
- int v = 0;
- struct per_cat_data *per_cat_data = user_data;
- const struct means *means = aux1;
- struct mtable *table = aux2;
-
- for (v = 0; v < table->n_dep_vars; ++v)
+ for (int dv = 0; dv < mt->n_dep_vars; ++dv)
{
- struct per_var_data *pvd = &per_cat_data->pvd[v];
- for (i = 0; i < means->n_cells; ++i)
+ for (int cmb = 0; cmb < mt->n_combinations; ++cmb)
{
- int csi = means->cells[i];
- const struct cell_spec *cs = &cell_spec[csi];
-
- if (cs->su)
- cs->sd (pvd, pvd->cell_stats[i]);
+ struct workspace *ws = mt->ws + cmb;
+ struct summary *summ = mt->summ
+ + cmb * mt->n_dep_vars + dv;
+
+ summ->n_total += weight;
+ const struct variable *var = mt->dep_vars[dv];
+ const union value *vv = case_data (c, var);
+ /* First check if the dependent variable is missing. */
+ if (var_is_value_missing (var, vv, means->dep_exclude))
+ summ->n_missing += weight;
+ /* If the dep var is not missing, then check each
+ control variable. */
+ else
+ for (int l = 0; l < mt->n_layers; ++l)
+ {
+ const struct layer *layer = mt->layers [l];
+ const struct variable *var
+ = layer->factor_vars[ws->control_idx[l]];
+ const union value *vv = case_data (c, var);
+ if (var_is_value_missing (var, vv, means->ctrl_exclude))
+ {
+ summ->n_missing += weight;
+ break;
+ }
+ }
}
}
}
-static void
+
+void
run_means (struct means *cmd, struct casereader *input,
const struct dataset *ds UNUSED)
{
- int t;
- const struct variable *wv = dict_get_weight (cmd->dict);
- struct ccase *c;
+ struct ccase *c = NULL;
struct casereader *reader;
- struct payload payload;
- payload.create = create_n;
- payload.update = update_n;
- payload.calculate = calculate_n;
- payload.destroy = destroy_n;
-
- for (t = 0; t < cmd->n_tables; ++t)
- {
- struct mtable *table = &cmd->table[t];
- table->cats
- = categoricals_create (table->interactions,
- table->n_layers, wv, cmd->exclude);
-
- categoricals_set_payload (table->cats, &payload, cmd, table);
- }
+ prepare_means (cmd);
for (reader = input;
(c = casereader_read (reader)) != NULL; case_unref (c))
{
- for (t = 0; t < cmd->n_tables; ++t)
+ const double weight
+ = dict_get_case_weight (cmd->dict, c, NULL);
+ for (int t = 0; t < cmd->n_tables; ++t)
{
- bool something_missing = false;
- int v;
- struct mtable *table = &cmd->table[t];
+ struct mtable *mt = cmd->table + t;
+ update_summaries (cmd, mt, c, weight);
- for (v = 0; v < table->n_dep_vars; ++v)
+ for (int cmb = 0; cmb < mt->n_combinations; ++cmb)
{
- int i;
- for (i = 0; i < table->n_layers; ++i)
- {
- const bool missing =
- is_missing (cmd, table->dep_vars[v],
- table->interactions[i], c);
- if (missing)
- {
- something_missing = true;
- table->summary[v * table->n_layers + i].missing++;
- }
- else
- table->summary[v * table->n_layers + i].non_missing++;
- }
- }
- if ( something_missing && cmd->listwise_exclude)
- continue;
+ struct workspace *ws = mt->ws + cmb;
- categoricals_update (table->cats, c);
+ ws->root_cell = service_cell_map (cmd, mt, c,
+ 0U, NULL, NULL, 0, ws);
+ }
}
}
casereader_destroy (reader);
- for (t = 0; t < cmd->n_tables; ++t)
- {
- struct mtable *table = &cmd->table[t];
-
- categoricals_done (table->cats);
- }
-
-
- for (t = 0; t < cmd->n_tables; ++t)
- {
- int i;
- const struct mtable *table = &cmd->table[t];
-
- output_case_processing_summary (table, wv);
-
- for (i = 0; i < table->n_layers; ++i)
- {
- output_report (cmd, i, table);
- }
- categoricals_destroy (table->cats);
- }
-
+ post_means (cmd);
}
-
-static void
-output_case_processing_summary (const struct mtable *mt,
- const struct variable *wv)
+/* Release all resources allocated by this routine.
+ This does not include those allocated by the parser,
+ which exclusively use MEANS->pool. */
+void
+destroy_means (struct means *means)
{
- struct pivot_table *table = pivot_table_create (
- N_("Case Processing Summary"));
- pivot_table_set_weight_var (table, wv);
-
- pivot_dimension_create (table, PIVOT_AXIS_COLUMN, N_("Statistics"),
- N_("N"), PIVOT_RC_COUNT,
- N_("Percent"), PIVOT_RC_PERCENT);
- pivot_dimension_create (table, PIVOT_AXIS_COLUMN, N_("Cases"),
- N_("Included"), N_("Excluded"), N_("Total"))
- ->root->show_label = true;
-
- struct pivot_dimension *tables = pivot_dimension_create (
- table, PIVOT_AXIS_ROW, N_("Tables"));
-
- for (size_t v = 0; v < mt->n_dep_vars; ++v)
+ for (int t = 0; t < means->n_tables; ++t)
{
- const struct variable *var = mt->dep_vars[v];
- for (size_t i = 0; i < mt->n_layers; ++i)
- {
- const int row = v * mt->n_layers + i;
- const struct interaction *iact = mt->interactions[i];
-
- struct string str = DS_EMPTY_INITIALIZER;
- ds_put_format (&str, "%s: ", var_to_string (var));
- interaction_to_string (iact, &str);
- int table_idx = pivot_category_create_leaf (
- tables->root, pivot_value_new_user_text_nocopy (
- ds_steal_cstr (&str)));
-
- const struct summary *s = &mt->summary[row];
- double n_total = s->missing + s->non_missing;
- struct entry
- {
- int stat_idx;
- int case_idx;
- double x;
- }
- entries[] =
- {
- { 0, 0, s->non_missing },
- { 1, 0, s->non_missing / n_total * 100.0 },
- { 0, 1, s->missing },
- { 1, 1, s->missing / n_total * 100.0 },
- { 0, 2, n_total },
- { 1, 2, 100.0 },
- };
-
- for (size_t j = 0; j < sizeof entries / sizeof *entries; j++)
- {
- const struct entry *e = &entries[j];
- pivot_table_put3 (table, e->stat_idx, e->case_idx, table_idx,
- pivot_value_new_number (e->x));
- }
+ const struct mtable *table = means->table + t;
+ for (int i = 0; i < table->n_combinations; ++i)
+ {
+ struct workspace *ws = table->ws + i;
+ if (ws->root_cell == NULL)
+ continue;
+ means_destroy_cells (means, ws->root_cell, table);
}
- }
-
- pivot_table_submit (table);
-}
-
-static void
-create_interaction_dimensions (struct pivot_table *table,
- const struct categoricals *cats,
- const struct interaction *iact)
-{
- for (size_t i = iact->n_vars; i-- > 0; )
- {
- const struct variable *var = iact->vars[i];
- struct pivot_dimension *d = pivot_dimension_create__ (
- table, PIVOT_AXIS_ROW, pivot_value_new_variable (var));
- d->root->show_label = true;
-
- size_t n;
- union value *values = categoricals_get_var_values (cats, var, &n);
- for (size_t j = 0; j < n; j++)
- pivot_category_create_leaf (
- d->root, pivot_value_new_var_value (var, &values[j]));
- }
-}
-
-static void
-output_report (const struct means *cmd, int iact_idx,
- const struct mtable *mt)
-{
- struct pivot_table *table = pivot_table_create (N_("Report"));
- table->omit_empty = true;
-
- struct pivot_dimension *statistics = pivot_dimension_create (
- table, PIVOT_AXIS_COLUMN, N_("Statistics"));
- for (int i = 0; i < cmd->n_cells; ++i)
- pivot_category_create_leaf (
- statistics->root, pivot_value_new_text (cell_spec[cmd->cells[i]].title));
-
- const struct interaction *iact = mt->interactions[iact_idx];
- create_interaction_dimensions (table, mt->cats, iact);
-
- struct pivot_dimension *dep_dim = pivot_dimension_create (
- table, PIVOT_AXIS_ROW, N_("Dependent Variables"));
-
- size_t *indexes = xnmalloc (table->n_dimensions, sizeof *indexes);
-
- size_t n_cats = categoricals_n_count (mt->cats, iact_idx);
- for (size_t v = 0; v < mt->n_dep_vars; ++v)
- {
- indexes[table->n_dimensions - 1] = pivot_category_create_leaf (
- dep_dim->root, pivot_value_new_variable (mt->dep_vars[v]));
-
- for (size_t i = 0; i < n_cats; ++i)
+ for (int i = 0; i < table->n_combinations; ++i)
{
- for (size_t j = 0; j < iact->n_vars; j++)
- {
- int idx = categoricals_get_value_index_by_category_real (
- mt->cats, iact_idx, i, j);
- indexes[table->n_dimensions - 2 - j] = idx;
- }
-
- struct per_cat_data *per_cat_data
- = categoricals_get_user_data_by_category_real (
- mt->cats, iact_idx, i);
-
- const struct per_var_data *pvd = &per_cat_data->pvd[v];
- for (int stat_idx = 0; stat_idx < cmd->n_cells; ++stat_idx)
- {
- indexes[0] = stat_idx;
- const int csi = cmd->cells[stat_idx];
- const struct cell_spec *cs = &cell_spec[csi];
-
- double result = cs->sd (pvd, pvd->cell_stats[stat_idx]);
- pivot_table_put (table, indexes, table->n_dimensions,
- pivot_value_new_number (result));
- }
- }
+ struct workspace *ws = table->ws + i;
+ destroy_workspace (table, ws);
+ }
+ free (table->ws);
+ free (table->summ);
}
- free (indexes);
-
- pivot_table_submit (table);
}
-
--- /dev/null
+/* PSPP - a program for statistical analysis.
+ Copyright (C) 2011, 2012, 2013, 2019 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
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see <http://www.gnu.org/licenses/>. */
+
+#ifndef MEANS_H
+#define MEANS_H
+
+#include "libpspp/compiler.h"
+
+struct cell_container
+{
+ /* A hash table containing the cells. The table is indexed by a hash
+ based on the cell's categorical value. */
+ struct hmap map;
+
+ /* A binary tree containing the cells. This is
+ used to sort the elements in order of their categorical
+ values. */
+ struct bt bt;
+};
+
+
+
+struct layer
+{
+ size_t n_factor_vars;
+ const struct variable **factor_vars;
+};
+
+
+struct statistic;
+
+typedef struct statistic *stat_create (struct pool *pool);
+typedef void stat_update (struct statistic *stat, double w, double x);
+typedef double stat_get (const struct statistic *);
+typedef void stat_destroy (struct statistic *);
+
+
+struct cell_spec
+{
+ /* Printable title for output */
+ const char *title;
+
+ /* Keyword for syntax */
+ const char *keyword;
+
+ /* The result class for the datum. */
+ const char *rc;
+
+ stat_create *sc;
+ stat_update *su;
+ stat_get *sd;
+ stat_destroy *sf;
+};
+
+struct summary
+{
+ double n_total;
+ double n_missing;
+};
+
+/* Intermediate data per table. */
+struct workspace
+{
+ /* An array of n_layers integers which are used
+ to permute access into the factor_vars of each layer. */
+ int *control_idx;
+
+ /* An array of n_layers cell_containers which hold the union
+ of instances used respectively by each layer. */
+ struct cell_container *instances;
+
+ struct cell *root_cell;
+};
+
+/* The thing parsed after TABLES= */
+struct mtable
+{
+ size_t n_dep_vars;
+ const struct variable **dep_vars;
+
+ struct layer **layers;
+ int n_layers;
+
+ int n_combinations;
+
+ /* An array of n_combinations workspaces. */
+ struct workspace *ws;
+
+ /* An array of n_combinations * n_dep_vars summaries.
+ These are displayed in the Case Processing
+ Summary box. */
+ struct summary *summ;
+};
+
+/* A structure created by the parser. Contains the definition of the
+ what the procedure should calculate. */
+struct means
+{
+ const struct dictionary *dict;
+
+ /* The "tables" (ie, a definition of how the data should
+ be broken down). */
+ struct mtable *table;
+ size_t n_tables;
+
+ /* Missing value class for categorical variables. */
+ enum mv_class ctrl_exclude;
+
+ /* Missing value class for dependent variables */
+ enum mv_class dep_exclude;
+
+ /* The statistics to be calculated for each cell. */
+ int *statistics;
+ int n_statistics;
+
+ /* Pool on which cell functions may allocate data. */
+ struct pool *pool;
+};
+
+
+
+#define n_MEANS_STATISTICS 17
+extern const struct cell_spec cell_spec[n_MEANS_STATISTICS];
+
+/* This enum must be consistent with the array cell_spec (in means-calc.c).
+ A bitfield instead of enums would in my opinion be
+ more elegent. However we want the order of the specified
+ statistics to be retained in the output. */
+enum
+ {
+ MEANS_MEAN = 0,
+ MEANS_N,
+ MEANS_STDDEV
+ };
+
+
+
+struct dataset;
+struct casereader;
+void run_means (struct means *cmd, struct casereader *input, const struct dataset *ds UNUSED);
+
+void means_shipout (const struct mtable *mt, const struct means *means);
+
+void means_case_processing_summary (const struct mtable *mt);
+
+
+void destroy_means (struct means *means);
+
+
+#endif
dnl PSPP - a program for statistical analysis.
-dnl Copyright (C) 2017 Free Software Foundation, Inc.
-dnl
+dnl Copyright (C) 2017, 2019 Free Software Foundation, Inc.
+dnl
dnl This program is free software: you can redistribute it and/or modify
dnl it under the terms of the GNU General Public License as published by
dnl the Free Software Foundation, either version 3 of the License, or
dnl (at your option) any later version.
-dnl
+dnl
dnl This program is distributed in the hope that it will be useful,
dnl but WITHOUT ANY WARRANTY; without even the implied warranty of
dnl MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
dnl GNU General Public License for more details.
-dnl
+dnl
dnl You should have received a copy of the GNU General Public License
dnl along with this program. If not, see <http://www.gnu.org/licenses/>.
dnl
AT_BANNER([MEANS procedure])
-AT_SETUP([MEANS simple example])
+AT_SETUP([MEANS simple])
AT_KEYWORDS([categorical categoricals])
AT_DATA([means-simple.sps], [dnl
-SET FORMAT=F12.5.
-
-data list notable list /score * factor *.
-BEGIN DATA.
-22 01
-22 01
-29 01
-16 01
-24 02
-21 02
-22 01
-24 01
-19 01
-17 01
-22 01
-17 02
-23 02
-25 02
-20 01
-15 01
-18 01
-26 01
-23 02
-35 02
-20 01
-16 01
-19 01
-14 01
-14 01
-21 01
-END DATA.
-
-MEANS TABLES = score BY factor.
+data list notable list /hand * score * w *.
+begin data.
+1 17 4
+1 16 5
+2 21 1
+2 22 1
+2 20 8
+end data.
+
+weight by w.
+
+means tables = score by hand
+ /cells = mean count.
])
-AT_CHECK([pspp -O format=csv means-simple.sps], [0],
- [dnl
+AT_CHECK([pspp -O format=csv means-simple.sps], [0], [dnl
Table: Case Processing Summary
,Cases,,,,,
,Included,,Excluded,,Total,
,N,Percent,N,Percent,N,Percent
-score: factor,26,100.0%,0,.0%,26,100.0%
-score: ,26,100.0%,0,.0%,26,100.0%
+score * hand,19,100.0%,0,.0%,19,100.0%
Table: Report
-,factor,Mean,N,Std. Deviation
-score,1.00000,19.78947,19.00000,4.03566
-,2.00000,24.00000,7.00000,5.50757
-
-Table: Report
-,Mean,N,Std. Deviation
-score,20.92308,26.00000,4.75750
+hand,Mean,N
+1.00,16.44,9
+2.00,20.30,10
+Total,18.47,19
])
AT_CLEANUP
-
-
-AT_SETUP([MEANS very simple example])
+AT_SETUP([MEANS very simple])
AT_KEYWORDS([categorical categoricals])
-AT_DATA([means-vsimple.sps], [dnl
-SET FORMAT=F12.5.
-
-data list notable list /score.
+AT_DATA([very-simple.sps], [dnl
+data list notable list /score *.
begin data.
-1
-1
-2
-2
+17
+17
+17
+16
+17
+16
+16
+16
+16
+21
+22
+20
+20
+20
+20
+20
+20
+20
+20
end data.
-means tables = score.
+means tables = score
+ /cells = mean count.
])
-AT_CHECK([pspp -O format=csv means-vsimple.sps], [0],
- [dnl
+AT_CHECK([pspp -O format=csv very-simple.sps], [0], [dnl
Table: Case Processing Summary
,Cases,,,,,
,Included,,Excluded,,Total,
,N,Percent,N,Percent,N,Percent
-score: ,4,100.0%,0,.0%,4,100.0%
+score,19,100.0%,0,.0%,19,100.0%
Table: Report
-,Mean,N,Std. Deviation
-score,1.50000,4.00000,.57735
+Mean,N
+18.47,19
])
AT_CLEANUP
-
-
-AT_SETUP([MEANS default missing])
+AT_SETUP([MEANS empty factor spec])
AT_KEYWORDS([categorical categoricals])
-AT_DATA([means-dmiss.sps], [dnl
-SET FORMAT=F12.2.
-data list notable list /a * g1 * g2 *.
+AT_DATA([means-bad.sps], [dnl
+data list list /outcome *.
begin data.
-3 1 .
-4 1 11
-3 1 21
-6 2 21
-2 2 31
-. 2 31
-8 2 31
-7 2 31
+1
+2
+3
end data.
-MEANS TABLES =
- a BY g1 g2
- BY g2
- /cells = MEAN COUNT
- .
+MEANS TABLES = outcome
+ BY.
])
-AT_CHECK([pspp -O format=csv means-dmiss.sps], [0],
- [dnl
-Table: Case Processing Summary
-,Cases,,,,,
-,Included,,Excluded,,Total,
-,N,Percent,N,Percent,N,Percent
-a: g1 × g2,6,75.0%,2,25.0%,8,100.0%
-a: g2,6,75.0%,2,25.0%,8,100.0%
-a: ,7,87.5%,1,12.5%,8,100.0%
+AT_CHECK([pspp -O format=csv means-bad.sps], [1], [ignore])
-Table: Report
-,g1,g2,Mean,N
-a,1.00,11.00,4.00,1.00
-,,21.00,3.00,1.00
-,2.00,,6.00,1.00
-,,31.00,5.67,3.00
+AT_CLEANUP
-Table: Report
-,g2,Mean,N
-a,11.00,4.00,1.00
-,21.00,4.50,2.00
-,31.00,5.67,3.00
-Table: Report
-,Mean,N
-a,5.00,6.00
+
+AT_SETUP([MEANS parser bug])
+AT_KEYWORDS([categorical categoricals])
+
+dnl This bug caused an infinite loop
+AT_DATA([means-bad.sps], [dnl
+DATA LIST notable LIST /a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 fylo *.
+begin data.
+1 2 3 4 5 6 7 8 9 0 11
+end data.
+
+MEANS TABLES = a1 a2 a3 a4 a5 a6 a7 a8 a9 a10a BY fylo.
])
+AT_CHECK([pspp -O format=csv means-bad.sps], [1], [ignore])
+
AT_CLEANUP
-AT_SETUP([MEANS linear stats])
+dnl This example is based upon info from https://libguides.library.kent.edu/SPSS/CompareMeans
+AT_SETUP([MEANS default missing behaviour])
AT_KEYWORDS([categorical categoricals])
-dnl Slightly more involved example to test the linear statistics
-AT_DATA([means-linear.sps], [dnl
-set format F12.4.
-data list notable list /id * group * test1 *
-begin data.
-1 1 85
-2 1 90
-3 1 82
-4 1 75
-5 1 99
-6 2 70
-7 2 66
-8 2 52
-9 2 71
-10 2 50
+AT_DATA([means-missing.sps], [dnl
+data list notable list /w * score * a * b *.
+begin data
+ 12 . 0 0
+ 13 . 0 1
+ 11 . 1 0
+ 7 . 1 1
+ 5 1 0 .
+ 91 1 0 0
+ 130 1 0 1
+ 4 1 1 .
+ 90 1 1 0
+ 72 1 1 1
end data.
-add value labels /group 1 "experimental group" 2 "control group".
+weight by w.
-means test1 by group
- /cells = mean count stddev sum min max range variance kurt skew
- .
+MEANS tables=score
+ /cells = count.
+
+MEANS tables=score by a
+ /cells = count.
+
+MEANS tables=score by a by b
+ /cells = count.
])
+AT_CHECK([pspp -O format=csv means-missing.sps], [0], [dnl
+Table: Case Processing Summary
+,Cases,,,,,
+,Included,,Excluded,,Total,
+,N,Percent,N,Percent,N,Percent
+score,392,90.1%,43,9.9%,435,100.0%
+
+Table: Report
+N
+392
-AT_CHECK([pspp -O format=csv means-linear.sps], [0],
- [dnl
Table: Case Processing Summary
,Cases,,,,,
,Included,,Excluded,,Total,
,N,Percent,N,Percent,N,Percent
-test1: group,10,100.0%,0,.0%,10,100.0%
-test1: ,10,100.0%,0,.0%,10,100.0%
+score * a,392,90.1%,43,9.9%,435,100.0%
Table: Report
-,group,Mean,N,Std. Deviation,Sum,Min,Max,Range,Variance,Kurtosis,Skewness
-test1,experimental group,86.2000,5.0000,8.9833,431.0000,75.0000,99.0000,24.0000,80.7000,.2727,.3858
-,control group,61.8000,5.0000,10.0598,309.0000,50.0000,71.0000,21.0000,101.2000,-3.0437,-.4830
+a,N
+.00,226
+1.00,166
+Total,392
+
+Table: Case Processing Summary
+,Cases,,,,,
+,Included,,Excluded,,Total,
+,N,Percent,N,Percent,N,Percent
+score * a * b,383,88.0%,52,12.0%,435,100.0%
Table: Report
-,Mean,N,Std. Deviation,Sum,Min,Max,Range,Variance,Kurtosis,Skewness
-test1,74.0000,10.0000,15.6915,740.0000,50.0000,99.0000,49.0000,246.2222,-.5759,-.1262
+a,b,N
+.00,.00,91
+,1.00,130
+,Total,221
+1.00,.00,90
+,1.00,72
+,Total,162
+Total,.00,181
+,1.00,202
+,Total,383
])
AT_CLEANUP
-AT_SETUP([MEANS standard errors])
+dnl This example from https://www.spss-tutorials.com/spss-means-command/
+AT_SETUP([MEANS two way])
AT_KEYWORDS([categorical categoricals])
-AT_DATA([means-stderr.sps], [dnl
-set format F12.4.
-data list notable list /id * group * test1 *
-begin data.
-1 1 85
-2 1 90
-3 1 82
-4 1 75
-5 1 99
-6 1 70
-7 2 66
-8 2 52
-9 2 71
-10 2 50
+AT_DATA([means-freelancer.sps], [dnl
+data list notable list /income_2010 * gender sector_2010.
+begin data
+6072.40 0 5
+12706.65 1 4
+14912.82 0 2
+16338.36 1 5
+22606.99 0 .
+23544.95 1 1
+24985.21 0 2
+26586.48 0 1
+29076.24 1 3
+31010.18 0 2
+33190.63 1 1
+35570.67 1 4
+36202.60 1 4
+36205.85 1 2
+36262.56 1 .
+38283.56 0 1
+38569.91 1 5
+39057.56 1 4
+39594.68 1 5
+42087.38 0 1
+42370.92 0 2
+42931.32 1 2
+45907.58 0 4
+45911.32 1 .
+47227.09 1 3
+50440.71 1 5
+57440.17 1 3
+58918.86 0 5
+59430.07 1 2
+61135.95 0 4
+64193.85 0 4
+64857.02 0 3
+65903.42 0 4
+66592.38 1 3
+70986.10 0 3
+71229.94 0 4
+74663.05 1 4
+76676.14 1 4
+79260.80 0 4
+80311.71 0 4
end data.
-means test1 by group
- /cells = mean count semean seskew sekurt.
+means income_2010 by gender by sector_2010
+ /cells count min mean stddev.
])
-
-AT_CHECK([pspp -O format=csv means-stderr.sps], [0],
- [dnl
+AT_CHECK([pspp -O format=csv means-freelancer.sps], [0], [dnl
Table: Case Processing Summary
,Cases,,,,,
,Included,,Excluded,,Total,
,N,Percent,N,Percent,N,Percent
-test1: group,10,100.0%,0,.0%,10,100.0%
-test1: ,10,100.0%,0,.0%,10,100.0%
+income_2010 * gender * sector_2010,37,92.5%,3,7.5%,40,100.0%
Table: Report
-,group,Mean,N,S.E. Mean,S.E. Skew,S.E. Kurt
-test1,1.0000,83.5000,6.0000,4.2485,.8452,1.7408
-,2.0000,59.7500,4.0000,5.1700,1.0142,2.6186
-
-Table: Report
-,Mean,N,S.E. Mean,S.E. Skew,S.E. Kurt
-test1,74.0000,10.0000,4.9621,.6870,1.3342
+gender,sector_2010,N,Minimum,Mean,Std. Deviation
+.00,1.00,3,26586.48,35652.47,8078.46
+,2.00,4,14912.82,28319.78,11482.43
+,3.00,2,64857.02,67921.56,4333.91
+,4.00,7,45907.58,66849.04,11787.11
+,5.00,2,6072.40,32495.63,37368.09
+,Total,18,6072.40,49389.68,22371.48
+1.00,1.00,2,23544.95,28367.79,6820.53
+,2.00,3,36205.85,46189.08,11949.93
+,3.00,4,29076.24,50083.97,16084.44
+,4.00,6,12706.65,45812.78,24995.16
+,5.00,4,16338.36,36235.92,14311.04
+,Total,19,12706.65,42918.90,17851.64
+Total,1.00,5,23544.95,32738.60,7757.62
+,2.00,7,14912.82,35978.05,14309.27
+,3.00,6,29076.24,56029.83,15615.06
+,4.00,13,12706.65,57139.99,21187.85
+,5.00,6,6072.40,34989.15,20146.69
+,Total,37,6072.40,46066.84,20160.12
])
AT_CLEANUP
-
-AT_SETUP([MEANS harmonic and geometric means])
+dnl Check that rows are suppressed and that things generally work ok
+dnl when there are a 2 way instance contains an unbalanced set of
+dnl categorical values.
+AT_SETUP([MEANS unbalanced])
AT_KEYWORDS([categorical categoricals])
-AT_DATA([means-hg.sps], [dnl
-set format F12.4.
-data list notable list /x * y *.
+AT_DATA([means-unbalanced.sps], [dnl
+data list notable list /b c x *.
begin data.
-1 3
-2 3
-3 3
-4 3
-5 3
+4 1 123
+3 1 123
+5 0 246
+4 0 246
+3 0 246
end data.
+* The data above lack a 5 1 case.
-means x y
- /cells = mean harmonic geometric
-.
+means
+ table=x by b by c
+ /cells = mean count
+ .
])
-
-AT_CHECK([pspp -O format=csv means-hg.sps], [0],
- [dnl
+AT_CHECK([pspp -O format=csv means-unbalanced.sps], [0], [dnl
Table: Case Processing Summary
,Cases,,,,,
,Included,,Excluded,,Total,
,N,Percent,N,Percent,N,Percent
-x: ,5,100.0%,0,.0%,5,100.0%
-y: ,5,100.0%,0,.0%,5,100.0%
+x * b * c,5,100.0%,0,.0%,5,100.0%
Table: Report
-,Mean,Harmonic Mean,Geom. Mean
-x,3.0000,2.1898,2.6052
-y,3.0000,3.0000,3.0000
+b,c,Mean,N
+3.00,.00,246.00,1
+,1.00,123.00,1
+,Total,184.50,2
+4.00,.00,246.00,1
+,1.00,123.00,1
+,Total,184.50,2
+5.00,.00,246.00,1
+,Total,246.00,1
+Total,.00,246.00,3
+,1.00,123.00,2
+,Total,196.80,5
])
AT_CLEANUP
-
-
-
-
-
-AT_SETUP([MEANS all/none/default])
+dnl This example kindly provided by Dana Williams
+AT_SETUP([MEANS three way])
AT_KEYWORDS([categorical categoricals])
-dnl Make sure that /CELLS = {ALL,NONE,DEFAULT} work properly
-AT_DATA([means-stat-keywords.sps], [dnl
-SET FORMAT=F12.2.
-SET DECIMAL=DOT.
-
-DATA LIST NOTABLE LIST /score *.
-BEGIN DATA.
-22
-22
-29
-16
-23
-END DATA.
+AT_DATA([means-threeway.sps], [dnl
+data list notable list /score a b c.
+begin data.
+3 0 0 0
+4 0 0 1
+41 0 0 2
+5 0 1 0
+6 0 1 1
+7 1 0 0
+8 1 0 1
+9 1 1 0
+10 1 1 1
+end data.
-MEANS score /CELLS = ALL.
-MEANS score /CELLS = DEFAULT.
-MEANS score /CELLS = NONE.
+means score by a by b by c.
])
-
-AT_CHECK([pspp -O format=csv means-stat-keywords.sps], [0],
- [dnl
+AT_CHECK([pspp -O format=csv means-threeway.sps], [0], [dnl
Table: Case Processing Summary
,Cases,,,,,
,Included,,Excluded,,Total,
,N,Percent,N,Percent,N,Percent
-score: ,5,100.0%,0,.0%,5,100.0%
+score * a * b * c,9,100.0%,0,.0%,9,100.0%
Table: Report
-,Mean,N,Std. Deviation,S.E. Mean,Sum,Min,Max,Range,Variance,Kurtosis,S.E. Kurt,Skewness,S.E. Skew,First,Last,Harmonic Mean,Geom. Mean
-score,22.40,5.00,4.62,2.06,112.00,16.00,29.00,13.00,21.30,1.85,2.00,.11,.91,22.00,23.00,21.61,22.01
+a,b,c,Mean,N,Std. Deviation
+.00,.00,.00,3.00,1,NaN
+,,1.00,4.00,1,NaN
+,,2.00,41.00,1,NaN
+,,Total,16.00,3,21.66
+,1.00,.00,5.00,1,NaN
+,,1.00,6.00,1,NaN
+,,Total,5.50,2,.71
+,Total,.00,4.00,2,1.41
+,,1.00,5.00,2,1.41
+,,2.00,41.00,1,NaN
+,,Total,11.80,5,16.36
+1.00,.00,.00,7.00,1,NaN
+,,1.00,8.00,1,NaN
+,,Total,7.50,2,.71
+,1.00,.00,9.00,1,NaN
+,,1.00,10.00,1,NaN
+,,Total,9.50,2,.71
+,Total,.00,8.00,2,1.41
+,,1.00,9.00,2,1.41
+,,Total,8.50,4,1.29
+Total,.00,.00,5.00,2,2.83
+,,1.00,6.00,2,2.83
+,,2.00,41.00,1,NaN
+,,Total,12.60,5,16.01
+,1.00,.00,7.00,2,2.83
+,,1.00,8.00,2,2.83
+,,Total,7.50,4,2.38
+,Total,.00,6.00,4,2.58
+,,1.00,7.00,4,2.58
+,,2.00,41.00,1,NaN
+,,Total,10.33,9,11.73
+])
-Table: Case Processing Summary
-,Cases,,,,,
-,Included,,Excluded,,Total,
-,N,Percent,N,Percent,N,Percent
-score: ,5,100.0%,0,.0%,5,100.0%
+AT_CLEANUP
-Table: Report
-,Mean,N,Std. Deviation
-score,22.40,5.00,4.62
+dnl The above example again, but with string variables for
+dnl the control vars.
+AT_SETUP([MEANS three way string])
+AT_KEYWORDS([categorical categoricals])
+
+AT_DATA([means-threeway-string.sps], [dnl
+data list notable list /score (f22.2) a (a24) b (a16) c (a8).
+begin data.
+3 fooberrycrumblexzaQ fosilationwereqd zero
+4 fooberrycrumblexzaQ fosilationwereqd one
+41 fooberrycrumblexzaQ fosilationwereqd two
+5 fooberrycrumblexzaQ onlyonekonboys zero
+6 fooberrycrumblexzaQ onlyonekonboys one
+7 wontledingbatsXASDF fosilationwereqd zero
+8 wontledingbatsXASDF fosilationwereqd one
+9 wontledingbatsXASDF onlyonekonboys zero
+10 wontledingbatsXASDF onlyonekonboys one
+end data.
+
+means score by a by b by c.
+])
+AT_CHECK([pspp -O format=csv means-threeway-string.sps], [0], [dnl
Table: Case Processing Summary
,Cases,,,,,
,Included,,Excluded,,Total,
,N,Percent,N,Percent,N,Percent
-score: ,5,100.0%,0,.0%,5,100.0%
+score * a * b * c,9,100.0%,0,.0%,9,100.0%
Table: Report
+a,b,c,Mean,N,Std. Deviation
+fooberrycrumblexzaQ ,fosilationwereqd,one ,4.00,1,NaN
+,,two ,41.00,1,NaN
+,,zero ,3.00,1,NaN
+,,Total,16.00,3,21.66
+,onlyonekonboys ,one ,6.00,1,NaN
+,,zero ,5.00,1,NaN
+,,Total,5.50,2,.71
+,Total,one ,5.00,2,1.41
+,,two ,41.00,1,NaN
+,,zero ,4.00,2,1.41
+,,Total,11.80,5,16.36
+wontledingbatsXASDF ,fosilationwereqd,one ,8.00,1,NaN
+,,zero ,7.00,1,NaN
+,,Total,7.50,2,.71
+,onlyonekonboys ,one ,10.00,1,NaN
+,,zero ,9.00,1,NaN
+,,Total,9.50,2,.71
+,Total,one ,9.00,2,1.41
+,,zero ,8.00,2,1.41
+,,Total,8.50,4,1.29
+Total,fosilationwereqd,one ,6.00,2,2.83
+,,two ,41.00,1,NaN
+,,zero ,5.00,2,2.83
+,,Total,12.60,5,16.01
+,onlyonekonboys ,one ,8.00,2,2.83
+,,zero ,7.00,2,2.83
+,,Total,7.50,4,2.38
+,Total,one ,7.00,4,2.58
+,,two ,41.00,1,NaN
+,,zero ,6.00,4,2.58
+,,Total,10.33,9,11.73
])
AT_CLEANUP
-
-AT_SETUP([MEANS missing=table ])
+dnl An example with multiple tables
+AT_SETUP([MEANS multiple tables])
AT_KEYWORDS([categorical categoricals])
-AT_DATA([means-miss-table.sps], [dnl
-data list notable list /a * b * g1.
+AT_DATA([means-multi-table.sps], [dnl
+data list notable list /a * b * c * x * y *.
begin data.
-1 9 1
-2 9 1
-3 9 1
-4 9 2
-5 9 2
-6 9 2
-7 . 2
+6 3 0 123 456
+6 3 1 123 456
+6 4 0 123 456
+6 4 1 123 456
+6 5 0 123 456
+6 5 1 123 456
+7 3 0 123 456
+7 3 1 123 456
+7 4 0 123 456
+7 4 1 123 456
+7 5 0 123 456
+7 5 1 123 456
+8 3 0 123 456
+8 3 1 123 456
+8 4 0 123 456
+8 4 1 123 456
+8 5 0 123 456
+8 5 1 123 456
+9 3 0 123 456
+9 3 1 123 456
+9 4 0 123 456
+9 4 1 123 456
+9 5 0 123 456
+9 5 1 123 456
end data.
-MEANS a b BY g1
- /a BY g1
- /cells = COUNT
- /missing = TABLE
- .
-MEANS a b BY g1
- /a BY g1
- /cells = COUNT
- .
+means table = x by b by c
+ /x by b
+ /y by a by b
+ cells = min count .
])
-
-AT_CHECK([pspp -o pspp.csv -o pspp.txt means-miss-table.sps])
-AT_CHECK([cat pspp.csv], [0], [dnl
+AT_CHECK([pspp -O format=csv means-multi-table.sps], [0], [dnl
Table: Case Processing Summary
,Cases,,,,,
,Included,,Excluded,,Total,
,N,Percent,N,Percent,N,Percent
-a: g1,7,100.0%,0,.0%,7,100.0%
-a: ,7,100.0%,0,.0%,7,100.0%
-b: g1,6,85.7%,1,14.3%,7,100.0%
-b: ,6,85.7%,1,14.3%,7,100.0%
+x * b * c,24,100.0%,0,.0%,24,100.0%
Table: Report
-,g1,N
-a,1.00,3.00
-,2.00,3.00
-b,1.00,3.00
-,2.00,3.00
-
-Table: Report
-,N
-a,6.00
-b,6.00
+b,c,Minimum,N
+3.00,.00,123.00,4
+,1.00,123.00,4
+,Total,123.00,8
+4.00,.00,123.00,4
+,1.00,123.00,4
+,Total,123.00,8
+5.00,.00,123.00,4
+,1.00,123.00,4
+,Total,123.00,8
+Total,.00,123.00,12
+,1.00,123.00,12
+,Total,123.00,24
Table: Case Processing Summary
,Cases,,,,,
,Included,,Excluded,,Total,
,N,Percent,N,Percent,N,Percent
-a: g1,7,100.0%,0,.0%,7,100.0%
-a: ,7,100.0%,0,.0%,7,100.0%
-
-Table: Report
-,g1,N
-a,1.00,3.00
-,2.00,4.00
+x * b,24,100.0%,0,.0%,24,100.0%
Table: Report
-,N
-a,7.00
+b,Minimum,N
+3.00,123.00,8
+4.00,123.00,8
+5.00,123.00,8
+Total,123.00,24
Table: Case Processing Summary
,Cases,,,,,
,Included,,Excluded,,Total,
,N,Percent,N,Percent,N,Percent
-a: g1,7,100.0%,0,.0%,7,100.0%
-a: ,7,100.0%,0,.0%,7,100.0%
-b: g1,6,85.7%,1,14.3%,7,100.0%
-b: ,6,85.7%,1,14.3%,7,100.0%
+y * a * b,24,100.0%,0,.0%,24,100.0%
Table: Report
-,g1,N
-a,1.00,3.00
-,2.00,4.00
-b,1.00,3.00
-,2.00,3.00
+a,b,Minimum,N
+6.00,3.00,456.00,2
+,4.00,456.00,2
+,5.00,456.00,2
+,Total,456.00,6
+7.00,3.00,456.00,2
+,4.00,456.00,2
+,5.00,456.00,2
+,Total,456.00,6
+8.00,3.00,456.00,2
+,4.00,456.00,2
+,5.00,456.00,2
+,Total,456.00,6
+9.00,3.00,456.00,2
+,4.00,456.00,2
+,5.00,456.00,2
+,Total,456.00,6
+Total,3.00,456.00,8
+,4.00,456.00,8
+,5.00,456.00,8
+,Total,456.00,24
+])
-Table: Report
-,N
-a,7.00
-b,6.00
+AT_CLEANUP
+
+
+dnl An example with more than one dependent variable.
+dnl This case uses a somewhat different table layout.
+AT_SETUP([MEANS multi variable])
+AT_KEYWORDS([categorical categoricals])
+
+AT_DATA([means-multi-variable.sps], [dnl
+data list notable list /b c x y.
+begin data.
+5 1 123 55
+5 1 123 55
+5 1 123 55
+5 1 123 55
+4 1 456 44
+4 1 456 44
+4 1 456 44
+4 1 456 44
+3 1 789 55
+3 1 789 55
+3 1 789 55
+3 1 789 55
+5 0 246 99
+5 0 246 99
+5 0 246 99
+5 0 246 .
+4 0 987 99
+4 0 987 99
+4 0 987 99
+4 0 987 99
+3 0 654 11
+3 0 654 11
+3 0 654 11
+3 0 654 11
+end data.
+
+means
+ table = x y by b by c
+ .
+])
+
+AT_CHECK([pspp -O format=csv means-multi-variable.sps], [0], [dnl
Table: Case Processing Summary
,Cases,,,,,
,Included,,Excluded,,Total,
,N,Percent,N,Percent,N,Percent
-a: g1,7,100.0%,0,.0%,7,100.0%
-a: ,7,100.0%,0,.0%,7,100.0%
+x * b * c,24,100.0%,0,.0%,24,100.0%
+y * b * c,23,95.8%,1,4.2%,24,100.0%
+
+Table: x * y * b * c
+b,c,,x,y
+3.00,.00,Mean,654.00,11.00
+,,N,4,4
+,,Std. Deviation,.00,.00
+,1.00,Mean,789.00,55.00
+,,N,4,4
+,,Std. Deviation,.00,.00
+,Total,Mean,721.50,33.00
+,,N,8,8
+,,Std. Deviation,72.16,23.52
+4.00,.00,Mean,987.00,99.00
+,,N,4,4
+,,Std. Deviation,.00,.00
+,1.00,Mean,456.00,44.00
+,,N,4,4
+,,Std. Deviation,.00,.00
+,Total,Mean,721.50,71.50
+,,N,8,8
+,,Std. Deviation,283.83,29.40
+5.00,.00,Mean,246.00,99.00
+,,N,4,3
+,,Std. Deviation,.00,.00
+,1.00,Mean,123.00,55.00
+,,N,4,4
+,,Std. Deviation,.00,.00
+,Total,Mean,184.50,73.86
+,,N,8,7
+,,Std. Deviation,65.75,23.52
+Total,.00,Mean,629.00,67.00
+,,N,12,11
+,,Std. Deviation,316.50,44.40
+,1.00,Mean,456.00,51.33
+,,N,12,12
+,,Std. Deviation,283.98,5.42
+,Total,Mean,542.50,58.83
+,,N,24,23
+,,Std. Deviation,307.06,31.22
+])
-Table: Report
-,g1,N
-a,1.00,3.00
-,2.00,4.00
-Table: Report
-,N
-a,7.00
-])
AT_CLEANUP
-AT_SETUP([MEANS user missing values])
+
+dnl This example is based upon one kindly provided by Dana Williams
+dnl It exercises the most complex case where there are multiple
+dnl dependent variables AND multiple control variables in each layer.
+AT_SETUP([MEANS multi combination])
AT_KEYWORDS([categorical categoricals])
-AT_DATA([means-missing.sps], [dnl
-data list notable list /a * b * g1.
-begin data.
-1 2 9
-2 2 1
-3 2 1
-4 2 2
-5 2 2
-6 2 2
-7 9 2
+AT_DATA([means-multi-combination.sps], [dnl
+data list notable list /one two three four five six.
+begin data
+1 1 1 1 1 1
+2 1 1 1 1 1
+1 2 1 1 1 1
+2 2 1 1 1 1
+1 1 2 1 1 1
+2 1 2 1 1 1
+1 2 2 1 1 1
+2 2 2 1 1 1
+1 1 1 2 1 1
+2 1 1 2 1 1
+1 2 1 2 1 1
+2 2 1 2 1 1
+1 1 2 2 1 1
+2 1 2 2 1 1
+1 2 2 2 1 1
+2 2 2 2 1 1
+1 1 1 1 2 1
+2 1 1 1 2 1
+1 2 1 1 2 1
+2 2 1 1 2 1
+1 1 2 1 2 1
+2 1 2 1 2 1
+1 2 2 1 2 1
+2 2 2 1 2 1
+1 1 1 2 2 1
+2 1 1 2 2 1
+1 2 1 2 2 1
+2 2 1 2 2 1
+1 1 2 2 2 1
+2 1 2 2 2 1
+1 2 2 2 2 1
+2 2 2 2 2 1
+1 1 1 1 1 2
+2 1 1 1 1 2
+1 2 1 1 1 2
+2 2 1 1 1 2
+1 1 2 1 1 2
+2 1 2 1 1 2
+1 2 2 1 1 2
+2 2 2 1 1 2
+1 1 1 2 1 2
+2 1 1 2 1 2
+1 2 1 2 1 2
+2 2 1 2 1 2
+1 1 2 2 1 2
+2 1 2 2 1 2
+1 2 2 2 1 2
+2 2 2 2 1 2
+1 1 1 1 2 2
+2 1 1 1 2 2
+1 2 1 1 2 2
+2 2 1 1 2 2
+1 1 2 1 2 2
+2 1 2 1 2 2
+1 2 2 1 2 2
+2 2 2 1 2 2
+1 1 1 2 2 2
+2 1 1 2 2 2
+1 2 1 2 2 2
+2 2 1 2 2 2
+1 1 2 2 2 2
+2 1 2 2 2 2
+1 2 2 2 2 2
+2 2 2 2 2 2
end data.
-MISSING VALUES a b g1 (9).
-
-MEANS a b BY g1 /cells = COUNT .
+recode six (2 = 62) (1 = 61).
+recode five (2 = 52) (1 = 51).
+recode four (2 = 42) (1 = 41).
+recode three (2 = 32) (1 = 31).
-MEANS a b BY g1 /cells = COUNT /missing = include .
+set format F22.5.
-MEANS a b BY g1 /cells = COUNT /missing = dependent .
+means tables = one two BY three four BY five six.
])
-
-AT_CHECK([pspp -o pspp.csv -o pspp.txt means-missing.sps])
-AT_CHECK([cat pspp.csv], [0], [dnl
+AT_CHECK([pspp -O format=csv means-multi-combination.sps], [0], [dnl
Table: Case Processing Summary
,Cases,,,,,
,Included,,Excluded,,Total,
,N,Percent,N,Percent,N,Percent
-a: g1,6,85.7%,1,14.3%,7,100.0%
-a: ,7,100.0%,0,.0%,7,100.0%
-b: g1,5,71.4%,2,28.6%,7,100.0%
-b: ,6,85.7%,1,14.3%,7,100.0%
-
-Table: Report
-,g1,N
-a,1.00,2.00
-,2.00,4.00
-b,1.00,2.00
-,2.00,3.00
+one * three * five,64,100.0%,0,.0%,64,100.0%
+two * three * five,64,100.0%,0,.0%,64,100.0%
+one * three * six,64,100.0%,0,.0%,64,100.0%
+two * three * six,64,100.0%,0,.0%,64,100.0%
+one * four * five,64,100.0%,0,.0%,64,100.0%
+two * four * five,64,100.0%,0,.0%,64,100.0%
+one * four * six,64,100.0%,0,.0%,64,100.0%
+two * four * six,64,100.0%,0,.0%,64,100.0%
+
+Table: one * two * three * five
+three,five,,one,two
+31.00,51.00,Mean,1.50000,1.50000
+,,N,16,16
+,,Std. Deviation,.51640,.51640
+,52.00,Mean,1.50000,1.50000
+,,N,16,16
+,,Std. Deviation,.51640,.51640
+,Total,Mean,1.50000,1.50000
+,,N,32,32
+,,Std. Deviation,.50800,.50800
+32.00,51.00,Mean,1.50000,1.50000
+,,N,16,16
+,,Std. Deviation,.51640,.51640
+,52.00,Mean,1.50000,1.50000
+,,N,16,16
+,,Std. Deviation,.51640,.51640
+,Total,Mean,1.50000,1.50000
+,,N,32,32
+,,Std. Deviation,.50800,.50800
+Total,51.00,Mean,1.50000,1.50000
+,,N,32,32
+,,Std. Deviation,.50800,.50800
+,52.00,Mean,1.50000,1.50000
+,,N,32,32
+,,Std. Deviation,.50800,.50800
+,Total,Mean,1.50000,1.50000
+,,N,64,64
+,,Std. Deviation,.50395,.50395
+
+Table: one * two * three * six
+three,six,,one,two
+31.00,61.00,Mean,1.50000,1.50000
+,,N,16,16
+,,Std. Deviation,.51640,.51640
+,62.00,Mean,1.50000,1.50000
+,,N,16,16
+,,Std. Deviation,.51640,.51640
+,Total,Mean,1.50000,1.50000
+,,N,32,32
+,,Std. Deviation,.50800,.50800
+32.00,61.00,Mean,1.50000,1.50000
+,,N,16,16
+,,Std. Deviation,.51640,.51640
+,62.00,Mean,1.50000,1.50000
+,,N,16,16
+,,Std. Deviation,.51640,.51640
+,Total,Mean,1.50000,1.50000
+,,N,32,32
+,,Std. Deviation,.50800,.50800
+Total,61.00,Mean,1.50000,1.50000
+,,N,32,32
+,,Std. Deviation,.50800,.50800
+,62.00,Mean,1.50000,1.50000
+,,N,32,32
+,,Std. Deviation,.50800,.50800
+,Total,Mean,1.50000,1.50000
+,,N,64,64
+,,Std. Deviation,.50395,.50395
+
+Table: one * two * four * five
+four,five,,one,two
+41.00,51.00,Mean,1.50000,1.50000
+,,N,16,16
+,,Std. Deviation,.51640,.51640
+,52.00,Mean,1.50000,1.50000
+,,N,16,16
+,,Std. Deviation,.51640,.51640
+,Total,Mean,1.50000,1.50000
+,,N,32,32
+,,Std. Deviation,.50800,.50800
+42.00,51.00,Mean,1.50000,1.50000
+,,N,16,16
+,,Std. Deviation,.51640,.51640
+,52.00,Mean,1.50000,1.50000
+,,N,16,16
+,,Std. Deviation,.51640,.51640
+,Total,Mean,1.50000,1.50000
+,,N,32,32
+,,Std. Deviation,.50800,.50800
+Total,51.00,Mean,1.50000,1.50000
+,,N,32,32
+,,Std. Deviation,.50800,.50800
+,52.00,Mean,1.50000,1.50000
+,,N,32,32
+,,Std. Deviation,.50800,.50800
+,Total,Mean,1.50000,1.50000
+,,N,64,64
+,,Std. Deviation,.50395,.50395
+
+Table: one * two * four * six
+four,six,,one,two
+41.00,61.00,Mean,1.50000,1.50000
+,,N,16,16
+,,Std. Deviation,.51640,.51640
+,62.00,Mean,1.50000,1.50000
+,,N,16,16
+,,Std. Deviation,.51640,.51640
+,Total,Mean,1.50000,1.50000
+,,N,32,32
+,,Std. Deviation,.50800,.50800
+42.00,61.00,Mean,1.50000,1.50000
+,,N,16,16
+,,Std. Deviation,.51640,.51640
+,62.00,Mean,1.50000,1.50000
+,,N,16,16
+,,Std. Deviation,.51640,.51640
+,Total,Mean,1.50000,1.50000
+,,N,32,32
+,,Std. Deviation,.50800,.50800
+Total,61.00,Mean,1.50000,1.50000
+,,N,32,32
+,,Std. Deviation,.50800,.50800
+,62.00,Mean,1.50000,1.50000
+,,N,32,32
+,,Std. Deviation,.50800,.50800
+,Total,Mean,1.50000,1.50000
+,,N,64,64
+,,Std. Deviation,.50395,.50395
+])
-Table: Report
-,N
-a,6.00
-b,5.00
+AT_CLEANUP
-Table: Case Processing Summary
-,Cases,,,,,
-,Included,,Excluded,,Total,
-,N,Percent,N,Percent,N,Percent
-a: g1,7,100.0%,0,.0%,7,100.0%
-a: ,7,100.0%,0,.0%,7,100.0%
-b: g1,7,100.0%,0,.0%,7,100.0%
-b: ,7,100.0%,0,.0%,7,100.0%
-Table: Report
-,g1,N
-a,1.00,2.00
-,2.00,4.00
-,9.00,1.00
-b,1.00,2.00
-,2.00,4.00
-,9.00,1.00
+dnl This example was observed to cause a crash in the
+dnl destructor. Found by zzuf.
+AT_SETUP([MEANS clean up])
+AT_KEYWORDS([categorical categoricals])
-Table: Report
-,N
-a,7.00
-b,7.00
+AT_DATA([means-bad.sps], [dnl
+data list notable list /one two three four five six.
+begin data
+1 1 1 1 1 1
+2 1 1 1 1 !
+1 2 2 2 2 2
+2 2 2 2 2 2
+end data.
-Table: Case Processing Summary
-,Cases,,,,,
-,Included,,Excluded,,Total,
-,N,Percent,N,Percent,N,Percent
-a: g1,7,100.0%,0,.0%,7,100.0%
-a: ,7,100.0%,0,.0%,7,100.0%
-b: g1,6,85.7%,1,14.3%,7,100.0%
-b: ,6,85.7%,1,14.3%,7,100.0%
+means tables = one two BY thsee four BY five six.
+])
-Table: Report
-,g1,N
-a,1.00,2.00
-,2.00,4.00
-,9.00,1.00
-b,1.00,2.00
-,2.00,3.00
-,9.00,1.00
+AT_CHECK([pspp -O format=csv means-bad.sps], [1], [ignore])
-Table: Report
-,N
-a,7.00
-b,6.00
-])
AT_CLEANUP
-
-AT_SETUP([MEANS empty factor spec])
+dnl Another example which caused a crash.
+dnl Found by zzuf.
+AT_SETUP([MEANS control all missing])
AT_KEYWORDS([categorical categoricals])
AT_DATA([means-bad.sps], [dnl
-data list list /outcome *.
+data list notable list /a * b * y * uu *.
begin data.
-1
-2
-3
+6 3 . 5
+6 3 . 5
+6 4 . 5
end data.
-MEANS TABLES = outcome
- BY.
+means table = b by a by y by uu
+ .
])
-AT_CHECK([pspp -O format=csv means-bad.sps], [1], [ignore])
+AT_CHECK([pspp -O format=csv means-bad.sps], [0], [dnl
+Table: Case Processing Summary
+,Cases,,,,,
+,Included,,Excluded,,Total,
+,N,Percent,N,Percent,N,Percent
+b * a * y * uu,0,.0%,3,100.0%,3,100.0%
-AT_CLEANUP
+"warning: The table ""a * y * uu"" has no non-empty control variables. No result for this table will be displayed."
+])
+AT_CLEANUP
-AT_SETUP([MEANS parser bug])
+dnl Do some tests on the MISSING keyword.
+AT_SETUP([MEANS missing classes])
AT_KEYWORDS([categorical categoricals])
-dnl This bug caused an infinite loop
-AT_DATA([means-bad.sps], [dnl
-DATA LIST notable LIST /a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 fylo *.
+AT_DATA([means-missing-classes.sps], [dnl
+data list notable list /hand * score *.
begin data.
-1 2 3 4 5 6 7 8 9 0 11
+1 17
+1 17
+1 17
+1 16
+1 17
+1 16
+1 16
+1 .
+1 99
+2 21
+2 22
+2 20
+2 20
+2 20
+2 20
+2 20
+2 20
+2 20
+2 20
+9 55
end data.
-MEANS TABLES = a1 a2 a3 a4 a5 a6 a7 a8 a9 a10a BY fylo.
+missing values score (99).
+missing values hand (9).
+
+means tables=score by hand
+ /cells = count max
+ /missing = dependent
+ .
+
+means tables=score by hand
+ /cells = count max
+ /missing = include
+ .
+
+means tables=score by hand
+ /cells = count max
+ .
+
])
-AT_CHECK([pspp -O format=csv means-bad.sps], [1], [ignore])
+AT_CHECK([pspp -O format=csv means-missing-classes.sps], [0], [dnl
+Table: Case Processing Summary
+,Cases,,,,,
+,Included,,Excluded,,Total,
+,N,Percent,N,Percent,N,Percent
+score * hand,18,90.0%,2,10.0%,20,100.0%
-AT_CLEANUP
+Table: Report
+hand,N,Maximum
+1.00,7,17.00
+2.00,10,22.00
+9.00,1,55.00
+Total,18,55.00
+Table: Case Processing Summary
+,Cases,,,,,
+,Included,,Excluded,,Total,
+,N,Percent,N,Percent,N,Percent
+score * hand,19,95.0%,1,5.0%,20,100.0%
+
+Table: Report
+hand,N,Maximum
+1.00,8,99.00
+2.00,10,22.00
+9.00,1,55.00
+Total,19,99.00
+
+Table: Case Processing Summary
+,Cases,,,,,
+,Included,,Excluded,,Total,
+,N,Percent,N,Percent,N,Percent
+score * hand,17,85.0%,3,15.0%,20,100.0%
+
+Table: Report
+hand,N,Maximum
+1.00,7,17.00
+2.00,10,22.00
+Total,17,22.00
+])
+
+AT_CLEANUP
projects / pspp / commitdiff