binomial_execute (const struct dataset *ds,
struct casereader *input,
enum mv_class exclude,
- const struct npar_test *test)
+ const struct npar_test *test,
+ bool exact UNUSED,
+ double timer UNUSED)
{
int v;
+ const struct dictionary *dict = dataset_dict (ds);
const struct binomial_test *bst = (const struct binomial_test *) test;
const struct one_sample_test *ost = (const struct one_sample_test*) test;
if ( bst->category2 != SYSMIS )
{
+ int i;
union value v;
v.f = bst->category2;
- cat2->value = value_dup (&v, 0);
+ for (i = 0; i < ost->n_vars; i++)
+ cat2[i].value = value_dup (&v, 0);
}
- if (do_binomial (dataset_dict(ds), input, bst, cat1, cat2, exclude))
+ if (do_binomial (dict, input, bst, cat1, cat2, exclude))
{
+ const struct variable *wvar = dict_get_weight (dict);
+ const struct fmt_spec *wfmt = wvar ?
+ var_get_print_format (wvar) : & F_8_0;
+
struct tab_table *table = tab_create (7, ost->n_vars * 3 + 1, 0);
tab_dim (table, tab_natural_dimensions);
tab_text (table, 2, 2 + v * 3, TAB_NONE, ds_cstr (&catstr2));
/* Observed N */
- tab_float (table, 3, 1 + v * 3, TAB_NONE, cat1[v].count, 8, 0);
- tab_float (table, 3, 2 + v * 3, TAB_NONE, cat2[v].count, 8, 0);
+ tab_double (table, 3, 1 + v * 3, TAB_NONE, cat1[v].count, wfmt);
+ tab_double (table, 3, 2 + v * 3, TAB_NONE, cat2[v].count, wfmt);
n_total = cat1[v].count + cat2[v].count;
- tab_float (table, 3, 3 + v * 3, TAB_NONE, n_total, 8, 0);
+ tab_double (table, 3, 3 + v * 3, TAB_NONE, n_total, wfmt);
/* Observed Proportions */
- tab_float (table, 4, 1 + v * 3, TAB_NONE,
- cat1[v].count / n_total, 8, 3);
- tab_float (table, 4, 2 + v * 3, TAB_NONE,
- cat2[v].count / n_total, 8, 3);
- tab_float (table, 4, 3 + v * 3, TAB_NONE,
- (cat1[v].count + cat2[v].count) / n_total, 8, 2);
+ tab_double (table, 4, 1 + v * 3, TAB_NONE,
+ cat1[v].count / n_total, NULL);
+ tab_double (table, 4, 2 + v * 3, TAB_NONE,
+ cat2[v].count / n_total, NULL);
+
+ tab_double (table, 4, 3 + v * 3, TAB_NONE,
- (cat1[v].count + cat2[v].count) / n_total, wfmt);
++ (cat1[v].count + cat2[v].count) / n_total, NULL);
/* Significance */
sig = calculate_binomial (cat1[v].count, cat2[v].count, bst->p);
static int internal_cmd_crosstabs (struct lexer *lexer, struct dataset *ds);
static void precalc (struct casereader *, const struct dataset *);
-static void calc_general (struct ccase *, const struct dataset *);
-static void calc_integer (struct ccase *, const struct dataset *);
+static void calc_general (const struct ccase *, const struct dataset *);
+static void calc_integer (const struct ccase *, const struct dataset *);
- static void postcalc (void);
+ static void postcalc (const struct dataset *);
++
static void submit (struct tab_table *);
static void format_short (char *s, const struct fmt_spec *fp,
static struct cmd_examine cmd;
static const struct variable **dependent_vars;
-
static size_t n_dependent_vars;
+/* PERCENTILES */
+
+static subc_list_double percentile_list;
+static enum pc_alg percentile_algorithm;
-struct factor
+struct factor_metrics
{
- /* The independent variable */
- struct variable *indep_var[2];
+ struct moments1 *moments;
+
+ struct percentile **ptl;
+ size_t n_ptiles;
+
+ struct statistic *tukey_hinges;
+ struct statistic *box_whisker;
+ struct statistic *trimmed_mean;
+ struct statistic *histogram;
+ struct order_stats *np;
+
+ /* Three quartiles indexing into PTL */
+ struct percentile **quartiles;
+
+ /* A reader sorted in ASCENDING order */
+ struct casereader *up_reader;
+
+ /* The minimum value of all the weights */
+ double cmin;
+
+ /* Sum of all weights, including those for missing values */
+ double n;
+
+ /* Sum of weights of non_missing values */
+ double n_valid;
+ double mean;
- /* Hash table of factor stats indexed by 2 values */
- struct hsh_table *fstats;
+ double variance;
- /* The hash table after it has been crunched */
- struct factor_statistics **fs;
+ double skewness;
- struct factor *next;
+ double kurtosis;
+ double se_mean;
+
+ struct extrema *minima;
+ struct extrema *maxima;
};
-/* Linked list of factors */
-static struct factor *factors = 0;
+struct factor_result
+{
+ struct ll ll;
-static struct metrics *totals = 0;
+ union value *value[2];
-/* Parse the clause specifying the factors */
-static int examine_parse_independent_vars (struct lexer *lexer, const struct dictionary *dict, struct cmd_examine *cmd);
+ /* An array of factor metrics, one for each variable */
+ struct factor_metrics *metrics;
+};
+struct xfactor
+{
+ /* We need to make a list of this structure */
+ struct ll ll;
+ /* The independent variable */
+ const struct variable const* indep_var[2];
-/* Output functions */
-static void show_summary (const struct variable **dependent_var, int n_dep_var,
- const struct dictionary *dict,
- const struct factor *f);
+ /* A list of results for this factor */
+ struct ll_list result_list ;
+};
-static void show_extremes (const struct variable **dependent_var,
- int n_dep_var,
- const struct factor *factor,
- int n_extremities);
-static void show_descriptives (const struct variable **dependent_var,
- int n_dep_var,
- struct factor *factor);
+static void
+factor_destroy (struct xfactor *fctr)
+{
+ struct ll *ll = ll_head (&fctr->result_list);
+ while (ll != ll_null (&fctr->result_list))
+ {
+ int v;
+ struct factor_result *result =
+ ll_data (ll, struct factor_result, ll);
-static void show_percentiles (const struct variable **dependent_var,
- int n_dep_var,
- struct factor *factor);
+ for (v = 0; v < n_dependent_vars; ++v)
+ {
+ int i;
+ moments1_destroy (result->metrics[v].moments);
+ extrema_destroy (result->metrics[v].minima);
+ extrema_destroy (result->metrics[v].maxima);
+ statistic_destroy (result->metrics[v].trimmed_mean);
+ statistic_destroy (result->metrics[v].tukey_hinges);
+ statistic_destroy (result->metrics[v].box_whisker);
+ statistic_destroy (result->metrics[v].histogram);
+ for (i = 0 ; i < result->metrics[v].n_ptiles; ++i)
+ statistic_destroy ((struct statistic *) result->metrics[v].ptl[i]);
+ free (result->metrics[v].ptl);
+ free (result->metrics[v].quartiles);
+ casereader_destroy (result->metrics[v].up_reader);
+ }
+
+ free (result->value[0]);
+ free (result->value[1]);
+ free (result->metrics);
+ ll = ll_next (ll);
+ free (result);
+ }
+}
+static struct xfactor level0_factor;
+static struct ll_list factor_list;
+
+/* Parse the clause specifying the factors */
+static int examine_parse_independent_vars (struct lexer *lexer,
+ const struct dictionary *dict,
+ struct cmd_examine *cmd);
+/* Output functions */
+static void show_summary (const struct variable **dependent_var, int n_dep_var,
++ const struct dictionary *dict,
+ const struct xfactor *f);
-void np_plot (const struct metrics *m, const char *factorname);
+static void show_descriptives (const struct variable **dependent_var,
+ int n_dep_var,
+ const struct xfactor *f);
-void box_plot_group (const struct factor *fctr,
- const struct variable **vars, int n_vars,
- const struct variable *id
- ) ;
+static void show_percentiles (const struct variable **dependent_var,
+ int n_dep_var,
+ const struct xfactor *f);
-void box_plot_variables (const struct factor *fctr,
- const struct variable **vars, int n_vars,
- const struct variable *id
- );
+static void show_extremes (const struct variable **dependent_var,
+ int n_dep_var,
+ const struct xfactor *f);
+
}
-/* Create a hash table of percentiles and their values from the list of
- percentiles */
-static struct hsh_table *
-list_to_ptile_hash (const subc_list_double *l)
+/* Show all the appropriate tables */
+static void
- output_examine (void)
++output_examine (const struct dictionary *dict)
{
- int i;
+ struct ll *ll;
+
- show_summary (dependent_vars, n_dependent_vars, &level0_factor);
++ show_summary (dependent_vars, n_dependent_vars, dict, &level0_factor);
- struct hsh_table *h ;
+ if ( cmd.a_statistics[XMN_ST_EXTREME] )
+ show_extremes (dependent_vars, n_dependent_vars, &level0_factor);
- h = hsh_create (subc_list_double_count (l),
- (hsh_compare_func *) ptile_compare,
- (hsh_hash_func *) ptile_hash,
- (hsh_free_func *) free,
- 0);
+ if ( cmd.a_statistics[XMN_ST_DESCRIPTIVES] )
+ show_descriptives (dependent_vars, n_dependent_vars, &level0_factor);
+ if ( cmd.sbc_percentiles)
+ show_percentiles (dependent_vars, n_dependent_vars, &level0_factor);
- for ( i = 0 ; i < subc_list_double_count (l) ; ++i )
+ if ( cmd.sbc_plot)
{
- struct percentile *p = xmalloc (sizeof *p);
-
- p->p = subc_list_double_at (l,i);
- p->v = SYSMIS;
+ if (cmd.a_plot[XMN_PLT_BOXPLOT])
+ show_boxplot_groups (dependent_vars, n_dependent_vars, &level0_factor);
- hsh_insert (h, p);
+ if (cmd.a_plot[XMN_PLT_HISTOGRAM])
+ show_histogram (dependent_vars, n_dependent_vars, &level0_factor);
+ if (cmd.a_plot[XMN_PLT_NPPLOT])
+ show_npplot (dependent_vars, n_dependent_vars, &level0_factor);
}
- return h;
+ for (ll = ll_head (&factor_list);
+ ll != ll_null (&factor_list); ll = ll_next (ll))
+ {
+ struct xfactor *factor = ll_data (ll, struct xfactor, ll);
- show_summary (dependent_vars, n_dependent_vars, factor);
++ show_summary (dependent_vars, n_dependent_vars, dict, factor);
+
+ if ( cmd.a_statistics[XMN_ST_EXTREME] )
+ show_extremes (dependent_vars, n_dependent_vars, factor);
+ if ( cmd.a_statistics[XMN_ST_DESCRIPTIVES] )
+ show_descriptives (dependent_vars, n_dependent_vars, factor);
+
+ if ( cmd.sbc_percentiles)
+ show_percentiles (dependent_vars, n_dependent_vars, factor);
+
+ if (cmd.a_plot[XMN_PLT_BOXPLOT] &&
+ cmd.cmp == XMN_GROUPS)
+ show_boxplot_groups (dependent_vars, n_dependent_vars, factor);
+
+
+ if (cmd.a_plot[XMN_PLT_BOXPLOT] &&
+ cmd.cmp == XMN_VARIABLES)
+ show_boxplot_variables (dependent_vars, n_dependent_vars,
+ factor);
+
+ if (cmd.a_plot[XMN_PLT_HISTOGRAM])
+ show_histogram (dependent_vars, n_dependent_vars, factor);
+
+ if (cmd.a_plot[XMN_PLT_NPPLOT])
+ show_npplot (dependent_vars, n_dependent_vars, factor);
+ }
}
/* Parse the PERCENTILES subcommand */
return success;
}
+static void
+examine_group (struct cmd_examine *cmd, struct casereader *reader, int level,
+ const struct dictionary *dict, struct xfactor *factor)
+{
+ struct ccase *c;
+ const struct variable *wv = dict_get_weight (dict);
+ int v;
+ int n_extrema = 1;
+ struct factor_result *result = xzalloc (sizeof (*result));
+
+ result->metrics = xcalloc (n_dependent_vars, sizeof (*result->metrics));
+ if ( cmd->a_statistics[XMN_ST_EXTREME] )
+ n_extrema = cmd->st_n;
-static void populate_percentiles (struct tab_table *tbl, int col, int row,
- const struct metrics *m);
+ c = casereader_peek (reader, 0);
+ if (c != NULL)
+ {
+ if ( level > 0)
+ {
+ result->value[0] =
+ value_dup (case_data (c, factor->indep_var[0]),
+ var_get_width (factor->indep_var[0]));
+
+ if ( level > 1)
+ result->value[1] =
+ value_dup (case_data (c, factor->indep_var[1]),
+ var_get_width (factor->indep_var[1]));
+ }
+ case_unref (c);
+ }
-static void populate_descriptives (struct tab_table *t, int col, int row,
- const struct variable *,
- const struct metrics *fs);
+ for (v = 0; v < n_dependent_vars; ++v)
+ {
+ struct casewriter *writer;
+ struct casereader *input = casereader_clone (reader);
+
+ result->metrics[v].moments = moments1_create (MOMENT_KURTOSIS);
+ result->metrics[v].minima = extrema_create (n_extrema, EXTREME_MINIMA);
+ result->metrics[v].maxima = extrema_create (n_extrema, EXTREME_MAXIMA);
+ result->metrics[v].cmin = DBL_MAX;
+
+ if (cmd->a_statistics[XMN_ST_DESCRIPTIVES] ||
+ cmd->a_plot[XMN_PLT_BOXPLOT] ||
+ cmd->a_plot[XMN_PLT_NPPLOT] ||
+ cmd->sbc_percentiles)
+ {
+ /* In this case, we need to sort the data, so we create a sorting
+ casewriter */
+ struct subcase up_ordering;
+ subcase_init_var (&up_ordering, dependent_vars[v], SC_ASCEND);
+ writer = sort_create_writer (&up_ordering,
+ casereader_get_value_cnt (reader));
+ subcase_destroy (&up_ordering);
+ }
+ else
+ {
+ /* but in this case, sorting is unnecessary, so an ordinary
+ casewriter is sufficient */
+ writer =
+ autopaging_writer_create (casereader_get_value_cnt (reader));
+ }
-static void populate_extremes (struct tab_table *t, int col, int row, int n,
- const struct variable *var,
- const struct metrics *m);
-static void populate_summary (struct tab_table *t, int col, int row,
- const struct dictionary *dict,
- const struct metrics *m);
+ /* Sort or just iterate, whilst calculating moments etc */
+ while ((c = casereader_read (input)) != NULL)
+ {
+ const casenumber loc =
+ case_data_idx (c, casereader_get_value_cnt (reader) - 1)->f;
+ const double weight = wv ? case_data (c, wv)->f : 1.0;
+ const union value *value = case_data (c, dependent_vars[v]);
+ if (weight != SYSMIS)
+ minimize (&result->metrics[v].cmin, weight);
+ moments1_add (result->metrics[v].moments,
+ value->f,
+ weight);
-/* Perform calculations for the sub factors */
-void
-factor_calc (const struct ccase *c, int case_no, double weight,
- bool case_missing)
-{
- size_t v;
- struct factor *fctr = factors;
+ result->metrics[v].n += weight;
- while ( fctr)
- {
- struct factor_statistics **foo ;
- union value *indep_vals[2] ;
+ if ( ! var_is_value_missing (dependent_vars[v], value, MV_ANY) )
+ result->metrics[v].n_valid += weight;
- indep_vals[0] = value_dup (
- case_data (c, fctr->indep_var[0]),
- var_get_width (fctr->indep_var[0])
- );
+ extrema_add (result->metrics[v].maxima,
+ value->f,
+ weight,
+ loc);
- if ( fctr->indep_var[1] )
- indep_vals[1] = value_dup (
- case_data (c, fctr->indep_var[1]),
- var_get_width (fctr->indep_var[1])
- );
- else
- {
- const union value sm = {SYSMIS};
- indep_vals[1] = value_dup (&sm, 0);
+ extrema_add (result->metrics[v].minima,
+ value->f,
+ weight,
+ loc);
+
+ casewriter_write (writer, c);
}
+ casereader_destroy (input);
+ result->metrics[v].up_reader = casewriter_make_reader (writer);
+ }
- assert (fctr->fstats);
+ /* If percentiles or descriptives have been requested, then a
+ second pass through the data (which has now been sorted)
+ is necessary */
+ if ( cmd->a_statistics[XMN_ST_DESCRIPTIVES] ||
+ cmd->a_plot[XMN_PLT_BOXPLOT] ||
+ cmd->a_plot[XMN_PLT_NPPLOT] ||
+ cmd->sbc_percentiles)
+ {
+ for (v = 0; v < n_dependent_vars; ++v)
+ {
+ int i;
+ int n_os;
+ struct order_stats **os ;
+ struct factor_metrics *metric = &result->metrics[v];
- foo = ( struct factor_statistics ** )
- hsh_probe (fctr->fstats, (void *) &indep_vals);
+ metric->n_ptiles = percentile_list.n_data;
- if ( !*foo )
- {
+ metric->ptl = xcalloc (metric->n_ptiles,
+ sizeof (struct percentile *));
- *foo = create_factor_statistics (n_dependent_vars,
- indep_vals[0],
- indep_vals[1]);
+ metric->quartiles = xcalloc (3, sizeof (*metric->quartiles));
- for ( v = 0 ; v < n_dependent_vars ; ++v )
+ for (i = 0 ; i < metric->n_ptiles; ++i)
{
- metrics_precalc ( & (*foo)->m[v] );
+ metric->ptl[i] = (struct percentile *)
+ percentile_create (percentile_list.data[i] / 100.0, metric->n_valid);
+
+ if ( percentile_list.data[i] == 25)
+ metric->quartiles[0] = metric->ptl[i];
+ else if ( percentile_list.data[i] == 50)
+ metric->quartiles[1] = metric->ptl[i];
+ else if ( percentile_list.data[i] == 75)
+ metric->quartiles[2] = metric->ptl[i];
}
- }
- else
- {
- free (indep_vals[0]);
- free (indep_vals[1]);
- }
+ metric->tukey_hinges = tukey_hinges_create (metric->n, metric->cmin);
+ metric->trimmed_mean = trimmed_mean_create (metric->n, 0.05);
- for ( v = 0 ; v < n_dependent_vars ; ++v )
- {
- const struct variable *var = dependent_vars[v];
- union value *val = value_dup (
- case_data (c, var),
- var_get_width (var)
- );
+ n_os = metric->n_ptiles + 2;
- if (case_missing || var_is_value_missing (var, val, exclude_values))
+ if ( cmd->a_plot[XMN_PLT_NPPLOT] )
{
- free (val);
- val = NULL;
+ metric->np = np_create (metric->moments);
+ n_os ++;
}
- metrics_calc ( & (*foo)->m[v], val, weight, case_no);
+ os = xcalloc (sizeof (struct order_stats *), n_os);
- free (val);
- }
+ for (i = 0 ; i < metric->n_ptiles ; ++i )
+ {
+ os[i] = (struct order_stats *) metric->ptl[i];
+ }
- fctr = fctr->next;
- }
-}
+ os[i] = (struct order_stats *) metric->tukey_hinges;
+ os[i+1] = (struct order_stats *) metric->trimmed_mean;
-static void
-run_examine (struct cmd_examine *cmd, struct casereader *input,
- struct dataset *ds)
-{
- struct dictionary *dict = dataset_dict (ds);
- casenumber case_no;
- struct ccase c;
- int v;
- bool ok;
+ if (cmd->a_plot[XMN_PLT_NPPLOT])
+ os[i+2] = metric->np;
- struct factor *fctr;
-
- if (!casereader_peek (input, 0, &c))
- {
- casereader_destroy (input);
- return;
+ order_stats_accumulate (os, n_os,
+ casereader_clone (metric->up_reader),
+ wv, dependent_vars[v], MV_ANY);
+ free (os);
+ }
}
- output_split_file_values (ds, &c);
- case_destroy (&c);
-
- input = casereader_create_filter_weight (input, dict, NULL, NULL);
- input = casereader_create_counter (input, &case_no, 0);
- /* Make sure we haven't got rubbish left over from a
- previous split. */
- fctr = factors;
- while (fctr)
+ /* FIXME: Do this in the above loop */
+ if ( cmd->a_plot[XMN_PLT_HISTOGRAM] )
{
- struct factor *next = fctr->next;
+ struct ccase *c;
+ struct casereader *input = casereader_clone (reader);
- hsh_clear (fctr->fstats);
+ for (v = 0; v < n_dependent_vars; ++v)
+ {
+ const struct extremum *max, *min;
+ struct factor_metrics *metric = &result->metrics[v];
- fctr->fs = 0;
+ const struct ll_list *max_list =
+ extrema_list (result->metrics[v].maxima);
- fctr = next;
- }
+ const struct ll_list *min_list =
+ extrema_list (result->metrics[v].minima);
- for ( v = 0 ; v < n_dependent_vars ; ++v )
- metrics_precalc (&totals[v]);
+ if ( ll_is_empty (max_list))
+ {
+ msg (MW, _("Not creating plot because data set is empty."));
+ continue;
+ }
- for (; casereader_read (input, &c); case_destroy (&c))
- {
- bool case_missing = false;
- const double weight = dict_get_case_weight (dict, &c, NULL);
+ assert (! ll_is_empty (min_list));
- if ( cmd->miss == XMN_LISTWISE )
- {
- for ( v = 0 ; v < n_dependent_vars ; ++v )
- {
- const struct variable *var = dependent_vars[v];
- union value *val = value_dup (
- case_data (&c, var),
- var_get_width (var)
- );
+ max = (const struct extremum *)
+ ll_data (ll_head(max_list), struct extremum, ll);
- if ( var_is_value_missing (var, val, exclude_values))
- case_missing = true;
+ min = (const struct extremum *)
+ ll_data (ll_head (min_list), struct extremum, ll);
- free (val);
- }
+ metric->histogram = histogram_create (10, min->value, max->value);
}
- for ( v = 0 ; v < n_dependent_vars ; ++v )
+ while ((c = casereader_read (input)) != NULL)
{
- const struct variable *var = dependent_vars[v];
- union value *val = value_dup (
- case_data (&c, var),
- var_get_width (var)
- );
-
- if ( var_is_value_missing (var, val, exclude_values)
- || case_missing )
+ const double weight = wv ? case_data (c, wv)->f : 1.0;
+
+ for (v = 0; v < n_dependent_vars; ++v)
{
- free (val) ;
- val = NULL;
+ struct factor_metrics *metric = &result->metrics[v];
+ if ( metric->histogram)
+ histogram_add ((struct histogram *) metric->histogram,
+ case_data (c, dependent_vars[v])->f, weight);
}
-
- metrics_calc (&totals[v], val, weight, case_no);
-
- free (val);
+ case_unref (c);
}
-
- factor_calc (&c, case_no, weight, case_missing);
+ casereader_destroy (input);
}
- ok = casereader_destroy (input);
- for ( v = 0 ; v < n_dependent_vars ; ++v)
+ /* In this case, a third iteration is required */
+ if (cmd->a_plot[XMN_PLT_BOXPLOT])
{
- fctr = factors;
- while ( fctr )
+ for (v = 0; v < n_dependent_vars; ++v)
{
- struct hsh_iterator hi;
- struct factor_statistics *fs;
+ struct factor_metrics *metric = &result->metrics[v];
+
+ metric->box_whisker =
+ box_whisker_create ((struct tukey_hinges *) metric->tukey_hinges,
+ cmd->v_id,
+ casereader_get_value_cnt (metric->up_reader)
+ - 1);
+
+ order_stats_accumulate ((struct order_stats **) &metric->box_whisker,
+ 1,
+ casereader_clone (metric->up_reader),
+ wv, dependent_vars[v], MV_ANY);
+ }
+ }
- for ( fs = hsh_first (fctr->fstats, &hi);
- fs != 0 ;
- fs = hsh_next (fctr->fstats, &hi))
- {
+ ll_push_tail (&factor->result_list, &result->ll);
+ casereader_destroy (reader);
+}
- fs->m[v].ptile_hash = list_to_ptile_hash (&percentile_list);
- fs->m[v].ptile_alg = percentile_algorithm;
- metrics_postcalc (&fs->m[v]);
- }
- fctr = fctr->next;
- }
+static void
+run_examine (struct cmd_examine *cmd, struct casereader *input,
+ struct dataset *ds)
+{
+ struct ll *ll;
+ const struct dictionary *dict = dataset_dict (ds);
+ struct ccase *c;
+ struct casereader *level0 = casereader_clone (input);
- totals[v].ptile_hash = list_to_ptile_hash (&percentile_list);
- totals[v].ptile_alg = percentile_algorithm;
- metrics_postcalc (&totals[v]);
+ c = casereader_peek (input, 0);
+ if (c == NULL)
+ {
+ casereader_destroy (input);
+ return;
}
+ output_split_file_values (ds, c);
+ case_unref (c);
- /* Make sure that the combination of factors are complete */
-
- fctr = factors;
- while ( fctr )
- {
- struct hsh_iterator hi;
- struct hsh_iterator hi0;
- struct hsh_iterator hi1;
- struct factor_statistics *fs;
+ ll_init (&level0_factor.result_list);
- struct hsh_table *idh0 = NULL;
- struct hsh_table *idh1 = NULL;
- union value **val0;
- union value **val1;
+ examine_group (cmd, level0, 0, dict, &level0_factor);
- idh0 = hsh_create (4, (hsh_compare_func *) compare_ptr_values,
- (hsh_hash_func *) hash_ptr_value,
- 0,0);
+ for (ll = ll_head (&factor_list);
+ ll != ll_null (&factor_list);
+ ll = ll_next (ll))
+ {
+ struct xfactor *factor = ll_data (ll, struct xfactor, ll);
- idh1 = hsh_create (4, (hsh_compare_func *) compare_ptr_values,
- (hsh_hash_func *) hash_ptr_value,
- 0,0);
+ struct casereader *group = NULL;
+ struct casereader *level1;
+ struct casegrouper *grouper1 = NULL;
+ level1 = casereader_clone (input);
+ level1 = sort_execute_1var (level1, factor->indep_var[0]);
+ grouper1 = casegrouper_create_vars (level1, &factor->indep_var[0], 1);
- for ( fs = hsh_first (fctr->fstats, &hi);
- fs != 0 ;
- fs = hsh_next (fctr->fstats, &hi))
+ while (casegrouper_get_next_group (grouper1, &group))
{
- hsh_insert (idh0, &fs->id[0]);
- hsh_insert (idh1, &fs->id[1]);
- }
+ struct casereader *group_copy = casereader_clone (group);
- /* Ensure that the factors combination is complete */
- for ( val0 = hsh_first (idh0, &hi0);
- val0 != 0 ;
- val0 = hsh_next (idh0, &hi0))
- {
- for ( val1 = hsh_first (idh1, &hi1);
- val1 != 0 ;
- val1 = hsh_next (idh1, &hi1))
+ if ( !factor->indep_var[1])
+ examine_group (cmd, group_copy, 1, dict, factor);
+ else
{
- struct factor_statistics **ffs;
- union value *key[2];
- key[0] = *val0;
- key[1] = *val1;
-
- ffs = (struct factor_statistics **)
- hsh_probe (fctr->fstats, &key );
-
- if ( !*ffs ) {
- size_t i;
- (*ffs) = create_factor_statistics (n_dependent_vars,
- key[0], key[1]);
- for ( i = 0 ; i < n_dependent_vars ; ++i )
- metrics_precalc ( & (*ffs)->m[i]);
- }
- }
- }
+ int n_groups = 0;
+ struct casereader *group2 = NULL;
+ struct casegrouper *grouper2 = NULL;
- hsh_destroy (idh0);
- hsh_destroy (idh1);
+ group_copy = sort_execute_1var (group_copy,
+ factor->indep_var[1]);
- fctr->fs = (struct factor_statistics **) hsh_sort_copy (fctr->fstats);
+ grouper2 = casegrouper_create_vars (group_copy,
+ &factor->indep_var[1], 1);
+
+ while (casegrouper_get_next_group (grouper2, &group2))
+ {
+ examine_group (cmd, group2, 2, dict, factor);
+ n_groups++;
+ }
+ casegrouper_destroy (grouper2);
+ }
- fctr = fctr->next;
+ casereader_destroy (group);
+ }
+ casegrouper_destroy (grouper1);
}
- if (ok)
- output_examine (dict);
+ casereader_destroy (input);
- output_examine ();
++ output_examine (dict);
+
+ factor_destroy (&level0_factor);
+
+ {
+ struct ll *ll;
+ for (ll = ll_head (&factor_list);
+ ll != ll_null (&factor_list);
+ ll = ll_next (ll))
+ {
+ struct xfactor *f = ll_data (ll, struct xfactor, ll);
+ factor_destroy (f);
+ }
+ }
- if ( totals )
- {
- size_t i;
- for ( i = 0 ; i < n_dependent_vars ; ++i )
- {
- metrics_destroy (&totals[i]);
- }
- }
}
static void
show_summary (const struct variable **dependent_var, int n_dep_var,
- const struct factor *fctr)
+ const struct dictionary *dict,
+ const struct xfactor *fctr)
{
++ const struct variable *wv = dict_get_weight (dict);
++ const struct fmt_spec *wfmt = wv ? var_get_print_format (wv) : & F_8_0;
++
static const char *subtitle[]=
{
- N_ ("Valid"),
- N_ ("Missing"),
- N_ ("Total")
+ N_("Valid"),
+ N_("Missing"),
+ N_("Total")
};
- int i;
- int heading_columns ;
+ int v, j;
+ int heading_columns = 1;
int n_cols;
const int heading_rows = 3;
struct tab_table *tbl;
}
}
-
- for ( i = 0 ; i < n_dep_var ; ++i )
+ for (v = 0 ; v < n_dep_var ; ++v)
{
- int n_factors = 1;
- if ( fctr )
- n_factors = hsh_count (fctr->fstats);
+ int j = 0;
+ struct ll *ll;
+ union value *last_value = NULL;
- if ( i > 0 )
- tab_hline (tbl, TAL_1, 0, n_cols -1 , i * n_factors + heading_rows);
+ if ( v > 0 )
+ tab_hline (tbl, TAL_1, 0, n_cols -1 ,
+ v * ll_count (&fctr->result_list)
+ + heading_rows);
tab_text (tbl,
- 0, i * n_factors + heading_rows,
+ 0,
+ v * ll_count (&fctr->result_list) + heading_rows,
TAB_LEFT | TAT_TITLE,
- var_to_string (dependent_var[i])
+ var_to_string (dependent_var[v])
);
- if ( !fctr )
- populate_summary (tbl, heading_columns,
- (i * n_factors) + heading_rows,
- dict,
- &totals[i]);
- else
+
+ for (ll = ll_head (&fctr->result_list);
+ ll != ll_null (&fctr->result_list); ll = ll_next (ll))
{
- struct factor_statistics **fs = fctr->fs;
- int count = 0 ;
- const union value *prev = NULL;
+ double n;
+ const struct factor_result *result =
+ ll_data (ll, struct factor_result, ll);
- while (*fs)
+ if ( fctr->indep_var[0] )
{
- if ( !prev ||
- 0 != compare_values (prev, (*fs)->id[0],
- var_get_width (fctr->indep_var[0])))
+
+ if ( last_value == NULL ||
+ compare_values_short (last_value, result->value[0],
+ fctr->indep_var[0]))
{
- struct string vstr;
- ds_init_empty (&vstr);
- var_append_value_name (fctr->indep_var[0],
- (*fs)->id[0], &vstr);
-
- tab_text (tbl,
- 1,
- (i * n_factors ) + count +
- heading_rows,
+ struct string str;
+
+ last_value = result->value[0];
+ ds_init_empty (&str);
+
+ var_append_value_name (fctr->indep_var[0], result->value[0],
+ &str);
+
+ tab_text (tbl, 1,
+ heading_rows + j +
+ v * ll_count (&fctr->result_list),
TAB_LEFT | TAT_TITLE,
- ds_cstr (&vstr)
- );
+ ds_cstr (&str));
- ds_destroy (&vstr);
+ ds_destroy (&str);
- if (fctr->indep_var[1] && count > 0 )
+ if ( fctr->indep_var[1] && j > 0)
tab_hline (tbl, TAL_1, 1, n_cols - 1,
- (i * n_factors ) + count + heading_rows);
+ heading_rows + j +
+ v * ll_count (&fctr->result_list));
}
- prev = (*fs)->id[0];
-
if ( fctr->indep_var[1])
{
- struct string vstr;
- ds_init_empty (&vstr);
+ struct string str;
+
+ ds_init_empty (&str);
+
var_append_value_name (fctr->indep_var[1],
- (*fs)->id[1], &vstr);
- tab_text (tbl,
- 2,
- (i * n_factors ) + count +
- heading_rows,
+ result->value[1], &str);
+
+ tab_text (tbl, 2,
+ heading_rows + j +
+ v * ll_count (&fctr->result_list),
TAB_LEFT | TAT_TITLE,
- ds_cstr (&vstr)
- );
- ds_destroy (&vstr);
+ ds_cstr (&str));
+
+ ds_destroy (&str);
}
+ }
- populate_summary (tbl, heading_columns,
- (i * n_factors) + count
- + heading_rows,
- dict,
- & (*fs)->m[i]);
- count++ ;
- fs++;
- }
+ moments1_calculate (result->metrics[v].moments,
+ &n, &result->metrics[v].mean,
+ &result->metrics[v].variance,
+ &result->metrics[v].skewness,
+ &result->metrics[v].kurtosis);
+
+ result->metrics[v].se_mean = sqrt (result->metrics[v].variance / n) ;
+
+ /* Total Valid */
- tab_float (tbl, heading_columns,
++ tab_double (tbl, heading_columns,
+ heading_rows + j + v * ll_count (&fctr->result_list),
+ TAB_LEFT,
- n, 8, 0);
++ n, wfmt);
+
+ tab_text (tbl, heading_columns + 1,
+ heading_rows + j + v * ll_count (&fctr->result_list),
+ TAB_RIGHT | TAT_PRINTF,
+ "%g%%", n * 100.0 / result->metrics[v].n);
+
+ /* Total Missing */
- tab_float (tbl, heading_columns + 2,
++ tab_double (tbl, heading_columns + 2,
+ heading_rows + j + v * ll_count (&fctr->result_list),
+ TAB_LEFT,
+ result->metrics[v].n - n,
- 8, 0);
++ wfmt);
+
+ tab_text (tbl, heading_columns + 3,
+ heading_rows + j + v * ll_count (&fctr->result_list),
+ TAB_RIGHT | TAT_PRINTF,
+ "%g%%",
+ (result->metrics[v].n - n) * 100.0 / result->metrics[v].n
+ );
+
+ /* Total Valid + Missing */
- tab_float (tbl, heading_columns + 4,
++ tab_double (tbl, heading_columns + 4,
+ heading_rows + j + v * ll_count (&fctr->result_list),
+ TAB_LEFT,
+ result->metrics[v].n,
- 8, 0);
++ wfmt);
+
+ tab_text (tbl, heading_columns + 5,
+ heading_rows + j + v * ll_count (&fctr->result_list),
+ TAB_RIGHT | TAT_PRINTF,
+ "%g%%",
+ (result->metrics[v].n) * 100.0 / result->metrics[v].n
+ );
+
+ ++j;
}
}
0, 0,
n_cols - 1, n_rows - 1);
- tab_hline (tbl, TAL_2, 0, n_cols - 1, heading_rows );
- tab_title (tbl, _ ("Extreme Values"));
+ tab_hline (tbl, TAL_2, 0, n_cols - 1, heading_rows );
+ tab_hline (tbl, TAL_2, 1, n_cols - 1, heading_rows );
- tab_vline (tbl, TAL_2, n_cols - 2, 0, n_rows -1);
- tab_vline (tbl, TAL_1, n_cols - 1, 0, n_rows -1);
+ tab_vline (tbl, TAL_1, n_cols - 1, 0, n_rows - 1);
- if ( fctr )
- {
- tab_text (tbl, 1, heading_rows - 1, TAB_CENTER | TAT_TITLE,
- var_to_string (fctr->indep_var[0]));
- if ( fctr->indep_var[1] )
- tab_text (tbl, 2, heading_rows - 1, TAB_CENTER | TAT_TITLE,
- var_to_string (fctr->indep_var[1]));
- }
+ if ( fctr->indep_var[0])
+ tab_text (tbl, 1, 0, TAT_TITLE, var_to_string (fctr->indep_var[0]));
- tab_text (tbl, n_cols - 1, 0, TAB_CENTER | TAT_TITLE, _ ("Value"));
- tab_text (tbl, n_cols - 2, 0, TAB_CENTER | TAT_TITLE, _ ("Case Number"));
+ if ( fctr->indep_var[1])
+ tab_text (tbl, 2, 0, TAT_TITLE, var_to_string (fctr->indep_var[1]));
- for ( i = 0 ; i < n_dep_var ; ++i )
+ for (v = 0 ; v < n_dep_var ; ++v )
{
+ struct ll *ll;
+ int i = 0;
- if ( i > 0 )
- tab_hline (tbl, TAL_1, 0, n_cols -1 ,
- i * 2 * n_extremities * n_factors + heading_rows);
+ const int row_var_start =
+ v * DESCRIPTIVE_ROWS * ll_count(&fctr->result_list);
- tab_text (tbl, 0,
- i * 2 * n_extremities * n_factors + heading_rows,
+ tab_text (tbl,
+ 0,
+ heading_rows + row_var_start,
TAB_LEFT | TAT_TITLE,
- var_to_string (dependent_var[i])
+ var_to_string (dependent_var[v])
);
-
- if ( !fctr )
- populate_extremes (tbl, heading_columns - 2,
- i * 2 * n_extremities * n_factors + heading_rows,
- n_extremities,
- dependent_var[i],
- &totals[i]);
- else
+ for (ll = ll_head (&fctr->result_list);
+ ll != ll_null (&fctr->result_list); i++, ll = ll_next (ll))
{
- struct factor_statistics **fs = fctr->fs;
- int count = 0 ;
- const union value *prev = NULL;
-
- while (*fs)
- {
- const int row = heading_rows + ( 2 * n_extremities ) *
- ( ( i * n_factors ) + count );
-
-
- if ( !prev || 0 != compare_values (prev, (*fs)->id[0],
- var_get_width (fctr->indep_var[0])))
- {
- struct string vstr;
- ds_init_empty (&vstr);
- var_append_value_name (fctr->indep_var[0],
- (*fs)->id[0], &vstr);
-
- if ( count > 0 )
- tab_hline (tbl, TAL_1, 1, n_cols - 1, row);
-
- tab_text (tbl,
- 1, row,
- TAB_LEFT | TAT_TITLE,
- ds_cstr (&vstr)
- );
-
- ds_destroy (&vstr);
- }
-
- prev = (*fs)->id[0];
-
- if (fctr->indep_var[1] && count > 0 )
- tab_hline (tbl, TAL_1, 2, n_cols - 1, row);
-
- if ( fctr->indep_var[1])
- {
- struct string vstr;
- ds_init_empty (&vstr);
- var_append_value_name (fctr->indep_var[1], (*fs)->id[1], &vstr);
-
- tab_text (tbl, 2, row,
- TAB_LEFT | TAT_TITLE,
- ds_cstr (&vstr)
- );
-
- ds_destroy (&vstr);
- }
+ const struct factor_result *result =
+ ll_data (ll, struct factor_result, ll);
- populate_extremes (tbl, heading_columns - 2,
- row, n_extremities,
- dependent_var[i],
- & (*fs)->m[i]);
+ const double t =
+ gsl_cdf_tdist_Qinv ((1 - cmd.n_cinterval[0] / 100.0) / 2.0,
+ result->metrics[v].n - 1);
- count++ ;
- fs++;
+ if ( i > 0 || v > 0 )
+ {
+ const int left_col = (i == 0) ? 0 : 1;
+ tab_hline (tbl, TAL_1, left_col, n_cols - 1,
+ heading_rows + row_var_start + i * DESCRIPTIVE_ROWS);
}
- }
- }
-
- tab_submit (tbl);
-}
-
-
-/* Fill in the extremities table */
-static void
-populate_extremes (struct tab_table *t,
- int col, int row, int n,
- const struct variable *var,
- const struct metrics *m)
-{
- int extremity;
- int idx=0;
-
- tab_text (t, col, row,
- TAB_RIGHT | TAT_TITLE ,
- _ ("Highest")
- );
-
- tab_text (t, col, row + n ,
- TAB_RIGHT | TAT_TITLE ,
- _ ("Lowest")
- );
-
-
- tab_hline (t, TAL_1, col, col + 3, row + n );
-
- for (extremity = 0; extremity < n ; ++extremity )
- {
- /* Highest */
- tab_fixed (t, col + 1, row + extremity,
- TAB_RIGHT,
- extremity + 1, 8, 0);
-
-
- /* Lowest */
- tab_fixed (t, col + 1, row + extremity + n,
- TAB_RIGHT,
- extremity + 1, 8, 0);
-
- }
-
-
- /* Lowest */
- for (idx = 0, extremity = 0; extremity < n && idx < m->n_data ; ++idx )
- {
- int j;
- const struct weighted_value *wv = m->wvp[idx];
- struct case_node *cn = wv->case_nos;
-
-
- for (j = 0 ; j < wv->w ; ++j )
- {
- if ( extremity + j >= n )
- break ;
-
- tab_value (t, col + 3, row + extremity + j + n,
- TAB_RIGHT,
- &wv->v, var_get_print_format (var));
+ if ( fctr->indep_var[0])
+ {
+ struct string vstr;
+ ds_init_empty (&vstr);
+ var_append_value_name (fctr->indep_var[0],
+ result->value[0], &vstr);
+
+ tab_text (tbl, 1,
+ heading_rows + row_var_start + i * DESCRIPTIVE_ROWS,
+ TAB_LEFT,
+ ds_cstr (&vstr)
+ );
- tab_fixed (t, col + 2, row + extremity + j + n,
- TAB_RIGHT,
- cn->num, 10, 0);
+ ds_destroy (&vstr);
+ }
- if ( cn->next )
- cn = cn->next;
- tab_float (tbl, n_cols - 2,
+ tab_text (tbl, n_cols - 4,
+ heading_rows + row_var_start + i * DESCRIPTIVE_ROWS,
+ TAB_LEFT,
+ _("Mean"));
+
+ tab_text (tbl, n_cols - 4,
+ heading_rows + row_var_start + 1 + i * DESCRIPTIVE_ROWS,
+ TAB_LEFT | TAT_PRINTF,
+ _("%g%% Confidence Interval for Mean"),
+ cmd.n_cinterval[0]);
+
+ tab_text (tbl, n_cols - 3,
+ heading_rows + row_var_start + 1 + i * DESCRIPTIVE_ROWS,
+ TAB_LEFT,
+ _("Lower Bound"));
+
+ tab_text (tbl, n_cols - 3,
+ heading_rows + row_var_start + 2 + i * DESCRIPTIVE_ROWS,
+ TAB_LEFT,
+ _("Upper Bound"));
+
+ tab_text (tbl, n_cols - 4,
+ heading_rows + row_var_start + 3 + i * DESCRIPTIVE_ROWS,
+ TAB_LEFT | TAT_PRINTF,
+ _("5%% Trimmed Mean"));
+
+ tab_text (tbl, n_cols - 4,
+ heading_rows + row_var_start + 4 + i * DESCRIPTIVE_ROWS,
+ TAB_LEFT,
+ _("Median"));
+
+ tab_text (tbl, n_cols - 4,
+ heading_rows + row_var_start + 5 + i * DESCRIPTIVE_ROWS,
+ TAB_LEFT,
+ _("Variance"));
+
+ tab_text (tbl, n_cols - 4,
+ heading_rows + row_var_start + 6 + i * DESCRIPTIVE_ROWS,
+ TAB_LEFT,
+ _("Std. Deviation"));
+
+ tab_text (tbl, n_cols - 4,
+ heading_rows + row_var_start + 7 + i * DESCRIPTIVE_ROWS,
+ TAB_LEFT,
+ _("Minimum"));
+
+ tab_text (tbl, n_cols - 4,
+ heading_rows + row_var_start + 8 + i * DESCRIPTIVE_ROWS,
+ TAB_LEFT,
+ _("Maximum"));
+
+ tab_text (tbl, n_cols - 4,
+ heading_rows + row_var_start + 9 + i * DESCRIPTIVE_ROWS,
+ TAB_LEFT,
+ _("Range"));
+
+ tab_text (tbl, n_cols - 4,
+ heading_rows + row_var_start + 10 + i * DESCRIPTIVE_ROWS,
+ TAB_LEFT,
+ _("Interquartile Range"));
+
+
+ tab_text (tbl, n_cols - 4,
+ heading_rows + row_var_start + 11 + i * DESCRIPTIVE_ROWS,
+ TAB_LEFT,
+ _("Skewness"));
+
+ tab_text (tbl, n_cols - 4,
+ heading_rows + row_var_start + 12 + i * DESCRIPTIVE_ROWS,
+ TAB_LEFT,
+ _("Kurtosis"));
+
+
+ /* Now the statistics ... */
+
- 8, 2);
++ tab_double (tbl, n_cols - 2,
+ heading_rows + row_var_start + i * DESCRIPTIVE_ROWS,
+ TAB_CENTER,
+ result->metrics[v].mean,
- tab_float (tbl, n_cols - 1,
++ NULL);
+
- 8, 3);
++ tab_double (tbl, n_cols - 1,
+ heading_rows + row_var_start + i * DESCRIPTIVE_ROWS,
+ TAB_CENTER,
+ result->metrics[v].se_mean,
- tab_float (tbl, n_cols - 2,
++ NULL);
+
+
- 8, 3);
++ tab_double (tbl, n_cols - 2,
+ heading_rows + row_var_start + 1 + i * DESCRIPTIVE_ROWS,
+ TAB_CENTER,
+ result->metrics[v].mean - t *
+ result->metrics[v].se_mean,
- tab_float (tbl, n_cols - 2,
++ NULL);
+
- 8, 3);
++ tab_double (tbl, n_cols - 2,
+ heading_rows + row_var_start + 2 + i * DESCRIPTIVE_ROWS,
+ TAB_CENTER,
+ result->metrics[v].mean + t *
+ result->metrics[v].se_mean,
- tab_float (tbl, n_cols - 2,
++ NULL);
+
+
- 8, 2);
++ tab_double (tbl, n_cols - 2,
+ heading_rows + row_var_start + 3 + i * DESCRIPTIVE_ROWS,
+ TAB_CENTER,
+ trimmed_mean_calculate ((struct trimmed_mean *) result->metrics[v].trimmed_mean),
- tab_float (tbl, n_cols - 2,
++ NULL);
+
+
- 8, 2);
++ tab_double (tbl, n_cols - 2,
+ heading_rows + row_var_start + 4 + i * DESCRIPTIVE_ROWS,
+ TAB_CENTER,
+ percentile_calculate (result->metrics[v].quartiles[1], percentile_algorithm),
- tab_float (tbl, n_cols - 2,
++ NULL);
+
+
- 8, 3);
++ tab_double (tbl, n_cols - 2,
+ heading_rows + row_var_start + 5 + i * DESCRIPTIVE_ROWS,
+ TAB_CENTER,
+ result->metrics[v].variance,
- tab_float (tbl, n_cols - 2,
++ NULL);
+
- 8, 3);
++ tab_double (tbl, n_cols - 2,
+ heading_rows + row_var_start + 6 + i * DESCRIPTIVE_ROWS,
+ TAB_CENTER,
+ sqrt (result->metrics[v].variance),
- tab_float (tbl, n_cols - 2,
++ NULL);
+
- 8, 2);
++ tab_double (tbl, n_cols - 2,
+ heading_rows + row_var_start + 10 + i * DESCRIPTIVE_ROWS,
+ TAB_CENTER,
+ percentile_calculate (result->metrics[v].quartiles[2],
+ percentile_algorithm) -
+ percentile_calculate (result->metrics[v].quartiles[0],
+ percentile_algorithm),
- tab_float (tbl, n_cols - 2,
++ NULL);
+
+
- 8, 3);
++ tab_double (tbl, n_cols - 2,
+ heading_rows + row_var_start + 11 + i * DESCRIPTIVE_ROWS,
+ TAB_CENTER,
+ result->metrics[v].skewness,
- tab_float (tbl, n_cols - 2,
++ NULL);
+
- 8, 3);
++ tab_double (tbl, n_cols - 2,
+ heading_rows + row_var_start + 12 + i * DESCRIPTIVE_ROWS,
+ TAB_CENTER,
+ result->metrics[v].kurtosis,
- tab_float (tbl, n_cols - 1,
++ NULL);
+
- 8, 3);
++ tab_double (tbl, n_cols - 1,
+ heading_rows + row_var_start + 11 + i * DESCRIPTIVE_ROWS,
+ TAB_CENTER,
+ calc_seskew (result->metrics[v].n),
- tab_float (tbl, n_cols - 1,
++ NULL);
+
- 8, 3);
++ tab_double (tbl, n_cols - 1,
+ heading_rows + row_var_start + 12 + i * DESCRIPTIVE_ROWS,
+ TAB_CENTER,
+ calc_sekurt (result->metrics[v].n),
- tab_float (tbl, n_cols - 2,
++ NULL);
+
+ {
+ struct extremum *minimum, *maximum ;
+
+ struct ll *max_ll = ll_head (extrema_list (result->metrics[v].maxima));
+ struct ll *min_ll = ll_head (extrema_list (result->metrics[v].minima));
+
+ maximum = ll_data (max_ll, struct extremum, ll);
+ minimum = ll_data (min_ll, struct extremum, ll);
+
- 8, 3);
++ tab_double (tbl, n_cols - 2,
+ heading_rows + row_var_start + 7 + i * DESCRIPTIVE_ROWS,
+ TAB_CENTER,
+ minimum->value,
- tab_float (tbl, n_cols - 2,
++ NULL);
+
- 8, 3);
++ tab_double (tbl, n_cols - 2,
+ heading_rows + row_var_start + 8 + i * DESCRIPTIVE_ROWS,
+ TAB_CENTER,
+ maximum->value,
- tab_float (tbl, n_cols - 2,
++ NULL);
+
- 8, 3);
++ tab_double (tbl, n_cols - 2,
+ heading_rows + row_var_start + 9 + i * DESCRIPTIVE_ROWS,
+ TAB_CENTER,
+ maximum->value - minimum->value,
++ NULL);
+ }
}
-
- extremity += wv->w ;
}
+ tab_vline (tbl, TAL_2, heading_columns, 0, n_rows - 1);
- /* Highest */
- for (idx = m->n_data - 1, extremity = 0; extremity < n && idx >= 0; --idx )
- {
- int j;
- const struct weighted_value *wv = m->wvp[idx];
- struct case_node *cn = wv->case_nos;
-
- for (j = 0 ; j < wv->w ; ++j )
- {
- if ( extremity + j >= n )
- break ;
-
- tab_value (t, col + 3, row + extremity + j,
- TAB_RIGHT,
- &wv->v, var_get_print_format (var));
-
- tab_fixed (t, col + 2, row + extremity + j,
- TAB_RIGHT,
- cn->num, 10, 0);
+ tab_title (tbl, _("Descriptives"));
- if ( cn->next )
- cn = cn->next;
+ tab_text (tbl, n_cols - 2, 0, TAB_CENTER | TAT_TITLE,
+ _("Statistic"));
- }
+ tab_text (tbl, n_cols - 1, 0, TAB_CENTER | TAT_TITLE,
+ _("Std. Error"));
- extremity += wv->w ;
- }
+ tab_submit (tbl);
}
0, 0,
n_cols - 1, n_rows - 1);
- tab_hline (tbl, TAL_2, 0, n_cols - 1, heading_rows );
- tab_vline (tbl, TAL_1, heading_columns, 0, n_rows - 1);
- tab_vline (tbl, TAL_2, n_cols - 2, 0, n_rows - 1);
+ tab_hline (tbl, TAL_2, 0, n_cols - 1, heading_rows );
+ tab_hline (tbl, TAL_2, 1, n_cols - 1, heading_rows );
tab_vline (tbl, TAL_1, n_cols - 1, 0, n_rows - 1);
- tab_text (tbl, n_cols - 2, 0, TAB_CENTER | TAT_TITLE, _ ("Statistic"));
- tab_text (tbl, n_cols - 1, 0, TAB_CENTER | TAT_TITLE, _ ("Std. Error"));
+ if ( fctr->indep_var[0])
+ tab_text (tbl, 1, 0, TAT_TITLE, var_to_string (fctr->indep_var[0]));
- tab_title (tbl, _ ("Descriptives"));
+ if ( fctr->indep_var[1])
+ tab_text (tbl, 2, 0, TAT_TITLE, var_to_string (fctr->indep_var[1]));
-
- for ( i = 0 ; i < n_dep_var ; ++i )
+ for (v = 0 ; v < n_dep_var ; ++v )
{
- const int row = heading_rows + i * n_stat_rows * n_factors ;
-
- if ( i > 0 )
- tab_hline (tbl, TAL_1, 0, n_cols - 1, row );
+ struct ll *ll;
+ int i = 0;
+ const int row_var_start = v * cmd.st_n * 2 * ll_count(&fctr->result_list);
- tab_text (tbl, 0,
- i * n_stat_rows * n_factors + heading_rows,
+ tab_text (tbl,
+ 0,
+ heading_rows + row_var_start,
TAB_LEFT | TAT_TITLE,
- var_to_string (dependent_var[i])
+ var_to_string (dependent_var[v])
);
-
- if ( fctr )
+ for (ll = ll_head (&fctr->result_list);
+ ll != ll_null (&fctr->result_list); i++, ll = ll_next (ll))
{
- const union value *prev = NULL;
+ int e ;
+ struct ll *min_ll;
+ struct ll *max_ll;
+ const int row_result_start = i * cmd.st_n * 2;
- struct factor_statistics **fs = fctr->fs;
- int count = 0;
+ const struct factor_result *result =
+ ll_data (ll, struct factor_result, ll);
- tab_text (tbl, 1, heading_rows - 1, TAB_CENTER | TAT_TITLE,
- var_to_string (fctr->indep_var[0]));
+ if (i > 0 || v > 0)
+ tab_hline (tbl, TAL_1, 1, n_cols - 1,
+ heading_rows + row_var_start + row_result_start);
+ tab_hline (tbl, TAL_1, heading_columns - 2, n_cols - 1,
+ heading_rows + row_var_start + row_result_start + cmd.st_n);
- if ( fctr->indep_var[1])
- tab_text (tbl, 2, heading_rows - 1, TAB_CENTER | TAT_TITLE,
- var_to_string (fctr->indep_var[1]));
-
- while ( *fs )
+ for ( e = 1; e <= cmd.st_n; ++e )
{
- const int row = heading_rows + n_stat_rows *
- ( ( i * n_factors ) + count );
-
-
- if ( !prev || 0 != compare_values (prev, (*fs)->id[0],
- var_get_width (fctr->indep_var[0])))
- {
- struct string vstr;
- ds_init_empty (&vstr);
- var_append_value_name (fctr->indep_var[0],
- (*fs)->id[0], &vstr);
-
- if ( count > 0 )
- tab_hline (tbl, TAL_1, 1, n_cols - 1, row);
-
- tab_text (tbl,
- 1, row,
- TAB_LEFT | TAT_TITLE,
- ds_cstr (&vstr)
- );
-
- ds_destroy (&vstr);
- }
+ tab_text (tbl, n_cols - 3,
+ heading_rows + row_var_start + row_result_start + e - 1,
+ TAB_RIGHT | TAT_PRINTF,
+ _("%d"), e);
+
+ tab_text (tbl, n_cols - 3,
+ heading_rows + row_var_start + row_result_start + cmd.st_n + e - 1,
+ TAB_RIGHT | TAT_PRINTF,
+ _("%d"), e);
+ }
- prev = (*fs)->id[0];
- if (fctr->indep_var[1] && count > 0 )
- tab_hline (tbl, TAL_1, 2, n_cols - 1, row);
+ min_ll = ll_head (extrema_list (result->metrics[v].minima));
+ for (e = 0; e < cmd.st_n;)
+ {
+ struct extremum *minimum = ll_data (min_ll, struct extremum, ll);
+ double weight = minimum->weight;
- if ( fctr->indep_var[1])
+ while (weight-- > 0 && e < cmd.st_n)
{
- tab_float (tbl, n_cols - 1,
- struct string vstr;
- ds_init_empty (&vstr);
- var_append_value_name (fctr->indep_var[1], (*fs)->id[1], &vstr);
-
- tab_text (tbl, 2, row,
- TAB_LEFT | TAT_TITLE,
- ds_cstr (&vstr)
- );
-
- ds_destroy (&vstr);
++ tab_double (tbl, n_cols - 1,
+ heading_rows + row_var_start + row_result_start + cmd.st_n + e,
+ TAB_RIGHT,
+ minimum->value,
- 8, 2);
++ NULL);
+
+
- tab_float (tbl, n_cols - 2,
- heading_rows + row_var_start + row_result_start + cmd.st_n + e,
++ tab_fixed (tbl, n_cols - 2,
++ heading_rows + row_var_start +
++ row_result_start + cmd.st_n + e,
+ TAB_RIGHT,
+ minimum->location,
- 8, 0);
++ 10, 0);
+ ++e;
}
- populate_descriptives (tbl, heading_columns - 2,
- row,
- dependent_var[i],
- & (*fs)->m[i]);
-
- count++ ;
- fs++;
+ min_ll = ll_next (min_ll);
}
- }
-
- else
- {
-
- populate_descriptives (tbl, heading_columns - 2,
- i * n_stat_rows * n_factors + heading_rows,
- dependent_var[i],
- &totals[i]);
- }
- }
-
- tab_submit (tbl);
-}
-
-/* Fill in the descriptives data */
-static void
-populate_descriptives (struct tab_table *tbl, int col, int row,
- const struct variable *var,
- const struct metrics *m)
-{
- const double t = gsl_cdf_tdist_Qinv ((1 - cmd.n_cinterval[0] / 100.0)/2.0,
- m->n -1);
-
- tab_text (tbl, col,
- row,
- TAB_LEFT | TAT_TITLE,
- _ ("Mean"));
-
- tab_double (tbl, col + 2,
- row,
- TAB_CENTER,
- m->mean,
- NULL);
-
- tab_double (tbl, col + 3,
- row,
- TAB_CENTER,
- m->se_mean,
- NULL);
-
-
- tab_text (tbl, col,
- row + 1,
- TAB_LEFT | TAT_TITLE | TAT_PRINTF,
- _ ("%g%% Confidence Interval for Mean"), cmd.n_cinterval[0]);
-
-
- tab_text (tbl, col + 1,
- row + 1,
- TAB_LEFT | TAT_TITLE,
- _ ("Lower Bound"));
-
- tab_double (tbl, col + 2,
- row + 1,
- TAB_CENTER,
- m->mean - t * m->se_mean,
- NULL);
-
- tab_text (tbl, col + 1,
- row + 2,
- TAB_LEFT | TAT_TITLE,
- _ ("Upper Bound"));
-
-
- tab_double (tbl, col + 2,
- row + 2,
- TAB_CENTER,
- m->mean + t * m->se_mean,
- NULL);
-
- tab_text (tbl, col,
- row + 3,
- TAB_LEFT | TAT_TITLE | TAT_PRINTF,
- _ ("5%% Trimmed Mean"));
-
- tab_double (tbl, col + 2,
- row + 3,
- TAB_CENTER,
- m->trimmed_mean,
- NULL);
-
- tab_text (tbl, col,
- row + 4,
- TAB_LEFT | TAT_TITLE,
- _ ("Median"));
-
- {
- struct percentile *p;
- double d = 50;
-
- p = hsh_find (m->ptile_hash, &d);
-
- assert (p);
-
-
- tab_double (tbl, col + 2,
- row + 4,
- TAB_CENTER,
- p->v,
- NULL);
- }
-
-
- tab_text (tbl, col,
- row + 5,
- TAB_LEFT | TAT_TITLE,
- _ ("Variance"));
-
- tab_double (tbl, col + 2,
- row + 5,
- TAB_CENTER,
- m->var,
- NULL);
-
-
- tab_text (tbl, col,
- row + 6,
- TAB_LEFT | TAT_TITLE,
- _ ("Std. Deviation"));
-
-
- tab_double (tbl, col + 2,
- row + 6,
- TAB_CENTER,
- m->stddev,
- NULL);
-
-
- tab_text (tbl, col,
- row + 7,
- TAB_LEFT | TAT_TITLE,
- _ ("Minimum"));
-
- tab_double (tbl, col + 2,
- row + 7,
- TAB_CENTER,
- m->min, var_get_print_format (var));
-
- tab_text (tbl, col,
- row + 8,
- TAB_LEFT | TAT_TITLE,
- _ ("Maximum"));
-
- tab_double (tbl, col + 2,
- row + 8,
- TAB_CENTER,
- m->max, var_get_print_format (var));
-
- tab_text (tbl, col,
- row + 9,
- TAB_LEFT | TAT_TITLE,
- _ ("Range"));
-
-
- tab_double (tbl, col + 2,
- row + 9,
- TAB_CENTER,
- m->max - m->min,
- NULL);
-
- tab_text (tbl, col,
- row + 10,
- TAB_LEFT | TAT_TITLE,
- _ ("Interquartile Range"));
-
- {
- struct percentile *p1;
- struct percentile *p2;
-
- double d = 75;
- p1 = hsh_find (m->ptile_hash, &d);
-
- d = 25;
- p2 = hsh_find (m->ptile_hash, &d);
-
- assert (p1);
- assert (p2);
-
- tab_double (tbl, col + 2,
- row + 10,
- TAB_CENTER,
- p1->v - p2->v,
- NULL);
- }
-
- tab_text (tbl, col,
- row + 11,
- TAB_LEFT | TAT_TITLE,
- _ ("Skewness"));
-
-
- tab_double (tbl, col + 2,
- row + 11,
- TAB_CENTER,
- m->skewness,
- NULL);
-
- /* stderr of skewness */
- tab_double (tbl, col + 3,
- row + 11,
- TAB_CENTER,
- calc_seskew (m->n),
- NULL);
-
- tab_text (tbl, col,
- row + 12,
- TAB_LEFT | TAT_TITLE,
- _ ("Kurtosis"));
-
-
- tab_double (tbl, col + 2,
- row + 12,
- TAB_CENTER,
- m->kurtosis,
- NULL);
-
- /* stderr of kurtosis */
- tab_double (tbl, col + 3,
- row + 12,
- TAB_CENTER,
- calc_sekurt (m->n),
- NULL);
-}
-
-
-
-void
-box_plot_variables (const struct factor *fctr,
- const struct variable **vars, int n_vars,
- const struct variable *id)
-{
-
- int i;
- struct factor_statistics **fs ;
-
- if ( ! fctr )
- {
- box_plot_group (fctr, vars, n_vars, id);
- return;
- }
-
- for ( fs = fctr->fs ; *fs ; ++fs )
- {
- struct string str;
- double y_min = DBL_MAX;
- double y_max = -DBL_MAX;
- struct chart *ch = chart_create ();
- ds_init_empty (&str);
- factor_to_string (fctr, *fs, 0, &str );
-
- chart_write_title (ch, ds_cstr (&str));
-
- for ( i = 0 ; i < n_vars ; ++i )
- {
- y_max = MAX (y_max, (*fs)->m[i].max);
- y_min = MIN (y_min, (*fs)->m[i].min);
- }
-
- boxplot_draw_yscale (ch, y_max, y_min);
-
- for ( i = 0 ; i < n_vars ; ++i )
- {
-
- const double box_width = (ch->data_right - ch->data_left)
- / (n_vars * 2.0 ) ;
-
- const double box_centre = ( i * 2 + 1) * box_width
- + ch->data_left;
-
- boxplot_draw_boxplot (ch,
- box_centre, box_width,
- & (*fs)->m[i],
- var_to_string (vars[i]));
-
-
- }
-
- chart_submit (ch);
- ds_destroy (&str);
- }
-}
-
-
-
-/* Do a box plot, grouping all factors into one plot ;
- each dependent variable has its own plot.
-*/
-void
-box_plot_group (const struct factor *fctr,
- const struct variable **vars,
- int n_vars,
- const struct variable *id UNUSED)
-{
-
- int i;
-
- for ( i = 0 ; i < n_vars ; ++i )
- {
- struct factor_statistics **fs ;
- struct chart *ch;
-
- ch = chart_create ();
+ max_ll = ll_head (extrema_list (result->metrics[v].maxima));
+ for (e = 0; e < cmd.st_n;)
+ {
+ struct extremum *maximum = ll_data (max_ll, struct extremum, ll);
+ double weight = maximum->weight;
- boxplot_draw_yscale (ch, totals[i].max, totals[i].min);
+ while (weight-- > 0 && e < cmd.st_n)
+ {
- tab_float (tbl, n_cols - 1,
- heading_rows + row_var_start + row_result_start + e,
++ tab_double (tbl, n_cols - 1,
++ heading_rows + row_var_start +
++ row_result_start + e,
+ TAB_RIGHT,
+ maximum->value,
- 8, 2);
++ NULL);
+
+
- tab_float (tbl, n_cols - 2,
- heading_rows + row_var_start + row_result_start + e,
++ tab_fixed (tbl, n_cols - 2,
++ heading_rows + row_var_start +
++ row_result_start + e,
+ TAB_RIGHT,
+ maximum->location,
- 8, 0);
++ 10, 0);
+ ++e;
+ }
- if ( fctr )
- {
- int n_factors = 0;
- int f=0;
- for ( fs = fctr->fs ; *fs ; ++fs )
- ++n_factors;
+ max_ll = ll_next (max_ll);
+ }
- chart_write_title (ch, _ ("Boxplot of %s vs. %s"),
- var_to_string (vars[i]), var_to_string (fctr->indep_var[0]) );
- for ( fs = fctr->fs ; *fs ; ++fs )
+ if ( fctr->indep_var[0])
{
- struct string str;
- const double box_width = (ch->data_right - ch->data_left)
- / (n_factors * 2.0 ) ;
-
- const double box_centre = ( f++ * 2 + 1) * box_width
- + ch->data_left;
-
- ds_init_empty (&str);
- factor_to_string_concise (fctr, *fs, &str);
+ struct string vstr;
+ ds_init_empty (&vstr);
+ var_append_value_name (fctr->indep_var[0],
+ result->value[0], &vstr);
+
+ tab_text (tbl, 1,
+ heading_rows + row_var_start + row_result_start,
+ TAB_LEFT,
+ ds_cstr (&vstr)
+ );
- boxplot_draw_boxplot (ch,
- box_centre, box_width,
- & (*fs)->m[i],
- ds_cstr (&str));
- ds_destroy (&str);
+ ds_destroy (&vstr);
}
- }
- else if ( ch )
- {
- const double box_width = (ch->data_right - ch->data_left) / 3.0;
- const double box_centre = (ch->data_right + ch->data_left) / 2.0;
- chart_write_title (ch, _ ("Boxplot"));
- boxplot_draw_boxplot (ch,
- box_centre, box_width,
- &totals[i],
- var_to_string (vars[i]) );
+ tab_text (tbl, n_cols - 4,
+ heading_rows + row_var_start + row_result_start,
+ TAB_RIGHT,
+ _("Highest"));
+ tab_text (tbl, n_cols - 4,
+ heading_rows + row_var_start + row_result_start + cmd.st_n,
+ TAB_RIGHT,
+ _("Lowest"));
}
-
- chart_submit (ch);
}
-}
-
-/* Plot the normal and detrended normal plots for m
- Label the plots with factorname */
-void
-np_plot (const struct metrics *m, const char *factorname)
-{
- int i;
- double yfirst=0, ylast=0;
-
- /* Normal Plot */
- struct chart *np_chart;
-
- /* Detrended Normal Plot */
- struct chart *dnp_chart;
-
- /* The slope and intercept of the ideal normal probability line */
- const double slope = 1.0 / m->stddev;
- const double intercept = - m->mean / m->stddev;
-
- /* Cowardly refuse to plot an empty data set */
- if ( m->n_data == 0 )
- return ;
-
- np_chart = chart_create ();
- dnp_chart = chart_create ();
-
- if ( !np_chart || ! dnp_chart )
- return ;
-
- chart_write_title (np_chart, _ ("Normal Q-Q Plot of %s"), factorname);
- chart_write_xlabel (np_chart, _ ("Observed Value"));
- chart_write_ylabel (np_chart, _ ("Expected Normal"));
-
-
- chart_write_title (dnp_chart, _ ("Detrended Normal Q-Q Plot of %s"),
- factorname);
- chart_write_xlabel (dnp_chart, _ ("Observed Value"));
- chart_write_ylabel (dnp_chart, _ ("Dev from Normal"));
-
- yfirst = gsl_cdf_ugaussian_Pinv (m->wvp[0]->rank / ( m->n + 1));
- ylast = gsl_cdf_ugaussian_Pinv (m->wvp[m->n_data-1]->rank / ( m->n + 1));
-
-
- {
- /* Need to make sure that both the scatter plot and the ideal fit into the
- plot */
- double x_lower = MIN (m->min, (yfirst - intercept) / slope) ;
- double x_upper = MAX (m->max, (ylast - intercept) / slope) ;
- double slack = (x_upper - x_lower) * 0.05 ;
-
- chart_write_xscale (np_chart, x_lower - slack, x_upper + slack, 5);
-
- chart_write_xscale (dnp_chart, m->min, m->max, 5);
-
- }
-
- chart_write_yscale (np_chart, yfirst, ylast, 5);
-
- {
- /* We have to cache the detrended data, beacause we need to
- find its limits before we can plot it */
- double *d_data = xnmalloc (m->n_data, sizeof *d_data);
- double d_max = -DBL_MAX;
- double d_min = DBL_MAX;
- for ( i = 0 ; i < m->n_data; ++i )
- {
- const double ns = gsl_cdf_ugaussian_Pinv (m->wvp[i]->rank / ( m->n + 1));
+ tab_vline (tbl, TAL_2, heading_columns, 0, n_rows - 1);
- chart_datum (np_chart, 0, m->wvp[i]->v.f, ns);
- d_data[i] = (m->wvp[i]->v.f - m->mean) / m->stddev - ns;
+ tab_title (tbl, _("Extreme Values"));
- if ( d_data[i] < d_min ) d_min = d_data[i];
- if ( d_data[i] > d_max ) d_max = d_data[i];
- }
- chart_write_yscale (dnp_chart, d_min, d_max, 5);
- for ( i = 0 ; i < m->n_data; ++i )
- chart_datum (dnp_chart, 0, m->wvp[i]->v.f, d_data[i]);
+ tab_text (tbl, n_cols - 2, 0, TAB_CENTER | TAT_TITLE,
+ _("Case Number"));
- free (d_data);
- }
- chart_line (np_chart, slope, intercept, yfirst, ylast , CHART_DIM_Y);
- chart_line (dnp_chart, 0, 0, m->min, m->max , CHART_DIM_X);
+ tab_text (tbl, n_cols - 1, 0, TAB_CENTER | TAT_TITLE,
+ _("Value"));
- chart_submit (np_chart);
- chart_submit (dnp_chart);
+ tab_submit (tbl);
}
+#define PERCENTILE_ROWS 2
-
-
-/* Show the percentiles */
-void
+static void
show_percentiles (const struct variable **dependent_var,
- int n_dep_var,
- struct factor *fctr)
+ int n_dep_var,
+ const struct xfactor *fctr)
{
- struct tab_table *tbl;
int i;
+ int v;
+ int heading_columns = 2;
+ int n_cols;
+ const int n_percentiles = subc_list_double_count (&percentile_list);
+ const int heading_rows = 2;
+ struct tab_table *tbl;
- int n_cols, n_rows;
- int n_factors;
-
- struct hsh_table *ptiles ;
-
- int n_heading_columns;
- const int n_heading_rows = 2;
- const int n_stat_rows = 2;
+ int n_rows ;
+ n_rows = n_dep_var;
- int n_ptiles ;
+ assert (fctr);
- if ( fctr )
+ if ( fctr->indep_var[0] )
{
- struct factor_statistics **fs = fctr->fs ;
- n_heading_columns = 3;
- n_factors = hsh_count (fctr->fstats);
-
- ptiles = (*fs)->m[0].ptile_hash;
+ heading_columns = 3;
if ( fctr->indep_var[1] )
- n_heading_columns = 4;
- }
- else
- {
- n_factors = 1;
- n_heading_columns = 2;
-
- ptiles = totals[0].ptile_hash;
+ {
+ heading_columns = 4;
+ }
}
- n_ptiles = hsh_count (ptiles);
-
- n_rows = n_heading_rows + n_dep_var * n_stat_rows * n_factors;
+ n_rows *= ll_count (&fctr->result_list) * PERCENTILE_ROWS;
+ n_rows += heading_rows;
- n_cols = n_heading_columns + n_ptiles ;
+ n_cols = heading_columns + n_percentiles;
tbl = tab_create (n_cols, n_rows, 0);
-
- tab_headers (tbl, n_heading_columns + 1, 0, n_heading_rows, 0);
+ tab_headers (tbl, heading_columns, 0, heading_rows, 0);
tab_dim (tbl, tab_natural_dimensions);
0, 0,
n_cols - 1, n_rows - 1);
- tab_hline (tbl, TAL_2, 0, n_cols - 1, n_heading_rows );
-
- tab_vline (tbl, TAL_2, n_heading_columns, 0, n_rows - 1);
-
-
- tab_title (tbl, _ ("Percentiles"));
-
-
- tab_hline (tbl, TAL_1, n_heading_columns, n_cols - 1, 1 );
-
- tab_box (tbl,
- -1, -1,
- -1, TAL_1,
- 0, n_heading_rows,
- n_heading_columns - 1, n_rows - 1);
-
-
- tab_box (tbl,
- -1, -1,
- -1, TAL_1,
- n_heading_columns, n_heading_rows - 1,
- n_cols - 1, n_rows - 1);
-
- tab_joint_text (tbl, n_heading_columns + 1, 0,
- n_cols - 1 , 0,
- TAB_CENTER | TAT_TITLE ,
- _ ("Percentiles"));
-
-
- {
- /* Put in the percentile break points as headings */
+ tab_hline (tbl, TAL_2, 0, n_cols - 1, heading_rows );
+ tab_hline (tbl, TAL_2, 1, n_cols - 1, heading_rows );
- struct percentile **p = (struct percentile **) hsh_sort (ptiles);
+ if ( fctr->indep_var[0])
+ tab_text (tbl, 1, 1, TAT_TITLE, var_to_string (fctr->indep_var[0]));
- i = 0;
- while ( (*p) )
- {
- tab_fixed (tbl, n_heading_columns + i++ , 1,
- TAB_CENTER,
- (*p)->p,
- 8, 0);
- p++;
- }
+ if ( fctr->indep_var[1])
+ tab_text (tbl, 2, 1, TAT_TITLE, var_to_string (fctr->indep_var[1]));
- }
-
- for ( i = 0 ; i < n_dep_var ; ++i )
+ for (v = 0 ; v < n_dep_var ; ++v )
{
- const int n_stat_rows = 2;
- const int row = n_heading_rows + i * n_stat_rows * n_factors ;
+ double hinges[3];
+ struct ll *ll;
+ int i = 0;
- if ( i > 0 )
- tab_hline (tbl, TAL_1, 0, n_cols - 1, row );
+ const int row_var_start =
+ v * PERCENTILE_ROWS * ll_count(&fctr->result_list);
- tab_text (tbl, 0,
- i * n_stat_rows * n_factors + n_heading_rows,
+ tab_text (tbl,
+ 0,
+ heading_rows + row_var_start,
TAB_LEFT | TAT_TITLE,
- var_to_string (dependent_var[i])
+ var_to_string (dependent_var[v])
);
- if ( fctr )
+ for (ll = ll_head (&fctr->result_list);
+ ll != ll_null (&fctr->result_list); i++, ll = ll_next (ll))
{
- const union value *prev = NULL ;
- struct factor_statistics **fs = fctr->fs;
- int count = 0;
+ int j;
+ const struct factor_result *result =
+ ll_data (ll, struct factor_result, ll);
- tab_text (tbl, 1, n_heading_rows - 1,
- TAB_CENTER | TAT_TITLE,
- var_to_string (fctr->indep_var[0]));
+ if ( i > 0 || v > 0 )
+ {
+ const int left_col = (i == 0) ? 0 : 1;
+ tab_hline (tbl, TAL_1, left_col, n_cols - 1,
+ heading_rows + row_var_start + i * PERCENTILE_ROWS);
+ }
+ if ( fctr->indep_var[0])
+ {
+ struct string vstr;
+ ds_init_empty (&vstr);
+ var_append_value_name (fctr->indep_var[0],
+ result->value[0], &vstr);
+
+ tab_text (tbl, 1,
+ heading_rows + row_var_start + i * PERCENTILE_ROWS,
+ TAB_LEFT,
+ ds_cstr (&vstr)
+ );
- if ( fctr->indep_var[1])
- tab_text (tbl, 2, n_heading_rows - 1, TAB_CENTER | TAT_TITLE,
- var_to_string (fctr->indep_var[1]));
+ ds_destroy (&vstr);
+ }
- while ( *fs )
- {
- const int row = n_heading_rows + n_stat_rows *
- ( ( i * n_factors ) + count );
+ tab_text (tbl, n_cols - n_percentiles - 1,
+ heading_rows + row_var_start + i * PERCENTILE_ROWS,
+ TAB_LEFT,
+ ptile_alg_desc [percentile_algorithm]);
- if ( !prev || 0 != compare_values (prev, (*fs)->id[0],
- var_get_width (fctr->indep_var[0])))
- {
- struct string vstr;
- ds_init_empty (&vstr);
- var_append_value_name (fctr->indep_var[0],
- (*fs)->id[0], &vstr);
+ tab_text (tbl, n_cols - n_percentiles - 1,
+ heading_rows + row_var_start + 1 + i * PERCENTILE_ROWS,
+ TAB_LEFT,
+ _("Tukey's Hinges"));
- if ( count > 0 )
- tab_hline (tbl, TAL_1, 1, n_cols - 1, row);
- tab_text (tbl,
- 1, row,
- TAB_LEFT | TAT_TITLE,
- ds_cstr (&vstr)
- );
+ tab_vline (tbl, TAL_1, n_cols - n_percentiles -1, heading_rows, n_rows - 1);
- ds_destroy (&vstr);
- }
+ tukey_hinges_calculate ((struct tukey_hinges *) result->metrics[v].tukey_hinges,
+ hinges);
- prev = (*fs)->id[0];
+ for (j = 0; j < n_percentiles; ++j)
+ {
+ double hinge = SYSMIS;
- tab_float (tbl, n_cols - n_percentiles + j,
++ tab_double (tbl, n_cols - n_percentiles + j,
+ heading_rows + row_var_start + i * PERCENTILE_ROWS,
+ TAB_CENTER,
+ percentile_calculate (result->metrics[v].ptl[j],
+ percentile_algorithm),
- 8, 2
++ NULL
+ );
+
+ if ( result->metrics[v].ptl[j]->ptile == 0.5)
+ hinge = hinges[1];
+ else if ( result->metrics[v].ptl[j]->ptile == 0.25)
+ hinge = hinges[0];
+ else if ( result->metrics[v].ptl[j]->ptile == 0.75)
+ hinge = hinges[2];
+
+ if ( hinge != SYSMIS)
- tab_float (tbl, n_cols - n_percentiles + j,
++ tab_double (tbl, n_cols - n_percentiles + j,
+ heading_rows + row_var_start + 1 + i * PERCENTILE_ROWS,
+ TAB_CENTER,
+ hinge,
- 8, 2
++ NULL
+ );
- if (fctr->indep_var[1] && count > 0 )
- tab_hline (tbl, TAL_1, 2, n_cols - 1, row);
+ }
+ }
+ }
- if ( fctr->indep_var[1])
- {
- struct string vstr;
- ds_init_empty (&vstr);
- var_append_value_name (fctr->indep_var[1], (*fs)->id[1], &vstr);
+ tab_vline (tbl, TAL_2, heading_columns, 0, n_rows - 1);
- tab_text (tbl, 2, row,
- TAB_LEFT | TAT_TITLE,
- ds_cstr (&vstr)
- );
+ tab_title (tbl, _("Percentiles"));
- ds_destroy (&vstr);
- }
+ for (i = 0 ; i < n_percentiles; ++i )
+ {
+ tab_text (tbl, n_cols - n_percentiles + i, 1,
+ TAB_CENTER | TAT_TITLE | TAT_PRINTF,
+ _("%g"),
+ subc_list_double_at (&percentile_list, i)
+ );
- populate_percentiles (tbl, n_heading_columns - 1,
- row,
- & (*fs)->m[i]);
+ }
- count++ ;
- fs++;
- }
+ tab_joint_text (tbl,
+ n_cols - n_percentiles, 0,
+ n_cols - 1, 0,
+ TAB_CENTER | TAT_TITLE,
+ _("Percentiles"));
+ /* Vertical lines for the data only */
+ tab_box (tbl,
+ -1, -1,
+ -1, TAL_1,
+ n_cols - n_percentiles, 1,
+ n_cols - 1, n_rows - 1);
- }
- else
- {
- populate_percentiles (tbl, n_heading_columns - 1,
- i * n_stat_rows * n_factors + n_heading_rows,
- &totals[i]);
- }
- }
+ tab_hline (tbl, TAL_1, n_cols - n_percentiles, n_cols - 1, 1);
tab_submit (tbl);
for (; casegrouper_get_next_group (grouper, &group);
casereader_destroy (group))
{
- struct ccase c;
+ struct ccase *c;
precalc (group, ds);
- for (; casereader_read (group, &c); case_destroy (&c))
- calc (&c, ds);
+ for (; (c = casereader_read (group)) != NULL; case_unref (c))
+ calc (c, ds);
- postcalc ();
+ postcalc (ds);
}
ok = casegrouper_destroy (grouper);
ok = proc_commit (ds) && ok;
/* Routines to show the output tables */
- static void show_anova_table (void);
- static void show_descriptives (void);
- static void show_homogeneity (void);
+ static void show_anova_table(void);
+ static void show_descriptives (const struct dictionary *dict);
+ static void show_homogeneity(void);
-static void show_contrast_coeffs(short *);
-static void show_contrast_tests(short *);
+static void show_contrast_coeffs (short *);
+static void show_contrast_tests (short *);
enum stat_table_t {STAT_DESC = 1, STAT_HOMO = 2};
-static enum stat_table_t stat_tables ;
+static enum stat_table_t stat_tables;
- void output_oneway (void);
+ static void output_oneway (const struct dictionary *dict);
int
}
- void
- output_oneway (void)
+ static void
+ output_oneway (const struct dictionary *dict)
{
size_t i;
- short *bad_contrast ;
+ short *bad_contrast;
bad_contrast = xnmalloc (cmd.sbc_contrast, sizeof *bad_contrast);
}
if ( stat_tables & STAT_DESC )
- show_descriptives ();
+ show_descriptives (dict);
if ( stat_tables & STAT_HOMO )
- show_homogeneity();
+ show_homogeneity ();
- show_anova_table();
+ show_anova_table ();
if (cmd.sbc_contrast )
{
/* Degrees of freedom */
- tab_float (t, 3, i * 3 + 1, 0, df1, 4, 0);
- tab_float (t, 3, i * 3 + 2, 0, df2, 4, 0);
- tab_float (t, 3, i * 3 + 3, 0, totals->n - 1, 4, 0);
+ tab_fixed (t, 3, i * 3 + 1, 0, df1, 4, 0);
+ tab_fixed (t, 3, i * 3 + 2, 0, df2, 4, 0);
+ tab_fixed (t, 3, i * 3 + 3, 0, totals->n - 1, 4, 0);
/* Mean Squares */
- tab_float (t, 4, i * 3 + 1, TAB_RIGHT, msa, 8, 3);
- tab_float (t, 4, i * 3 + 2, TAB_RIGHT, gp->mse, 8, 3);
+ tab_double (t, 4, i * 3 + 1, TAB_RIGHT, msa, NULL);
+ tab_double (t, 4, i * 3 + 2, TAB_RIGHT, gp->mse, NULL);
-
{
- const double F = msa/gp->mse;
+ const double F = msa / gp->mse ;
/* The F value */
- tab_float (t, 5, i * 3 + 1, 0, F, 8, 3);
+ tab_double (t, 5, i * 3 + 1, 0, F, NULL);
/* The significance */
- tab_float (t, 6, i * 3 + 1, 0, gsl_cdf_fdist_Q (F, df1, df2), 8, 3);
- tab_double (t, 6, i * 3 + 1, 0, gsl_cdf_fdist_Q (F, df1,df2), NULL);
++ tab_double (t, 6, i * 3 + 1, 0, gsl_cdf_fdist_Q (F, df1, df2), NULL);
}
-
}
-
}
const double confidence = 0.95;
const double q = (1.0 - confidence) / 2.0;
+ const struct variable *wv = dict_get_weight (dict);
+ const struct fmt_spec *wfmt = wv ? var_get_print_format (wv) : & F_8_0;
- int n_rows = 2 ;
+ int n_rows = 2;
- for ( v = 0 ; v < n_vars ; ++v )
+ for ( v = 0; v < n_vars; ++v )
n_rows += group_proc_get (vars[v])->n_groups + 1;
- t = tab_create (n_cols,n_rows,0);
+ t = tab_create (n_cols, n_rows, 0);
tab_headers (t, 2, 0, 2, 0);
tab_dim (t, tab_natural_dimensions);
struct group_statistics *totals = &gp->ugs;
const char *s = var_to_string (vars[v]);
+ const struct fmt_spec *fmt = var_get_print_format (vars[v]);
struct group_statistics *const *gs_array =
- (struct group_statistics *const *) hsh_sort(gp->group_hash);
+ (struct group_statistics *const *) hsh_sort (gp->group_hash);
int count = 0;
tab_text (t, 0, row, TAB_LEFT | TAT_TITLE, s);
}
tab_text (t, 1, row + count,
- TAB_LEFT | TAT_TITLE ,_("Total"));
+ TAB_LEFT | TAT_TITLE, _("Total"));
- tab_float (t, 2, row + count, 0, totals->n, 8, 0);
+ tab_double (t, 2, row + count, 0, totals->n, wfmt);
- tab_float (t, 3, row + count, 0, totals->mean, 8, 2);
+ tab_double (t, 3, row + count, 0, totals->mean, NULL);
- tab_float (t, 4, row + count, 0, totals->std_dev, 8, 2);
+ tab_double (t, 4, row + count, 0, totals->std_dev, NULL);
- std_error = totals->std_dev/sqrt (totals->n);
+ std_error = totals->std_dev / sqrt (totals->n) ;
- tab_float (t, 5, row + count, 0, std_error, 8, 2);
+ tab_double (t, 5, row + count, 0, std_error, NULL);
/* Now the confidence interval */
df_numerator += (coef * coef) * winv;
df_denominator += pow2((coef * coef) * winv) / (gs->n - 1);
}
- sec_vneq = sqrt(sec_vneq);
+ sec_vneq = sqrt (sec_vneq);
- df_numerator = pow2(df_numerator);
+ df_numerator = pow2 (df_numerator);
- tab_float (t, 3, (v * lines_per_variable) + i + 1,
- TAB_RIGHT, contrast_value, 8, 2);
+ tab_double (t, 3, (v * lines_per_variable) + i + 1,
+ TAB_RIGHT, contrast_value, NULL);
- tab_float (t, 3, (v * lines_per_variable) + i + 1 +
+ tab_double (t, 3, (v * lines_per_variable) + i + 1 +
cmd.sbc_contrast,
- TAB_RIGHT, contrast_value, 8, 2);
+ TAB_RIGHT, contrast_value, NULL);
std_error_contrast = sqrt (grp_data->mse * coef_msq);
/* Std. Error */
- tab_float (t, 4, (v * lines_per_variable) + i + 1,
+ tab_double (t, 4, (v * lines_per_variable) + i + 1,
TAB_RIGHT, std_error_contrast,
- 8, 3);
+ NULL);
- T = fabs(contrast_value / std_error_contrast) ;
+ T = fabs (contrast_value / std_error_contrast);
/* T Statistic */
/* Significance TWO TAILED !!*/
- tab_float (t, 7, (v * lines_per_variable) + i + 1,
+ tab_double (t, 7, (v * lines_per_variable) + i + 1,
TAB_RIGHT, 2 * gsl_cdf_tdist_Q (T, df),
- 8, 3);
-
+ NULL);
-
/* Now for the Variances NOT Equal case */
/* Std. Error */
- tab_float (t, 4,
+ tab_double (t, 4,
(v * lines_per_variable) + i + 1 + cmd.sbc_contrast,
TAB_RIGHT, sec_vneq,
- 8, 3);
-
+ NULL);
-
T = contrast_value / sec_vneq;
- tab_float (t, 5,
+ tab_double (t, 5,
(v * lines_per_variable) + i + 1 + cmd.sbc_contrast,
TAB_RIGHT, T,
- 8, 3);
-
+ NULL);
-
df = df_numerator / df_denominator;
- tab_float (t, 6,
+ tab_double (t, 6,
(v * lines_per_variable) + i + 1 + cmd.sbc_contrast,
TAB_RIGHT, df,
- 8, 3);
+ NULL);
/* The Significance */
- tab_float (t, 7, (v * lines_per_variable) + i + 1 + cmd.sbc_contrast,
- TAB_RIGHT, 2 * gsl_cdf_tdist_Q (T, df),
- 8, 3);
-
-
+ tab_double (t, 7, (v * lines_per_variable) + i + 1 + cmd.sbc_contrast,
+ TAB_RIGHT, 2 * gsl_cdf_tdist_Q (T,df),
+ NULL);
-
-
}
if ( v > 0 )
--- /dev/null
- struct reliability rel = {
+/* PSPP - a program for statistical analysis.
+ Copyright (C) 2008, 2009 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 "xalloc.h"
+#include "xmalloca.h"
+
+#include "gettext.h"
+#define _(msgid) gettext (msgid)
+#define N_(msgid) msgid
+
+#include <data/variable.h>
+#include <data/dictionary.h>
+#include <data/procedure.h>
+#include <data/casereader.h>
+#include <data/casegrouper.h>
+#include <math/moments.h>
+#include <data/case.h>
+
+#include <language/command.h>
+
+#include <output/manager.h>
+#include <output/table.h>
+
+/* (headers) */
+
+/* (specification)
+ reliability (rel_):
+ *^variables=varlist("PV_NO_SCRATCH | PV_NUMERIC");
+ scale=custom;
+ missing=miss:!exclude/include;
+ model=custom;
+ method=covariance;
+ +summary[sum_]=total.
+*/
+/* (declarations) */
+/* (functions) */
+
+
+static int rel_custom_scale (struct lexer *lexer, struct dataset *ds,
+ struct cmd_reliability *p, void *aux);
+
+static int rel_custom_model (struct lexer *, struct dataset *,
+ struct cmd_reliability *, void *);
+
+int cmd_reliability (struct lexer *lexer, struct dataset *ds);
+
+struct cronbach
+{
+ const struct variable **items;
+ size_t n_items;
+ double alpha;
+ double sum_of_variances;
+ double variance_of_sums;
+ int totals_idx; /* Casereader index into the totals */
+
+ struct moments1 **m ; /* Moments of the items */
+ struct moments1 *total ; /* Moments of the totals */
+};
+
+#if 0
+static void
+dump_cronbach (const struct cronbach *s)
+{
+ int i;
+ printf ("N items %d\n", s->n_items);
+ for (i = 0 ; i < s->n_items; ++i)
+ {
+ printf ("%s\n", var_get_name (s->items[i]));
+ }
+
+ printf ("Totals idx %d\n", s->totals_idx);
+
+ printf ("scale variance %g\n", s->variance_of_sums);
+ printf ("alpha %g\n", s->alpha);
+ putchar ('\n');
+}
+#endif
+
+enum model
+ {
+ MODEL_ALPHA,
+ MODEL_SPLIT
+ };
+
+
+struct reliability
+{
++ const struct dictionary *dict;
+ const struct variable **variables;
+ int n_variables;
+ enum mv_class exclude;
+
+ struct cronbach *sc;
+ int n_sc;
+
+ int total_start;
+
+ struct string scale_name;
+
+ enum model model;
+ int split_point;
+};
+
+
+static double
+alpha (int k, double sum_of_variances, double variance_of_sums)
+{
+ return k / ( k - 1.0) * ( 1 - sum_of_variances / variance_of_sums);
+}
+
+static void reliability_summary_total (const struct reliability *rel);
+
+static void reliability_statistics (const struct reliability *rel);
+
+
+
+static void
+run_reliability (struct casereader *group, struct dataset *ds,
+ struct reliability *rel);
+
+
+int
+cmd_reliability (struct lexer *lexer, struct dataset *ds)
+{
+ int i;
+ bool ok = false;
+ struct casegrouper *grouper;
+ struct casereader *group;
+ struct cmd_reliability cmd;
+
- static void case_processing_summary (casenumber n_valid, casenumber n_missing);
++ struct reliability rel = {NULL,
+ NULL, 0, MV_ANY, NULL, 0, -1,
+ DS_EMPTY_INITIALIZER,
+ MODEL_ALPHA, 0};
+
+ cmd.v_variables = NULL;
+
+ if ( ! parse_reliability (lexer, ds, &cmd, &rel) )
+ {
+ goto done;
+ }
+
++ rel.dict = dataset_dict (ds);
+ rel.variables = cmd.v_variables;
+ rel.n_variables = cmd.n_variables;
+ rel.exclude = MV_ANY;
+
+
+ if (NULL == rel.sc)
+ {
+ struct cronbach *c;
+ /* Create a default Scale */
+
+ rel.n_sc = 1;
+ rel.sc = xzalloc (sizeof (struct cronbach) * rel.n_sc);
+
+ ds_init_cstr (&rel.scale_name, "ANY");
+
+ c = &rel.sc[0];
+ c->n_items = cmd.n_variables;
+ c->items = xzalloc (sizeof (struct variable*) * c->n_items);
+
+ for (i = 0 ; i < c->n_items ; ++i)
+ c->items[i] = cmd.v_variables[i];
+ }
+
+ if ( cmd.miss == REL_INCLUDE)
+ rel.exclude = MV_SYSTEM;
+
+ if ( rel.model == MODEL_SPLIT)
+ {
+ int i;
+ const struct cronbach *s;
+
+ rel.n_sc += 2 ;
+ rel.sc = xrealloc (rel.sc, sizeof (struct cronbach) * rel.n_sc);
+
+ s = &rel.sc[0];
+
+ rel.sc[1].n_items =
+ (rel.split_point == -1) ? s->n_items / 2 : rel.split_point;
+
+ rel.sc[2].n_items = s->n_items - rel.sc[1].n_items;
+ rel.sc[1].items = xzalloc (sizeof (struct variable *)
+ * rel.sc[1].n_items);
+
+ rel.sc[2].items = xzalloc (sizeof (struct variable *) *
+ rel.sc[2].n_items);
+
+ for (i = 0; i < rel.sc[1].n_items ; ++i)
+ rel.sc[1].items[i] = s->items[i];
+
+ while (i < s->n_items)
+ {
+ rel.sc[2].items[i - rel.sc[1].n_items] = s->items[i];
+ i++;
+ }
+ }
+
+ if (cmd.a_summary[REL_SUM_TOTAL])
+ {
+ int i;
+ const int base_sc = rel.n_sc;
+
+ rel.total_start = base_sc;
+
+ rel.n_sc += rel.sc[0].n_items ;
+ rel.sc = xrealloc (rel.sc, sizeof (struct cronbach) * rel.n_sc);
+
+ for (i = 0 ; i < rel.sc[0].n_items; ++i )
+ {
+ int v_src;
+ int v_dest = 0;
+ struct cronbach *s = &rel.sc[i + base_sc];
+
+ s->n_items = rel.sc[0].n_items - 1;
+ s->items = xzalloc (sizeof (struct variable *) * s->n_items);
+ for (v_src = 0 ; v_src < rel.sc[0].n_items ; ++v_src)
+ {
+ if ( v_src != i)
+ s->items[v_dest++] = rel.sc[0].items[v_src];
+ }
+ }
+ }
+
+ /* Data pass. */
+ grouper = casegrouper_create_splits (proc_open (ds), dataset_dict (ds));
+ while (casegrouper_get_next_group (grouper, &group))
+ {
+ run_reliability (group, ds, &rel);
+
+ reliability_statistics (&rel);
+
+ if (cmd.a_summary[REL_SUM_TOTAL])
+ reliability_summary_total (&rel);
+ }
+ ok = casegrouper_destroy (grouper);
+ ok = proc_commit (ds) && ok;
+
+ free_reliability (&cmd);
+
+ done:
+
+ /* Free all the stuff */
+ for (i = 0 ; i < rel.n_sc; ++i)
+ {
+ int x;
+ struct cronbach *c = &rel.sc[i];
+ free (c->items);
+
+ moments1_destroy (c->total);
+
+ if ( c->m)
+ for (x = 0 ; x < c->n_items; ++x)
+ moments1_destroy (c->m[x]);
+
+ free (c->m);
+ }
+
+ ds_destroy (&rel.scale_name);
+ free (rel.sc);
+
+ if (ok)
+ return CMD_SUCCESS;
+
+ return CMD_FAILURE;
+}
+
+/* Return the sum of all the item variables in S */
+static double
+append_sum (const struct ccase *c, casenumber n UNUSED, void *aux)
+{
+ double sum = 0;
+ const struct cronbach *s = aux;
+
+ int v;
+ for (v = 0 ; v < s->n_items; ++v)
+ {
+ sum += case_data (c, s->items[v])->f;
+ }
+
+ return sum;
+};
+
+
- run_reliability (struct casereader *input, struct dataset *ds UNUSED,
++static void case_processing_summary (casenumber n_valid, casenumber n_missing,
++ const struct dictionary *dict);
+
+static void
- case_processing_summary (n_valid, n_missing);
++run_reliability (struct casereader *input, struct dataset *ds,
+ struct reliability *rel)
+{
+ int i;
+ int si;
+ struct ccase *c;
+ casenumber n_missing ;
+ casenumber n_valid = 0;
+
+
+ for (si = 0 ; si < rel->n_sc; ++si)
+ {
+ struct cronbach *s = &rel->sc[si];
+
+ s->m = xzalloc (sizeof (s->m) * s->n_items);
+ s->total = moments1_create (MOMENT_VARIANCE);
+
+ for (i = 0 ; i < s->n_items ; ++i )
+ s->m[i] = moments1_create (MOMENT_VARIANCE);
+ }
+
+ input = casereader_create_filter_missing (input,
+ rel->variables,
+ rel->n_variables,
+ rel->exclude,
+ &n_missing,
+ NULL);
+
+ for (si = 0 ; si < rel->n_sc; ++si)
+ {
+ struct cronbach *s = &rel->sc[si];
+
+
+ s->totals_idx = casereader_get_value_cnt (input);
+ input =
+ casereader_create_append_numeric (input, append_sum,
+ s, NULL);
+ }
+
+ for (; (c = casereader_read (input)) != NULL; case_unref (c))
+ {
+ double weight = 1.0;
+ n_valid ++;
+
+ for (si = 0; si < rel->n_sc; ++si)
+ {
+ struct cronbach *s = &rel->sc[si];
+
+ for (i = 0 ; i < s->n_items ; ++i )
+ moments1_add (s->m[i], case_data (c, s->items[i])->f, weight);
+
+ moments1_add (s->total, case_data_idx (c, s->totals_idx)->f, weight);
+ }
+ }
+ casereader_destroy (input);
+
+ for (si = 0; si < rel->n_sc; ++si)
+ {
+ struct cronbach *s = &rel->sc[si];
+
+ s->sum_of_variances = 0;
+ for (i = 0 ; i < s->n_items ; ++i )
+ {
+ double weight, mean, variance;
+ moments1_calculate (s->m[i], &weight, &mean, &variance, NULL, NULL);
+
+ s->sum_of_variances += variance;
+ }
+
+ moments1_calculate (s->total, NULL, NULL, &s->variance_of_sums,
+ NULL, NULL);
+
+ s->alpha =
+ alpha (s->n_items, s->sum_of_variances, s->variance_of_sums);
+ }
+
+
+ {
+ struct tab_table *tab = tab_create(1, 1, 0);
+
+ tab_dim (tab, tab_natural_dimensions);
+ tab_flags (tab, SOMF_NO_TITLE );
+
+ tab_text(tab, 0, 0, TAT_PRINTF, "Scale: %s", ds_cstr (&rel->scale_name));
+
+ tab_submit(tab);
+ }
+
+
- void (*populate)(struct tab_table *, const struct reliability *);
++ case_processing_summary (n_valid, n_missing, dataset_dict (ds));
+}
+
+
+static void reliability_statistics_model_alpha (struct tab_table *tbl,
+ const struct reliability *rel);
+
+static void reliability_statistics_model_split (struct tab_table *tbl,
+ const struct reliability *rel);
+
+struct reliability_output_table
+{
+ int n_cols;
+ int n_rows;
+ int heading_cols;
+ int heading_rows;
- tab_float (tbl, 1, heading_rows + i, TAB_RIGHT,
- mean, 8, 3);
++ void (*populate) (struct tab_table *, const struct reliability *);
+};
+
+static struct reliability_output_table rol[2] =
+ {
+ { 2, 2, 1, 1, reliability_statistics_model_alpha},
+ { 4, 9, 3, 0, reliability_statistics_model_split}
+ };
+
+static void
+reliability_statistics (const struct reliability *rel)
+{
+ int n_cols = rol[rel->model].n_cols;
+ int n_rows = rol[rel->model].n_rows;
+ int heading_columns = rol[rel->model].heading_cols;
+ int heading_rows = rol[rel->model].heading_rows;
+
+ struct tab_table *tbl = tab_create (n_cols, n_rows, 0);
+ tab_headers (tbl, heading_columns, 0, heading_rows, 0);
+
+ tab_dim (tbl, tab_natural_dimensions);
+
+ tab_title (tbl, _("Reliability Statistics"));
+
+ /* Vertical lines for the data only */
+ tab_box (tbl,
+ -1, -1,
+ -1, TAL_1,
+ heading_columns, 0,
+ n_cols - 1, n_rows - 1);
+
+ /* Box around table */
+ tab_box (tbl,
+ TAL_2, TAL_2,
+ -1, -1,
+ 0, 0,
+ n_cols - 1, n_rows - 1);
+
+
+ tab_hline (tbl, TAL_2, 0, n_cols - 1, heading_rows);
+
+ tab_vline (tbl, TAL_2, heading_columns, 0, n_rows - 1);
+
+ if ( rel->model == MODEL_ALPHA )
+ reliability_statistics_model_alpha (tbl, rel);
+ else if (rel->model == MODEL_SPLIT )
+ reliability_statistics_model_split (tbl, rel);
+
+ tab_submit (tbl);
+}
+
+static void
+reliability_summary_total (const struct reliability *rel)
+{
+ int i;
+ const int n_cols = 5;
+ const int heading_columns = 1;
+ const int heading_rows = 1;
+ const int n_rows = rel->sc[0].n_items + heading_rows ;
+
+ struct tab_table *tbl = tab_create (n_cols, n_rows, 0);
+ tab_headers (tbl, heading_columns, 0, heading_rows, 0);
+
+ tab_dim (tbl, tab_natural_dimensions);
+
+ tab_title (tbl, _("Item-Total Statistics"));
+
+ /* Vertical lines for the data only */
+ tab_box (tbl,
+ -1, -1,
+ -1, TAL_1,
+ heading_columns, 0,
+ n_cols - 1, n_rows - 1);
+
+ /* Box around table */
+ tab_box (tbl,
+ TAL_2, TAL_2,
+ -1, -1,
+ 0, 0,
+ n_cols - 1, n_rows - 1);
+
+
+ tab_hline (tbl, TAL_2, 0, n_cols - 1, heading_rows);
+
+ tab_vline (tbl, TAL_2, heading_columns, 0, n_rows - 1);
+
+ tab_text (tbl, 1, 0, TAB_CENTER | TAT_TITLE,
+ _("Scale Mean if Item Deleted"));
+
+ tab_text (tbl, 2, 0, TAB_CENTER | TAT_TITLE,
+ _("Scale Variance if Item Deleted"));
+
+ tab_text (tbl, 3, 0, TAB_CENTER | TAT_TITLE,
+ _("Corrected Item-Total Correlation"));
+
+ tab_text (tbl, 4, 0, TAB_CENTER | TAT_TITLE,
+ _("Cronbach's Alpha if Item Deleted"));
+
+
+ for (i = 0 ; i < rel->sc[0].n_items; ++i)
+ {
+ double cov, item_to_total_r;
+ double mean, weight, var;
+
+ const struct cronbach *s = &rel->sc[rel->total_start + i];
+ tab_text (tbl, 0, heading_rows + i, TAB_LEFT| TAT_TITLE,
+ var_to_string (rel->sc[0].items[i]));
+
+ moments1_calculate (s->total, &weight, &mean, &var, 0, 0);
+
- tab_float (tbl, 2, heading_rows + i, TAB_RIGHT,
- s->variance_of_sums, 8, 3);
++ tab_double (tbl, 1, heading_rows + i, TAB_RIGHT,
++ mean, NULL);
+
- tab_float (tbl, 4, heading_rows + i, TAB_RIGHT,
- s->alpha, 8, 3);
++ tab_double (tbl, 2, heading_rows + i, TAB_RIGHT,
++ s->variance_of_sums, NULL);
+
- tab_float (tbl, 3, heading_rows + i, TAB_RIGHT,
- item_to_total_r, 8, 3);
++ tab_double (tbl, 4, heading_rows + i, TAB_RIGHT,
++ s->alpha, NULL);
+
+
+ moments1_calculate (rel->sc[0].m[i], &weight, &mean, &var, 0,0);
+ cov = rel->sc[0].variance_of_sums + var - s->variance_of_sums;
+ cov /= 2.0;
+
+ item_to_total_r = (cov - var) / (sqrt(var) * sqrt (s->variance_of_sums));
+
+
- tab_float (tbl, 0, 1, TAB_RIGHT, s->alpha, 8, 3);
++ tab_double (tbl, 3, heading_rows + i, TAB_RIGHT,
++ item_to_total_r, NULL);
+ }
+
+
+ tab_submit (tbl);
+}
+
+
+static void
+reliability_statistics_model_alpha (struct tab_table *tbl,
+ const struct reliability *rel)
+{
++ const struct variable *wv = dict_get_weight (rel->dict);
++ const struct fmt_spec *wfmt = wv ? var_get_print_format (wv) : & F_8_0;
++
+ const struct cronbach *s = &rel->sc[0];
+
+ tab_text (tbl, 0, 0, TAB_CENTER | TAT_TITLE,
+ _("Cronbach's Alpha"));
+
+ tab_text (tbl, 1, 0, TAB_CENTER | TAT_TITLE,
+ _("N of items"));
+
- tab_float (tbl, 1, 1, TAB_RIGHT, s->n_items, 8, 0);
++ tab_double (tbl, 0, 1, TAB_RIGHT, s->alpha, NULL);
+
- tab_float (tbl, 3, 0, TAB_RIGHT, rel->sc[1].alpha, 8, 3);
- tab_float (tbl, 3, 2, TAB_RIGHT, rel->sc[2].alpha, 8, 3);
++ tab_double (tbl, 1, 1, TAB_RIGHT, s->n_items, wfmt);
+}
+
+
+static void
+reliability_statistics_model_split (struct tab_table *tbl,
+ const struct reliability *rel)
+{
++ const struct variable *wv = dict_get_weight (rel->dict);
++ const struct fmt_spec *wfmt = wv ? var_get_print_format (wv) : & F_8_0;
++
+ tab_text (tbl, 0, 0, TAB_LEFT,
+ _("Cronbach's Alpha"));
+
+ tab_text (tbl, 1, 0, TAB_LEFT,
+ _("Part 1"));
+
+ tab_text (tbl, 2, 0, TAB_LEFT,
+ _("Value"));
+
+ tab_text (tbl, 2, 1, TAB_LEFT,
+ _("N of Items"));
+
+
+
+ tab_text (tbl, 1, 2, TAB_LEFT,
+ _("Part 2"));
+
+ tab_text (tbl, 2, 2, TAB_LEFT,
+ _("Value"));
+
+ tab_text (tbl, 2, 3, TAB_LEFT,
+ _("N of Items"));
+
+
+
+ tab_text (tbl, 1, 4, TAB_LEFT,
+ _("Total N of Items"));
+
+ tab_text (tbl, 0, 5, TAB_LEFT,
+ _("Correlation Between Forms"));
+
+
+ tab_text (tbl, 0, 6, TAB_LEFT,
+ _("Spearman-Brown Coefficient"));
+
+ tab_text (tbl, 1, 6, TAB_LEFT,
+ _("Equal Length"));
+
+ tab_text (tbl, 1, 7, TAB_LEFT,
+ _("Unequal Length"));
+
+
+ tab_text (tbl, 0, 8, TAB_LEFT,
+ _("Guttman Split-Half Coefficient"));
+
+
+
- tab_float (tbl, 3, 1, TAB_RIGHT, rel->sc[1].n_items, 8, 0);
- tab_float (tbl, 3, 3, TAB_RIGHT, rel->sc[2].n_items, 8, 0);
++ tab_double (tbl, 3, 0, TAB_RIGHT, rel->sc[1].alpha, NULL);
++ tab_double (tbl, 3, 2, TAB_RIGHT, rel->sc[2].alpha, NULL);
+
- tab_float (tbl, 3, 4, TAB_RIGHT,
- rel->sc[1].n_items + rel->sc[2].n_items, 8, 0);
++ tab_double (tbl, 3, 1, TAB_RIGHT, rel->sc[1].n_items, wfmt);
++ tab_double (tbl, 3, 3, TAB_RIGHT, rel->sc[2].n_items, wfmt);
+
- tab_float (tbl, 3, 5, TAB_RIGHT, r, 8, 3);
++ tab_double (tbl, 3, 4, TAB_RIGHT,
++ rel->sc[1].n_items + rel->sc[2].n_items, wfmt);
+
+ {
+ /* R is the correlation between the two parts */
+ double r = rel->sc[0].variance_of_sums -
+ rel->sc[1].variance_of_sums -
+ rel->sc[2].variance_of_sums ;
+
+ /* Guttman Split Half Coefficient */
+ double g = 2 * r / rel->sc[0].variance_of_sums;
+
+ /* Unequal Length Spearman Brown Coefficient, and
+ intermediate value used in the computation thereof */
+ double uly, tmp;
+
+ r /= sqrt (rel->sc[1].variance_of_sums);
+ r /= sqrt (rel->sc[2].variance_of_sums);
+ r /= 2.0;
+
- tab_float (tbl, 3, 6, TAB_RIGHT, 2 * r / (1.0 + r), 8, 3);
++ tab_double (tbl, 3, 5, TAB_RIGHT, r, NULL);
+
+ /* Equal length Spearman-Brown Coefficient */
- tab_float (tbl, 3, 8, TAB_RIGHT, g, 8, 3);
++ tab_double (tbl, 3, 6, TAB_RIGHT, 2 * r / (1.0 + r), NULL);
+
- tab_float (tbl, 3, 7, TAB_RIGHT, uly, 8, 3);
-
++ tab_double (tbl, 3, 8, TAB_RIGHT, g, NULL);
+
+ tmp = (1.0 - r*r) * rel->sc[1].n_items * rel->sc[2].n_items /
+ pow2 (rel->sc[0].n_items);
+
+ uly = sqrt( pow4 (r) + 4 * pow2 (r) * tmp);
+ uly -= pow2 (r);
+ uly /= 2 * tmp;
+
- case_processing_summary (casenumber n_valid, casenumber n_missing)
++ tab_double (tbl, 3, 7, TAB_RIGHT, uly, NULL);
+ }
+}
+
+
+
+static void
- tab_float (tbl, 2, heading_rows, TAB_RIGHT,
- n_valid, 8, 0);
++case_processing_summary (casenumber n_valid, casenumber n_missing,
++ const struct dictionary *dict)
+{
++ const struct variable *wv = dict_get_weight (dict);
++ const struct fmt_spec *wfmt = wv ? var_get_print_format (wv) : & F_8_0;
++
+ casenumber total;
+ int n_cols = 4;
+ int n_rows = 4;
+ int heading_columns = 2;
+ int heading_rows = 1;
+ struct tab_table *tbl;
+ tbl = tab_create (n_cols, n_rows, 0);
+ tab_headers (tbl, heading_columns, 0, heading_rows, 0);
+
+ tab_dim (tbl, tab_natural_dimensions);
+
+ tab_title (tbl, _("Case Processing Summary"));
+
+ /* Vertical lines for the data only */
+ tab_box (tbl,
+ -1, -1,
+ -1, TAL_1,
+ heading_columns, 0,
+ n_cols - 1, n_rows - 1);
+
+ /* Box around table */
+ tab_box (tbl,
+ TAL_2, TAL_2,
+ -1, -1,
+ 0, 0,
+ n_cols - 1, n_rows - 1);
+
+
+ tab_hline (tbl, TAL_2, 0, n_cols - 1, heading_rows);
+
+ tab_vline (tbl, TAL_2, heading_columns, 0, n_rows - 1);
+
+
+ tab_text (tbl, 0, heading_rows, TAB_LEFT | TAT_TITLE,
+ _("Cases"));
+
+ tab_text (tbl, 1, heading_rows, TAB_LEFT | TAT_TITLE,
+ _("Valid"));
+
+ tab_text (tbl, 1, heading_rows + 1, TAB_LEFT | TAT_TITLE,
+ _("Excluded"));
+
+ tab_text (tbl, 1, heading_rows + 2, TAB_LEFT | TAT_TITLE,
+ _("Total"));
+
+ tab_text (tbl, heading_columns, 0, TAB_CENTER | TAT_TITLE,
+ _("N"));
+
+ tab_text (tbl, heading_columns + 1, 0, TAB_CENTER | TAT_TITLE | TAT_PRINTF,
+ _("%%"));
+
+ total = n_missing + n_valid;
+
- tab_float (tbl, 2, heading_rows + 1, TAB_RIGHT,
- n_missing, 8, 0);
++ tab_double (tbl, 2, heading_rows, TAB_RIGHT,
++ n_valid, wfmt);
+
+
- tab_float (tbl, 2, heading_rows + 2, TAB_RIGHT,
- total, 8, 0);
++ tab_double (tbl, 2, heading_rows + 1, TAB_RIGHT,
++ n_missing, wfmt);
+
+
- tab_float (tbl, 3, heading_rows, TAB_RIGHT,
- 100 * n_valid / (double) total, 8, 1);
++ tab_double (tbl, 2, heading_rows + 2, TAB_RIGHT,
++ total, wfmt);
+
+
- tab_float (tbl, 3, heading_rows + 1, TAB_RIGHT,
- 100 * n_missing / (double) total, 8, 1);
++ tab_double (tbl, 3, heading_rows, TAB_RIGHT,
++ 100 * n_valid / (double) total, NULL);
+
+
- tab_float (tbl, 3, heading_rows + 2, TAB_RIGHT,
- 100 * total / (double) total, 8, 1);
++ tab_double (tbl, 3, heading_rows + 1, TAB_RIGHT,
++ 100 * n_missing / (double) total, NULL);
+
+
++ tab_double (tbl, 3, heading_rows + 2, TAB_RIGHT,
++ 100 * total / (double) total, NULL);
+
+
+ tab_submit (tbl);
+}
+
+static int
+rel_custom_model (struct lexer *lexer, struct dataset *ds UNUSED,
+ struct cmd_reliability *cmd UNUSED, void *aux)
+{
+ struct reliability *rel = aux;
+
+ if (lex_match_id (lexer, "ALPHA"))
+ {
+ rel->model = MODEL_ALPHA;
+ }
+ else if (lex_match_id (lexer, "SPLIT"))
+ {
+ rel->model = MODEL_SPLIT;
+ rel->split_point = -1;
+ if ( lex_match (lexer, '('))
+ {
+ lex_force_num (lexer);
+ rel->split_point = lex_number (lexer);
+ lex_get (lexer);
+ lex_force_match (lexer, ')');
+ }
+ }
+ else
+ return 0;
+
+ return 1;
+}
+
+
+
+static int
+rel_custom_scale (struct lexer *lexer, struct dataset *ds UNUSED,
+ struct cmd_reliability *p, void *aux)
+{
+ struct const_var_set *vs;
+ struct reliability *rel = aux;
+ struct cronbach *scale;
+
+ rel->n_sc = 1;
+ rel->sc = xzalloc (sizeof (struct cronbach) * rel->n_sc);
+ scale = &rel->sc[0];
+
+ if ( ! lex_force_match (lexer, '(')) return 0;
+
+ if ( ! lex_force_string (lexer) ) return 0;
+
+ ds_init_string (&rel->scale_name, lex_tokstr (lexer));
+
+ lex_get (lexer);
+
+ if ( ! lex_force_match (lexer, ')')) return 0;
+
+ lex_match (lexer, '=');
+
+ vs = const_var_set_create_from_array (p->v_variables, p->n_variables);
+
+ if (!parse_const_var_set_vars (lexer, vs, &scale->items, &scale->n_items, 0))
+ {
+ const_var_set_destroy (vs);
+ return 2;
+ }
+
+ const_var_set_destroy (vs);
+ return 1;
+}
+
+/*
+ Local Variables:
+ mode: c
+ End:
+*/
--- /dev/null
- const struct two_sample_test *);
+/* Pspp - a program for statistical analysis.
+ Copyright (C) 2008, 2009 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 "wilcoxon.h"
+#include <data/variable.h>
+#include <data/casereader.h>
+#include <data/casewriter.h>
+#include <data/subcase.h>
+#include <math/sort.h>
+#include <libpspp/message.h>
+#include <xalloc.h>
+#include <output/table.h>
+#include <data/procedure.h>
+#include <data/dictionary.h>
+#include <math/wilcoxon-sig.h>
+#include <gsl/gsl_cdf.h>
+#include <unistd.h>
+#include <signal.h>
+#include <libpspp/assertion.h>
++#include <data/format.h>
+
+static double
+append_difference (const struct ccase *c, casenumber n UNUSED, void *aux)
+{
+ const variable_pair *vp = aux;
+
+ return case_data (c, (*vp)[0])->f - case_data (c, (*vp)[1])->f;
+}
+
+static void show_ranks_box (const struct wilcoxon_state *,
- show_ranks_box (ws, t2s);
++ const struct two_sample_test *,
++ const struct dictionary *);
+
+static void show_tests_box (const struct wilcoxon_state *,
+ const struct two_sample_test *,
+ bool exact, double timer);
+
+
+
+static void
+distinct_callback (double v UNUSED, casenumber n, double w UNUSED, void *aux)
+{
+ struct wilcoxon_state *ws = aux;
+
+ ws->tiebreaker += pow3 (n) - n;
+}
+
+#define WEIGHT_IDX 2
+
+void
+wilcoxon_execute (const struct dataset *ds,
+ struct casereader *input,
+ enum mv_class exclude,
+ const struct npar_test *test,
+ bool exact,
+ double timer)
+{
+ int i;
+ bool warn = true;
+ const struct dictionary *dict = dataset_dict (ds);
+ const struct two_sample_test *t2s = (struct two_sample_test *) test;
+
+ struct wilcoxon_state *ws = xcalloc (sizeof (*ws), t2s->n_pairs);
+ const struct variable *weight = dict_get_weight (dict);
+ struct variable *weightx = var_create_internal (WEIGHT_IDX);
+
+ input =
+ casereader_create_filter_weight (input, dict, &warn, NULL);
+
+ for (i = 0 ; i < t2s->n_pairs; ++i )
+ {
+ struct casereader *r = casereader_clone (input);
+ struct casewriter *writer;
+ struct ccase *c;
+ struct subcase ordering;
+ variable_pair *vp = &t2s->pairs[i];
+
+ const int reader_width = weight ? 3 : 2;
+
+ ws[i].sign = var_create_internal (0);
+ ws[i].absdiff = var_create_internal (1);
+
+ r = casereader_create_filter_missing (r, *vp, 2,
+ exclude,
+ NULL, NULL);
+
+ subcase_init_var (&ordering, ws[i].absdiff, SC_ASCEND);
+ writer = sort_create_writer (&ordering, reader_width);
+ subcase_destroy (&ordering);
+
+ for (; (c = casereader_read (r)) != NULL; case_unref (c))
+ {
+ struct ccase *output = case_create (reader_width);
+ double d = append_difference (c, 0, vp);
+
+ if (d > 0)
+ {
+ case_data_rw (output, ws[i].sign)->f = 1.0;
+
+ }
+ else if (d < 0)
+ {
+ case_data_rw (output, ws[i].sign)->f = -1.0;
+ }
+ else
+ {
+ double w = 1.0;
+ if (weight)
+ w = case_data (c, weight)->f;
+
+ /* Central point values should be dropped */
+ ws[i].n_zeros += w;
+ case_unref (output);
+ continue;
+ }
+
+ case_data_rw (output, ws[i].absdiff)->f = fabs (d);
+
+ if (weight)
+ case_data_rw (output, weightx)->f = case_data (c, weight)->f;
+
+ casewriter_write (writer, output);
+ }
+ casereader_destroy (r);
+ ws[i].reader = casewriter_make_reader (writer);
+ }
+
+ for (i = 0 ; i < t2s->n_pairs; ++i )
+ {
+ struct casereader *rr ;
+ struct ccase *c;
+ enum rank_error err = 0;
+
+ rr = casereader_create_append_rank (ws[i].reader, ws[i].absdiff,
+ weight ? weightx : NULL, &err,
+ distinct_callback, &ws[i]
+ );
+
+ for (; (c = casereader_read (rr)) != NULL; case_unref (c))
+ {
+ double sign = case_data (c, ws[i].sign)->f;
+ double rank = case_data_idx (c, weight ? 3 : 2)->f;
+ double w = 1.0;
+ if (weight)
+ w = case_data (c, weightx)->f;
+
+ if ( sign > 0 )
+ {
+ ws[i].positives.sum += rank * w;
+ ws[i].positives.n += w;
+ }
+ else if (sign < 0)
+ {
+ ws[i].negatives.sum += rank * w;
+ ws[i].negatives.n += w;
+ }
+ else
+ NOT_REACHED ();
+ }
+
+ casereader_destroy (rr);
+ }
+
+ casereader_destroy (input);
+
+ var_destroy (weightx);
+
- show_ranks_box (const struct wilcoxon_state *ws, const struct two_sample_test *t2s)
++ show_ranks_box (ws, t2s, dict);
+ show_tests_box (ws, t2s, exact, timer);
+
+ for (i = 0 ; i < t2s->n_pairs; ++i )
+ {
+ var_destroy (ws[i].sign);
+ var_destroy (ws[i].absdiff);
+ }
+
+ free (ws);
+}
+
+
+\f
+
+#include "gettext.h"
+#define _(msgid) gettext (msgid)
+
+static void
- tab_float (table, 2, 1 + i * 4, TAB_RIGHT, ws[i].negatives.n, 8, 0);
- tab_float (table, 2, 2 + i * 4, TAB_RIGHT, ws[i].positives.n, 8, 0);
- tab_float (table, 2, 3 + i * 4, TAB_RIGHT, ws[i].n_zeros, 8, 0);
++show_ranks_box (const struct wilcoxon_state *ws,
++ const struct two_sample_test *t2s,
++ const struct dictionary *dict)
+{
+ size_t i;
++
++ const struct variable *wv = dict_get_weight (dict);
++ const struct fmt_spec *wfmt = wv ? var_get_print_format (wv) : & F_8_0;
++
+ struct tab_table *table = tab_create (5, 1 + 4 * t2s->n_pairs, 0);
+
+ tab_dim (table, tab_natural_dimensions);
+
+ tab_title (table, _("Ranks"));
+
+ tab_headers (table, 2, 0, 1, 0);
+
+ /* Vertical lines inside the box */
+ tab_box (table, 0, 0, -1, TAL_1,
+ 1, 0, table->nc - 1, tab_nr (table) - 1 );
+
+ /* Box around entire table */
+ tab_box (table, TAL_2, TAL_2, -1, -1,
+ 0, 0, table->nc - 1, tab_nr (table) - 1 );
+
+
+ tab_text (table, 2, 0, TAB_CENTER, _("N"));
+ tab_text (table, 3, 0, TAB_CENTER, _("Mean Rank"));
+ tab_text (table, 4, 0, TAB_CENTER, _("Sum of Ranks"));
+
+
+ for (i = 0 ; i < t2s->n_pairs; ++i)
+ {
+ variable_pair *vp = &t2s->pairs[i];
+
+ struct string pair_name;
+ ds_init_cstr (&pair_name, var_to_string ((*vp)[0]));
+ ds_put_cstr (&pair_name, " - ");
+ ds_put_cstr (&pair_name, var_to_string ((*vp)[1]));
+
+ tab_text (table, 1, 1 + i * 4, TAB_LEFT, _("Negative Ranks"));
+ tab_text (table, 1, 2 + i * 4, TAB_LEFT, _("Positive Ranks"));
+ tab_text (table, 1, 3 + i * 4, TAB_LEFT, _("Ties"));
+ tab_text (table, 1, 4 + i * 4, TAB_LEFT, _("Total"));
+
+ tab_hline (table, TAL_1, 0, table->nc - 1, 1 + i * 4);
+
+
+ tab_text (table, 0, 1 + i * 4, TAB_LEFT, ds_cstr (&pair_name));
+ ds_destroy (&pair_name);
+
+
+ /* N */
- tab_float (table, 2, 4 + i * 4, TAB_RIGHT,
- ws[i].n_zeros + ws[i].positives.n + ws[i].negatives.n, 8, 0);
++ tab_double (table, 2, 1 + i * 4, TAB_RIGHT, ws[i].negatives.n, wfmt);
++ tab_double (table, 2, 2 + i * 4, TAB_RIGHT, ws[i].positives.n, wfmt);
++ tab_double (table, 2, 3 + i * 4, TAB_RIGHT, ws[i].n_zeros, wfmt);
+
- tab_float (table, 4, 1 + i * 4, TAB_RIGHT, ws[i].negatives.sum, 8, 2);
- tab_float (table, 4, 2 + i * 4, TAB_RIGHT, ws[i].positives.sum, 8, 2);
++ tab_double (table, 2, 4 + i * 4, TAB_RIGHT,
++ ws[i].n_zeros + ws[i].positives.n + ws[i].negatives.n, wfmt);
+
+ /* Sums */
- tab_float (table, 3, 1 + i * 4, TAB_RIGHT,
- ws[i].negatives.sum / (double) ws[i].negatives.n, 8, 2);
++ tab_double (table, 4, 1 + i * 4, TAB_RIGHT, ws[i].negatives.sum, NULL);
++ tab_double (table, 4, 2 + i * 4, TAB_RIGHT, ws[i].positives.sum, NULL);
+
+
+ /* Means */
- tab_float (table, 3, 2 + i * 4, TAB_RIGHT,
- ws[i].positives.sum / (double) ws[i].positives.n, 8, 2);
++ tab_double (table, 3, 1 + i * 4, TAB_RIGHT,
++ ws[i].negatives.sum / (double) ws[i].negatives.n, NULL);
+
- tab_float (table, 1 + i, 1, TAB_RIGHT, z, 8, 3);
++ tab_double (table, 3, 2 + i * 4, TAB_RIGHT,
++ ws[i].positives.sum / (double) ws[i].positives.n, NULL);
+
+ }
+
+ tab_hline (table, TAL_2, 0, table->nc - 1, 1);
+ tab_vline (table, TAL_2, 2, 0, table->nr - 1);
+
+
+ tab_submit (table);
+}
+
+
+static void
+show_tests_box (const struct wilcoxon_state *ws,
+ const struct two_sample_test *t2s,
+ bool exact,
+ double timer UNUSED
+ )
+{
+ size_t i;
+ struct tab_table *table = tab_create (1 + t2s->n_pairs, exact ? 5 : 3, 0);
+
+ tab_dim (table, tab_natural_dimensions);
+
+ tab_title (table, _("Test Statistics"));
+
+ tab_headers (table, 1, 0, 1, 0);
+
+ /* Vertical lines inside the box */
+ tab_box (table, 0, 0, -1, TAL_1,
+ 0, 0, table->nc - 1, tab_nr (table) - 1 );
+
+ /* Box around entire table */
+ tab_box (table, TAL_2, TAL_2, -1, -1,
+ 0, 0, table->nc - 1, tab_nr (table) - 1 );
+
+
+ tab_text (table, 0, 1, TAB_LEFT, _("Z"));
+ tab_text (table, 0, 2, TAB_LEFT, _("Asymp. Sig (2-tailed)"));
+
+ if ( exact )
+ {
+ tab_text (table, 0, 3, TAB_LEFT, _("Exact Sig (2-tailed)"));
+ tab_text (table, 0, 4, TAB_LEFT, _("Exact Sig (1-tailed)"));
+
+#if 0
+ tab_text (table, 0, 5, TAB_LEFT, _("Point Probability"));
+#endif
+ }
+
+ for (i = 0 ; i < t2s->n_pairs; ++i)
+ {
+ double z;
+ double n = ws[i].positives.n + ws[i].negatives.n;
+ variable_pair *vp = &t2s->pairs[i];
+
+ struct string pair_name;
+ ds_init_cstr (&pair_name, var_to_string ((*vp)[0]));
+ ds_put_cstr (&pair_name, " - ");
+ ds_put_cstr (&pair_name, var_to_string ((*vp)[1]));
+
+
+ tab_text (table, 1 + i, 0, TAB_CENTER, ds_cstr (&pair_name));
+ ds_destroy (&pair_name);
+
+ z = MIN (ws[i].positives.sum, ws[i].negatives.sum);
+ z -= n * (n + 1)/ 4.0;
+
+ z /= sqrt (n * (n + 1) * (2*n + 1)/24.0 - ws[i].tiebreaker / 48.0);
+
- tab_float (table, 1 + i, 2, TAB_RIGHT,
++ tab_double (table, 1 + i, 1, TAB_RIGHT, z, NULL);
+
- 8, 3);
++ tab_double (table, 1 + i, 2, TAB_RIGHT,
+ 2.0 * gsl_cdf_ugaussian_P (z),
- tab_float (table, 1 + i, 3, TAB_RIGHT, p, 8, 3);
- tab_float (table, 1 + i, 4, TAB_RIGHT, p / 2.0, 8, 3);
++ NULL);
+
+ if (exact)
+ {
+ double p = LevelOfSignificanceWXMPSR (ws[i].positives.sum, n);
+ if (p < 0)
+ {
+ msg (MW, ("Too many pairs to calculate exact significance."));
+ }
+ else
+ {
++ tab_double (table, 1 + i, 3, TAB_RIGHT, p, NULL);
++ tab_double (table, 1 + i, 4, TAB_RIGHT, p / 2.0, NULL);
+ }
+ }
+ }
+
+ tab_hline (table, TAL_2, 0, table->nc - 1, 1);
+ tab_vline (table, TAL_2, 1, 0, table->nr - 1);
+
+
+ tab_submit (table);
+}
perl -pi -e 's/^\s*$//g' $TEMPDIR/pspp.list
diff -b $TEMPDIR/pspp.list - << EOF
1.1 EXAMINE. Case Processing Summary
- #==#===============================#
- # # Cases #
- # #----------+---------+----------#
- # # Valid | Missing | Total #
- # #--+-------+-+-------+--+-------#
- # # N|Percent|N|Percent| N|Percent#
- #==#==#=======#=#=======#==#=======#
- #V1#23| 100%|0| 0%|23| 100%#
- #==#==#=======#=#=======#==#=======#
+ #==#=======================================#
+ # # Cases #
+ # #-------------+-----------+-------------#
+ # # Valid | Missing | Total #
+ # #-----+-------+---+-------+-----+-------#
+ # # N |Percent| N |Percent| N |Percent#
+ #==#=====#=======#===#=======#=====#=======#
+ #V1#23.00| 100%|.00| 0%|23.00| 100%#
+ #==#=====#=======#===#=======#=====#=======#
1.2 EXAMINE. Extreme Values
-#============#===========#========#
-# #Case Number| Value #
-#============#===========#========#
-#V1 Highest 1# 21| 20.00#
-# 2# 20| 19.00#
-# 3# 19| 18.00#
-# 4# 19| 18.00#
-# 5# 18| 17.00#
-# 6# 17| 16.00#
-# ----------#-----------+--------#
-# Lowest 1# 1| 1.00#
-# 2# 2| 2.00#
-# 3# 4| 3.00#
-# 4# 3| 3.00#
-# 5# 3| 3.00#
-# 6# 5| 4.00#
-#============#===========#========#
+#============#===========#=====#
+# #Case Number|Value#
+#============#===========#=====#
+#V1 Highest 1# 21|20.00#
+# 2# 20|19.00#
+# 3# 19|18.00#
+# 4# 19|18.00#
+# 5# 18|17.00#
+# 6# 17|16.00#
+# ----------#-----------+-----#
+# Lowest 1# 1| 1.00#
+# 2# 2| 2.00#
+# 3# 3| 3.00#
+# 4# 3| 3.00#
+# 5# 4| 3.00#
+# 6# 5| 4.00#
+#============#===========#=====#
EOF
if [ $? -ne 0 ] ; then fail ; fi
+--------+------+
Case# QUALITY W BRAND
----- -------- -------- --------
- 1 3.00 1.00 1.00
- 2 2.00 2.00 1.00
- 3 1.00 2.00 1.00
- 4 1.00 1.00 1.00
- 5 4.00 1.00 1.00
- 6 4.00 1.00 1.00
- 7 5.00 1.00 2.00
- 8 2.00 1.00 2.00
- 9 4.00 4.00 2.00
- 10 2.00 1.00 2.00
- 11 3.00 1.00 2.00
- 12 7.00 1.00 3.00
- 13 4.00 2.00 3.00
- 14 5.00 3.00 3.00
- 15 3.00 1.00 3.00
- 16 6.00 1.00 3.00
+ 1 3.00 1.00 1.00
+ 2 2.00 2.00 1.00
+ 3 1.00 2.00 1.00
+ 4 1.00 1.00 1.00
+ 5 4.00 1.00 1.00
+ 6 4.00 1.00 1.00
+ 7 5.00 1.00 2.00
+ 8 2.00 1.00 2.00
+ 9 4.00 4.00 2.00
+ 10 2.00 1.00 2.00
+ 11 3.00 1.00 2.00
+ 12 7.00 1.00 3.00
+ 13 4.00 2.00 3.00
+ 14 5.00 3.00 3.00
+ 15 3.00 1.00 3.00
+ 16 6.00 1.00 3.00
2.1 EXAMINE. Case Processing Summary
- #===============#===============================#
- # # Cases #
- # #----------+---------+----------#
- # # Valid | Missing | Total #
- # #--+-------+-+-------+--+-------#
- # # N|Percent|N|Percent| N|Percent#
- #===============#==#=======#=#=======#==#=======#
- #Breaking Strain#24| 100%|0| 0%|24| 100%#
- #===============#==#=======#=#=======#==#=======#
+ #===============#=======================================#
+ # # Cases #
+ # #-------------+-----------+-------------#
+ # # Valid | Missing | Total #
+ # #-----+-------+---+-------+-----+-------#
+ # # N |Percent| N |Percent| N |Percent#
+ #===============#=====#=======#===#=======#=====#=======#
+ #Breaking Strain#24.00| 100%|.00| 0%|24.00| 100%#
+ #===============#=====#=======#===#=======#=====#=======#
2.2 EXAMINE. Extreme Values
-#=========================#===========#========#
-# #Case Number| Value #
-#=========================#===========#========#
-#Breaking Strain Highest 1# 12| 7.00#
-# 2# 16| 6.00#
-# 3# 14| 5.00#
-# ----------#-----------+--------#
-# Lowest 1# 4| 1.00#
-# 2# 3| 1.00#
-# 3# 3| 1.00#
-#=========================#===========#========#
+#=========================#===========#=====#
+# #Case Number|Value#
+#=========================#===========#=====#
+#Breaking Strain Highest 1# 12| 7.00#
+# 2# 16| 6.00#
+# 3# 7| 5.00#
+# ----------#-----------+-----#
+# Lowest 1# 3| 1.00#
+# 2# 3| 1.00#
+# 3# 4| 1.00#
+#=========================#===========#=====#
2.3 EXAMINE. Descriptives
#============================================================#=========#==========#
# #Statistic|Std. Error#
#============================================================#=========#==========#
- #Breaking Strain Mean # 3.54 | .324 #
- # 95% Confidence Interval for Mean Lower Bound# 2.871 | #
- # Upper Bound# 4.212 | #
+ #Breaking Strain Mean # 3.54 | .32 #
+ # 95% Confidence Interval for Mean Lower Bound# 2.87 | #
+ # Upper Bound# 4.21 | #
# 5% Trimmed Mean # 3.50 | #
# Median # 4.00 | #
- # Variance # 2.520 | #
- # Std. Deviation # 1.587 | #
- # Minimum # 1.000 | #
- # Maximum # 7.000 | #
- # Range # 6.000 | #
+ # Variance # 2.52 | #
+ # Std. Deviation # 1.59 | #
+ # Minimum # 1.00 | #
+ # Maximum # 7.00 | #
+ # Range # 6.00 | #
# Interquartile Range # 2.75 | #
- # Skewness # .059 | .472 #
- # Kurtosis # -.358 | .918 #
+ # Skewness # .06 | .47 #
+ # Kurtosis # -.36 | .92 #
#============================================================#=========#==========#
2.4 EXAMINE. Case Processing Summary
- #============================#=============================#
- # # Cases #
- # #---------+---------+---------#
- # # Valid | Missing | Total #
- # #-+-------+-+-------+-+-------#
- # Manufacturer#N|Percent|N|Percent|N|Percent#
- #============================#=#=======#=#=======#=#=======#
- #Breaking Strain Aspeger #8| 100%|0| 0%|8| 100%#
- # Bloggs #8| 100%|0| 0%|8| 100%#
- # Charlies #8| 100%|0| 0%|8| 100%#
- #============================#=#=======#=#=======#=#=======#
+ #============================#=====================================#
+ # # Cases #
+ # #------------+-----------+------------#
+ # # Valid | Missing | Total #
+ # #----+-------+---+-------+----+-------#
+ # Manufacturer# N |Percent| N |Percent| N |Percent#
+ #============================#====#=======#===#=======#====#=======#
+ #Breaking Strain Aspeger #8.00| 100%|.00| 0%|8.00| 100%#
+ # Bloggs #8.00| 100%|.00| 0%|8.00| 100%#
+ # Charlies #8.00| 100%|.00| 0%|8.00| 100%#
+ #============================#====#=======#===#=======#====#=======#
2.5 EXAMINE. Extreme Values
-#======================================#===========#========#
-# Manufacturer #Case Number| Value #
-#======================================#===========#========#
-#Breaking Strain Aspeger Highest 1# 6| 4.00#
-# 2# 5| 4.00#
-# 3# 1| 3.00#
-# ----------#-----------+--------#
-# Lowest 1# 4| 1.00#
-# 2# 3| 1.00#
-# 3# 3| 1.00#
-# -----------------------#-----------+--------#
-# Bloggs Highest 1# 7| 5.00#
-# 2# 9| 4.00#
-# 3# 9| 4.00#
-# ----------#-----------+--------#
-# Lowest 1# 10| 2.00#
-# 2# 8| 2.00#
-# 3# 11| 3.00#
-# -----------------------#-----------+--------#
-# Charlies Highest 1# 12| 7.00#
-# 2# 16| 6.00#
-# 3# 14| 5.00#
-# ----------#-----------+--------#
-# Lowest 1# 15| 3.00#
-# 2# 13| 4.00#
-# 3# 13| 4.00#
-#======================================#===========#========#
+#======================================#===========#=====#
+# Manufacturer #Case Number|Value#
+#======================================#===========#=====#
+#Breaking Strain Aspeger Highest 1# 5| 4.00#
+# 2# 6| 4.00#
+# 3# 1| 3.00#
+# ----------#-----------+-----#
+# Lowest 1# 3| 1.00#
+# 2# 3| 1.00#
+# 3# 4| 1.00#
+# -----------------------#-----------+-----#
+# Bloggs Highest 1# 7| 5.00#
+# 2# 9| 4.00#
+# 3# 9| 4.00#
+# ----------#-----------+-----#
+# Lowest 1# 8| 2.00#
+# 2# 10| 2.00#
+# 3# 11| 3.00#
+# -----------------------#-----------+-----#
+# Charlies Highest 1# 12| 7.00#
+# 2# 16| 6.00#
+# 3# 14| 5.00#
+# ----------#-----------+-----#
+# Lowest 1# 15| 3.00#
+# 2# 13| 4.00#
+# 3# 13| 4.00#
+#======================================#===========#=====#
2.6 EXAMINE. Descriptives
#=========================================================================#=========#==========#
- # Manufacturer #Statistic|Std. Error#
+ # Manufacturer #Statistic|Std. Error#
#=========================================================================#=========#==========#
- #Breaking Strain Aspeger Mean # 2.25 | .453 #
- # 95% Confidence Interval for Mean Lower Bound# 1.178 | #
- # Upper Bound# 3.322 | #
+ #Breaking Strain Aspeger Mean # 2.25 | .45 #
+ # 95% Confidence Interval for Mean Lower Bound# 1.18 | #
+ # Upper Bound# 3.32 | #
# 5% Trimmed Mean # 2.22 | #
# Median # 2.00 | #
- # Variance # 1.643 | #
- # Std. Deviation # 1.282 | #
- # Minimum # 1.000 | #
- # Maximum # 4.000 | #
- # Range # 3.000 | #
+ # Variance # 1.64 | #
+ # Std. Deviation # 1.28 | #
+ # Minimum # 1.00 | #
+ # Maximum # 4.00 | #
+ # Range # 3.00 | #
# Interquartile Range # 2.75 | #
- # Skewness # .475 | .752 #
- # Kurtosis # -1.546 | 1.481 #
+ # Skewness # .47 | .75 #
+ # Kurtosis # -1.55 | 1.48 #
# ----------------------------------------------------------#---------+----------#
- # Bloggs Mean # 3.50 | .378 #
- # 95% Confidence Interval for Mean Lower Bound# 2.606 | #
- # Upper Bound# 4.394 | #
+ # Bloggs Mean # 3.50 | .38 #
+ # 95% Confidence Interval for Mean Lower Bound# 2.61 | #
+ # Upper Bound# 4.39 | #
# 5% Trimmed Mean # 3.50 | #
# Median # 4.00 | #
- # Variance # 1.143 | #
- # Std. Deviation # 1.069 | #
- # Minimum # 2.000 | #
- # Maximum # 5.000 | #
- # Range # 3.000 | #
+ # Variance # 1.14 | #
+ # Std. Deviation # 1.07 | #
+ # Minimum # 2.00 | #
+ # Maximum # 5.00 | #
+ # Range # 3.00 | #
# Interquartile Range # 1.75 | #
- # Skewness # -.468 | .752 #
- # Kurtosis # -.831 | 1.481 #
+ # Skewness # -.47 | .75 #
+ # Kurtosis # -.83 | 1.48 #
# ----------------------------------------------------------#---------+----------#
- # Charlies Mean # 4.88 | .441 #
- # 95% Confidence Interval for Mean Lower Bound# 3.833 | #
- # Upper Bound# 5.917 | #
+ # Charlies Mean # 4.88 | .44 #
+ # 95% Confidence Interval for Mean Lower Bound# 3.83 | #
+ # Upper Bound# 5.92 | #
# 5% Trimmed Mean # 4.86 | #
# Median # 5.00 | #
- # Variance # 1.554 | #
- # Std. Deviation # 1.246 | #
- # Minimum # 3.000 | #
- # Maximum # 7.000 | #
- # Range # 4.000 | #
+ # Variance # 1.55 | #
+ # Std. Deviation # 1.25 | #
+ # Minimum # 3.00 | #
+ # Maximum # 7.00 | #
+ # Range # 4.00 | #
# Interquartile Range # 1.75 | #
- # Skewness # .304 | .752 #
- # Kurtosis # .146 | 1.481 #
+ # Skewness # .30 | .75 #
+ # Kurtosis # .15 | 1.48 #
#=========================================================================#=========#==========#
EOF
if [ $? -ne 0 ] ; then fail ; fi
diff -b $TEMPDIR/pspp.list - << EOF
P < 0.5; N1/N2 < 1
1.1 NPAR TESTS. Binomial Test
- +-+------#--------+--+--------------+----------+---------------------+
- | | #Category| N|Observed Prop.|Test Prop.|Exact Sig. (1-tailed)|
- +-+------#--------+--+--------------+----------+---------------------+
- |x|Group1# 1.00| 6| .286| .300| .551|
- | |Group2# 2.00|15| .714| | |
- | |Total # |21| 1.00| | |
- +-+------#--------+--+--------------+----------+---------------------+
+ +-+------#--------+-----+--------------+----------+---------------------+
+ | | #Category| N |Observed Prop.|Test Prop.|Exact Sig. (1-tailed)|
+ +-+------#--------+-----+--------------+----------+---------------------+
+ |x|Group1# 1.00| 6.00| .286| .300| .551|
+ | |Group2# 2.00|15.00| .714| | |
-| |Total # |21.00| 1.00| | |
++| |Total # |21.00| 1.000| | |
+ +-+------#--------+-----+--------------+----------+---------------------+
P < 0.5; N1/N2 > 1
2.1 NPAR TESTS. Binomial Test
+-+------#--------+--+--------------+----------+---------------------+
| | #Category| N|Observed Prop.|Test Prop.|Exact Sig. (1-tailed)|
+-+------#--------+--+--------------+----------+---------------------+
- |x|Group1# 1.00| 7| .538| .400| .229|
- | |Group2# 2.00| 6| .462| | |
- | |Total # |13| 1.00| | |
+ |x|Group1# 1| 7| .538| .400| .229|
+ | |Group2# 2| 6| .462| | |
-| |Total # |13| 1| | |
++| |Total # |13| 1.000| | |
+-+------#--------+--+--------------+----------+---------------------+
P < 0.5; N1/N2 = 1
3.1 NPAR TESTS. Binomial Test
+-+------#--------+--+--------------+----------+---------------------+
| | #Category| N|Observed Prop.|Test Prop.|Exact Sig. (1-tailed)|
+-+------#--------+--+--------------+----------+---------------------+
- |x|Group1# 1.00| 8| .500| .400| .284|
- | |Group2# 2.00| 8| .500| | |
- | |Total # |16| 1.00| | |
+ |x|Group1# 1| 8| .500| .400| .284|
+ | |Group2# 2| 8| .500| | |
-| |Total # |16| 1| | |
++| |Total # |16| 1.000| | |
+-+------#--------+--+--------------+----------+---------------------+
P > 0.5; N1/N2 < 1
4.1 NPAR TESTS. Binomial Test
+-+------#--------+--+--------------+----------+---------------------+
| | #Category| N|Observed Prop.|Test Prop.|Exact Sig. (1-tailed)|
+-+------#--------+--+--------------+----------+---------------------+
- |x|Group1# 1.00|11| .478| .600| .164|
- | |Group2# 2.00|12| .522| | |
- | |Total # |23| 1.00| | |
+ |x|Group1# 1|11| .478| .600| .164|
+ | |Group2# 2|12| .522| | |
-| |Total # |23| 1| | |
++| |Total # |23| 1.000| | |
+-+------#--------+--+--------------+----------+---------------------+
P > 0.5; N1/N2 > 1
5.1 NPAR TESTS. Binomial Test
+-+------#--------+--+--------------+----------+---------------------+
| | #Category| N|Observed Prop.|Test Prop.|Exact Sig. (1-tailed)|
+-+------#--------+--+--------------+----------+---------------------+
- |x|Group1# 1.00|11| .550| .600| .404|
- | |Group2# 2.00| 9| .450| | |
- | |Total # |20| 1.00| | |
+ |x|Group1# 1|11| .550| .600| .404|
+ | |Group2# 2| 9| .450| | |
-| |Total # |20| 1| | |
++| |Total # |20| 1.000| | |
+-+------#--------+--+--------------+----------+---------------------+
P > 0.5; N1/N2 == 1
6.1 NPAR TESTS. Binomial Test
+-+------#--------+--+--------------+----------+---------------------+
| | #Category| N|Observed Prop.|Test Prop.|Exact Sig. (1-tailed)|
+-+------#--------+--+--------------+----------+---------------------+
- |x|Group1# 1.00|11| .500| .600| .228|
- | |Group2# 2.00|11| .500| | |
- | |Total # |22| 1.00| | |
+ |x|Group1# 1|11| .500| .600| .228|
+ | |Group2# 2|11| .500| | |
-| |Total # |22| 1| | |
++| |Total # |22| 1.000| | |
+-+------#--------+--+--------------+----------+---------------------+
P == 0.5; N1/N2 < 1
7.1 NPAR TESTS. Binomial Test
+-+------#--------+--+--------------+----------+---------------------+
| | #Category| N|Observed Prop.|Test Prop.|Exact Sig. (2-tailed)|
+-+------#--------+--+--------------+----------+---------------------+
- |x|Group1# 1.00| 8| .348| .500| .210|
- | |Group2# 2.00|15| .652| | |
- | |Total # |23| 1.00| | |
+ |x|Group1# 1| 8| .348| .500| .210|
+ | |Group2# 2|15| .652| | |
-| |Total # |23| 1| | |
++| |Total # |23| 1.000| | |
+-+------#--------+--+--------------+----------+---------------------+
P == 0.5; N1/N2 > 1
8.1 NPAR TESTS. Binomial Test
+-+------#--------+--+--------------+----------+---------------------+
| | #Category| N|Observed Prop.|Test Prop.|Exact Sig. (2-tailed)|
+-+------#--------+--+--------------+----------+---------------------+
- |x|Group1# 1.00|12| .667| .500| .238|
- | |Group2# 2.00| 6| .333| | |
- | |Total # |18| 1.00| | |
+ |x|Group1# 1|12| .667| .500| .238|
+ | |Group2# 2| 6| .333| | |
-| |Total # |18| 1| | |
++| |Total # |18| 1.000| | |
+-+------#--------+--+--------------+----------+---------------------+
P == 0.5; N1/N2 == 1
9.1 NPAR TESTS. Binomial Test
+-+------#--------+--+--------------+----------+---------------------+
| | #Category| N|Observed Prop.|Test Prop.|Exact Sig. (2-tailed)|
+-+------#--------+--+--------------+----------+---------------------+
- |x|Group1# 1.00|10| .500| .500| 1.000|
- | |Group2# 2.00|10| .500| | |
- | |Total # |20| 1.00| | |
+ |x|Group1# 1|10| .500| .500| 1.000|
+ | |Group2# 2|10| .500| | |
-| |Total # |20| 1| | |
++| |Total # |20| 1.000| | |
+-+------#--------+--+--------------+----------+---------------------+
- +-+------#--------+--+--------------+----------+---------------------+
- | | #Category| N|Observed Prop.|Test Prop.|Exact Sig. (2-tailed)|
- +-+------#--------+--+--------------+----------+---------------------+
- |x|Group1# <= 10|10| .385| .500| .327|
- | |Group2# |16| .615| | |
- | |Total # |26| 1.00| | |
- +-+------#--------+--+--------------+----------+---------------------+
+P == 0.5; N1/N2 == 1 Cutpoint
+10.1 NPAR TESTS. Binomial Test
- +-+------#--------+--+--------------+----------+---------------------+
- | | #Category| N|Observed Prop.|Test Prop.|Exact Sig. (2-tailed)|
- +-+------#--------+--+--------------+----------+---------------------+
- |x|Group1# 10.00|10| .435| .500| .678|
- | |Group2# 20.00|13| .565| | |
- | |Total # |23| 1.00| | |
- +-+------#--------+--+--------------+----------+---------------------+
+++-+------#--------+------+--------------+----------+---------------------+
++| | #Category| N |Observed Prop.|Test Prop.|Exact Sig. (2-tailed)|
+++-+------#--------+------+--------------+----------+---------------------+
++|x|Group1# <= 10|10.000| .385| .500| .327|
++| |Group2# |16.000| .615| | |
++| |Total # |26.000| 1.000| | |
+++-+------#--------+------+--------------+----------+---------------------+
+P == 0.5; N1/N2 == 1 Named values
+11.1 NPAR TESTS. Binomial Test
+++-+------#--------+------+--------------+----------+---------------------+
++| | #Category| N |Observed Prop.|Test Prop.|Exact Sig. (2-tailed)|
+++-+------#--------+------+--------------+----------+---------------------+
++|x|Group1# 10.000|10.000| .435| .500| .678|
++| |Group2# 20.000|13.000| .565| | |
++| |Total # |23.000| 1.000| | |
+++-+------#--------+------+--------------+----------+---------------------+
EOF
if [ $? -ne 0 ] ; then fail ; fi
--- /dev/null
- data list notable list /foo * bar * w *.
+#!/bin/sh
+
+# This program tests the wilcoxon subcommand of npar tests
+
+TEMPDIR=/tmp/pspp-tst-$$
+TESTFILE=$TEMPDIR/`basename $0`.sps
+
+# ensure that top_srcdir and top_builddir are absolute
+if [ -z "$top_srcdir" ] ; then top_srcdir=. ; fi
+if [ -z "$top_builddir" ] ; then top_builddir=. ; fi
+top_srcdir=`cd $top_srcdir; pwd`
+top_builddir=`cd $top_builddir; pwd`
+
+PSPP=$top_builddir/src/ui/terminal/pspp
+
+STAT_CONFIG_PATH=$top_srcdir/config
+export STAT_CONFIG_PATH
+
+LANG=C
+export LANG
+
+
+cleanup()
+{
+ if [ x"$PSPP_TEST_NO_CLEANUP" != x ] ; then
+ echo "NOT cleaning $TEMPDIR"
+ return ;
+ fi
+ rm -rf $TEMPDIR
+}
+
+
+fail()
+{
+ echo $activity
+ echo FAILED
+ cleanup;
+ exit 1;
+}
+
+
+no_result()
+{
+ echo $activity
+ echo NO RESULT;
+ cleanup;
+ exit 2;
+}
+
+pass()
+{
+ cleanup;
+ exit 0;
+}
+
+mkdir -p $TEMPDIR
+
+cd $TEMPDIR
+
+activity="create program 1"
+cat > $TESTFILE << EOF
-
++data list notable list /foo * bar * w (f8.0).
+begin data.
+1.00 1.00 1
+1.00 2.00 1
+2.00 1.00 1
+1.00 4.00 1
+2.00 5.00 1
+1.00 19.00 1
+2.00 7.00 1
+4.00 5.00 1
+1.00 12.00 1
+2.00 13.00 1
+2.00 2.00 1
+12.00 .00 2
+12.00 1.00 1
+13.00 1.00 1
+end data
+
+variable labels foo "first" bar "second".
+
+weight by w.
+
+npar test
+ /wilcoxon=foo with bar (paired)
+ /missing analysis
+ /method=exact.
+
+EOF
+if [ $? -ne 0 ] ; then no_result ; fi
+
+
+activity="run program 1"
+$SUPERVISOR $PSPP --testing-mode -o raw-ascii $TESTFILE
+if [ $? -ne 0 ] ; then no_result ; fi
+
- #Z # -.175#
- #Asymp. Sig (2-tailed)# .861#
- #Exact Sig (2-tailed) # .893#
- #Exact Sig (1-tailed) # .446#
+activity="generate results"
+cat > $TEMPDIR/results.txt <<EOF
+1.1 NPAR TESTS. Ranks
+#============================#==#=========#============#
+# # N|Mean Rank|Sum of Ranks#
+#============================#==#=========#============#
+#second - firstNegative Ranks# 5| 8.60| 43.00#
+# Positive Ranks# 8| 6.00| 48.00#
+# Ties # 2| | #
+# Total #15| | #
+#============================#==#=========#============#
+
+1.2 NPAR TESTS. Test Statistics
+#=====================#==============#
+# #second - first#
+#=====================#==============#
-
++#Z # -.18#
++#Asymp. Sig (2-tailed)# .86#
++#Exact Sig (2-tailed) # .89#
++#Exact Sig (1-tailed) # .45#
+#=====================#==============#
+
+EOF
+if [ $? -ne 0 ] ; then no_result ; fi
+
+
+activity="compare output 1"
+diff pspp.list $TEMPDIR/results.txt
+if [ $? -ne 0 ] ; then fail ; fi
+
+
++
+# No weights this time. But some missing values
+activity="create program 2"
+cat > $TESTFILE << EOF
+data list notable list /foo * bar * dummy *.
+begin data.
+1.00 1.00 1
+1.00 2.00 1
+2.00 1.00 1
+1.00 4.00 .
+2.00 5.00 .
+1.00 19.00 .
+2.00 7.00 1
+4.00 5.00 1
+1.00 12.00 1
+2.00 13.00 1
+2.00 2.00 1
+12.00 .00 1
+12.00 .00 1
+34.2 . 1
+12.00 1.00 1
+13.00 1.00 1
+end data
+
+variable labels foo "first" bar "second".
+
+npar test
+ /wilcoxon=foo with bar (paired)
+ /missing analysis
+ /method=exact.
+
+EOF
+if [ $? -ne 0 ] ; then no_result ; fi
+
+
+activity="run program 2"
+$SUPERVISOR $PSPP --testing-mode -o raw-ascii $TESTFILE
+if [ $? -ne 0 ] ; then no_result ; fi
+
+activity="compare output 2"
+diff pspp.list $TEMPDIR/results.txt
+if [ $? -ne 0 ] ; then fail ; fi
+
+
+
+pass;
--- /dev/null
- #==============#===#=====#
- # # N | % #
- #==============#===#=====#
- #Cases Valid #131| 92.9#
- # Excluded# 10| 7.1#
- # Total #141|100.0#
- #==============#===#=====#
+#!/bin/sh
+
+# This program tests the reliability command.
+
+TEMPDIR=/tmp/pspp-tst-$$
+TESTFILE=$TEMPDIR/`basename $0`.sps
+
+# ensure that top_srcdir and top_builddir are absolute
+if [ -z "$top_srcdir" ] ; then top_srcdir=. ; fi
+if [ -z "$top_builddir" ] ; then top_builddir=. ; fi
+top_srcdir=`cd $top_srcdir; pwd`
+top_builddir=`cd $top_builddir; pwd`
+
+PSPP=$top_builddir/src/ui/terminal/pspp
+
+STAT_CONFIG_PATH=$top_srcdir/config
+export STAT_CONFIG_PATH
+
+LANG=C
+export LANG
+
+
+cleanup()
+{
+ if [ x"$PSPP_TEST_NO_CLEANUP" != x ] ; then
+ echo "NOT cleaning $TEMPDIR"
+ return ;
+ fi
+ rm -rf $TEMPDIR
+}
+
+
+fail()
+{
+ echo $activity
+ echo FAILED
+ cleanup;
+ exit 1;
+}
+
+
+no_result()
+{
+ echo $activity
+ echo NO RESULT;
+ cleanup;
+ exit 2;
+}
+
+pass()
+{
+ cleanup;
+ exit 0;
+}
+
+mkdir -p $TEMPDIR
+
+cd $TEMPDIR
+
+
+activity="create program"
+cat > $TESTFILE <<EOF
+
+data list notable list /var1 *
+ var2 *
+ var6 *
+ var7 *
+ var8 *
+ var9 *
+ var11 *
+ var12 *
+ var15 *
+ var16 *
+ var17 *
+ var19 *
+ .
+
+begin data.
+6 7 7 5 7 7 7 7 7 7 6 6
+6 7 7 6 7 6 7 5 6 5 7 7
+6 6 7 6 5 3 6 4 5 6 4 5
+4 6 5 6 6 5 4 3 5 6 5 6
+5 6 5 5 6 5 4 4 6 6 5 5
+6 6 7 6 6 5 6 5 6 6 5 6
+5 6 6 5 6 5 5 4 6 5 5 5
+5 7 7 7 7 7 6 5 7 7 7 7
+6 6 6 5 5 7 6 5 6 6 5 6
+. . . . . . . . . . . .
+6 6 5 5 5 6 6 4 6 5 5 5
+7 7 7 6 7 6 7 6 6 6 7 6
+4 7 6 6 6 5 5 4 4 5 5 6
+5 6 3 5 4 1 4 6 2 3 3 2
+3 6 6 5 6 2 4 2 2 4 4 5
+6 6 7 5 6 5 7 6 5 6 6 5
+6 5 6 6 5 6 6 6 6 4 5 5
+5 7 7 . 6 6 6 5 6 6 6 6
+5 7 5 5 4 6 7 6 5 4 6 5
+7 7 7 6 7 7 7 6 7 7 7 6
+3 6 5 6 5 7 7 3 4 7 5 7
+6 7 7 6 5 6 5 5 6 6 6 6
+5 5 6 5 5 5 5 4 5 5 5 6
+6 6 7 4 5 6 6 6 6 5 5 6
+6 5 6 6 4 4 5 4 5 6 4 5
+5 6 7 6 6 7 7 5 6 6 6 5
+5 6 5 7 4 6 6 5 7 7 5 6
+. . . . . . . . . . . .
+7 6 6 5 6 6 7 6 6 5 5 6
+6 6 7 7 7 7 7 6 7 6 6 7
+7 5 5 . 5 . 7 3 5 4 5 3
+7 6 7 5 4 5 7 5 7 5 5 6
+6 5 6 6 6 5 5 5 5 6 5 6
+7 7 7 7 7 7 7 7 5 6 7 7
+. . . . . . . . . . . .
+5 5 6 7 5 6 6 4 6 6 6 5
+6 6 5 7 5 6 7 5 6 5 4 6
+7 6 7 6 7 5 6 7 7 6 6 6
+5 6 5 6 5 6 7 2 5 7 3 7
+6 6 5 6 5 6 6 6 6 6 5 6
+7 6 7 6 6 6 6 6 6 7 6 7
+7 7 6 5 6 6 7 7 7 4 6 5
+3 7 7 6 6 7 7 7 6 6 6 4
+3 5 3 4 3 3 3 3 3 3 3 5
+5 7 7 7 5 7 6 2 6 7 6 7
+7 7 7 7 7 7 7 6 7 7 7 6
+6 5 7 4 4 4 5 6 5 5 4 5
+4 7 7 4 4 3 6 3 5 3 4 5
+7 7 7 7 7 7 7 7 7 7 7 5
+3 6 5 5 4 5 4 4 5 5 3 5
+6 7 6 6 6 7 7 6 6 6 7 6
+2 5 4 6 3 2 2 2 2 7 2 2
+4 6 6 5 5 5 6 5 5 6 6 5
+5 7 4 5 6 6 6 5 6 6 5 6
+5 7 7 5 6 5 6 5 5 4 5 4
+4 5 6 5 6 4 5 5 5 4 5 5
+7 6 6 5 5 6 7 5 6 5 7 6
+5 6 6 5 4 5 5 3 4 5 5 5
+5 7 6 4 4 5 6 5 6 4 4 6
+6 6 6 6 5 7 7 6 5 5 6 6
+6 6 7 6 7 6 6 5 6 7 6 5
+7 6 7 6 7 6 7 7 5 5 6 6
+5 6 6 5 5 5 6 5 6 7 7 5
+5 6 6 5 6 5 6 6 6 6 6 6
+5 5 5 5 6 4 5 3 4 7 6 5
+5 7 7 6 6 6 6 5 6 7 6 7
+6 6 7 7 7 5 6 5 5 5 5 4
+2 7 5 4 6 5 5 2 5 6 4 6
+6 7 7 5 6 6 7 6 6 7 5 7
+5 6 7 6 6 3 5 7 6 6 5 6
+6 6 6 3 5 5 5 6 6 6 4 5
+4 7 7 4 7 4 5 5 5 7 4 4
+. . . . . . . . . . . .
+6 6 7 6 7 6 7 7 6 7 7 6
+. . . . . . . . . . . .
+5 6 5 7 6 5 6 6 5 6 4 6
+5 5 5 5 4 5 5 5 7 5 5 5
+6 6 6 4 5 4 6 6 6 4 5 4
+6 5 7 4 6 4 6 5 6 6 6 3
+5 7 6 5 5 5 5 5 6 7 6 6
+5 5 7 7 5 5 6 6 5 5 5 7
+5 6 7 6 7 5 6 4 6 7 6 7
+4 5 5 5 6 5 6 5 6 6 5 6
+6 5 5 5 6 3 4 5 5 4 5 3
+6 6 6 5 5 5 4 3 4 5 5 5
+6 7 7 6 2 3 6 6 6 5 7 7
+6 7 5 5 6 6 6 5 6 6 6 6
+6 7 7 6 7 7 7 5 5 6 6 6
+6 6 6 6 7 6 6 7 6 6 6 6
+5 6 6 6 3 5 6 6 5 5 4 6
+4 6 5 6 6 5 6 5 6 6 5 5
+6 4 6 5 4 6 7 4 5 6 5 5
+6 7 6 4 6 5 7 6 7 7 6 5
+6 7 7 6 7 6 7 7 7 6 6 6
+6 6 6 4 5 6 7 7 5 6 4 4
+3 3 5 3 3 1 5 6 3 2 3 3
+7 7 5 6 6 7 7 6 7 7 7 7
+5 6 6 6 7 5 4 5 4 7 6 7
+3 6 5 4 3 3 3 5 5 6 3 4
+5 7 6 4 6 5 5 6 6 7 5 6
+5 7 6 6 6 6 6 5 6 7 7 6
+7 7 5 6 7 7 7 7 6 5 7 7
+6 7 6 6 5 6 7 7 6 5 6 6
+6 7 7 7 7 6 6 7 6 7 7 7
+4 6 4 7 3 6 5 5 4 3 5 6
+5 5 7 5 4 6 7 5 4 6 6 5
+5 5 6 4 6 5 7 6 5 5 5 6
+. . . . . . . . . . . .
+. . . . . . . . . . . .
+5 7 7 5 6 6 7 7 6 6 6 7
+6 7 7 1 2 1 7 7 5 5 5 2
+. . . . . . . . . . . .
+3 7 4 6 4 7 4 6 4 7 4 7
+5 7 3 5 5 6 7 5 4 7 7 4
+4 7 7 5 4 6 7 7 6 5 4 4
+6 6 2 2 6 4 6 5 5 1 5 2
+5 5 6 4 5 4 6 5 5 6 5 5
+. . . . . . . . . . . .
+5 7 6 6 6 6 6 6 5 6 6 6
+6 6 6 5 6 6 6 6 7 5 6 7
+3 6 3 3 5 3 3 5 3 5 7 4
+4 4 6 3 3 3 4 3 4 2 3 6
+5 7 7 6 5 4 7 5 7 7 3 7
+4 5 4 4 4 4 3 3 3 4 3 3
+6 7 7 5 6 6 7 5 4 5 5 5
+3 5 3 3 1 3 4 3 4 7 6 7
+4 5 4 4 4 3 4 5 6 6 4 5
+5 6 3 4 5 3 5 3 4 5 6 4
+5 5 5 6 6 6 6 4 5 6 6 5
+6 7 7 2 2 6 7 7 7 7 5 7
+5 7 7 4 6 5 7 5 5 5 6 6
+6 6 7 7 5 5 5 7 6 7 7 7
+6 5 7 3 6 5 6 5 5 6 5 4
+5 7 6 5 6 6 6 5 6 5 5 6
+4 5 5 5 6 3 5 3 3 6 5 5
+. . . . . . . . . . . .
+5 6 6 4 4 4 5 3 5 5 2 6
+5 6 7 5 5 6 6 5 5 6 6 6
+6 7 7 6 4 7 7 6 7 5 6 7
+6 6 5 4 5 2 7 6 6 5 6 6
+2 2 2 2 2 2 3 2 3 1 1 2
+end data.
+
+RELIABILITY
+ /VARIABLES=var2 var8 var15 var17 var6
+ /SCALE('Everything') var6 var8 var15 var17
+ /MODEL=ALPHA.
+
+RELIABILITY
+ /VARIABLES=var6 var8 var15 var17
+ /SCALE('Nothing') ALL
+ /MODEL=SPLIT(2)
+ .
+
+RELIABILITY
+ /VARIABLES=var2 var6 var8 var15 var17 var19
+ /SCALE('Totals') var6 var8 var15 var17
+ /SUMMARY = total
+ .
+
+
+RELIABILITY
+ /VARIABLES=var6 var8 var15 var17
+ .
+
+EOF
+if [ $? -ne 0 ] ; then no_result ; fi
+
+activity="run program"
+$SUPERVISOR $PSPP --testing-mode -o raw-ascii $TESTFILE
+if [ $? -ne 0 ] ; then no_result ; fi
+
+activity="compare output"
+diff pspp.list - << EOF
+Scale: Everything
+
+1.1 RELIABILITY. Case Processing Summary
- # .748# 4#
++#==============#===#======#
++# # N | % #
++#==============#===#======#
++#Cases Valid #131| 92.91#
++# Excluded# 10| 7.09#
++# Total #141|100.00#
++#==============#===#======#
+
+1.2 RELIABILITY. Reliability Statistics
+#================#==========#
+#Cronbach's Alpha#N of items#
+#================#==========#
- #==============#===#=====#
- # # N | % #
- #==============#===#=====#
- #Cases Valid #131| 92.9#
- # Excluded# 10| 7.1#
- # Total #141|100.0#
- #==============#===#=====#
++# .75# 4#
+#================#==========#
+
+Scale: Nothing
+
+2.1 RELIABILITY. Case Processing Summary
- #==========================================================#====#
- #Cronbach's Alpha Part 1 Value #.550#
- # N of Items# 2#
- # Part 2 Value #.631#
- # N of Items# 2#
- # Total N of Items # 4#
- #Correlation Between Forms #.606#
- #Spearman-Brown Coefficient Equal Length #.755#
- # Unequal Length #.755#
- #Guttman Split-Half Coefficient #.754#
- #==========================================================#====#
++#==============#===#======#
++# # N | % #
++#==============#===#======#
++#Cases Valid #131| 92.91#
++# Excluded# 10| 7.09#
++# Total #141|100.00#
++#==============#===#======#
+
+2.2 RELIABILITY. Reliability Statistics
- #==============#===#=====#
- # # N | % #
- #==============#===#=====#
- #Cases Valid #131| 92.9#
- # Excluded# 10| 7.1#
- # Total #141|100.0#
- #==============#===#=====#
++#==========================================================#===#
++#Cronbach's Alpha Part 1 Value #.55#
++# N of Items# 2#
++# Part 2 Value #.63#
++# N of Items# 2#
++# Total N of Items # 4#
++#Correlation Between Forms #.61#
++#Spearman-Brown Coefficient Equal Length #.75#
++# Unequal Length #.75#
++#Guttman Split-Half Coefficient #.75#
++#==========================================================#===#
+
+Scale: Totals
+
+3.1 RELIABILITY. Case Processing Summary
- # .748# 4#
++#==============#===#======#
++# # N | % #
++#==============#===#======#
++#Cases Valid #131| 92.91#
++# Excluded# 10| 7.09#
++# Total #141|100.00#
++#==============#===#======#
+
+3.2 RELIABILITY. Reliability Statistics
+#================#==========#
+#Cronbach's Alpha#N of items#
+#================#==========#
- #var6 # 15.969| 8.430| .513| .705#
- #var8 # 16.565| 7.863| .530| .698#
- #var15# 16.473| 8.451| .558| .682#
- #var17# 16.603| 7.995| .570| .673#
++# .75# 4#
+#================#==========#
+
+3.3 RELIABILITY. Item-Total Statistics
+#=====#==========================#==============================#================================#================================#
+# #Scale Mean if Item Deleted|Scale Variance if Item Deleted|Corrected Item-Total Correlation|Cronbach's Alpha if Item Deleted#
+#=====#==========================#==============================#================================#================================#
- #==============#===#=====#
- # # N | % #
- #==============#===#=====#
- #Cases Valid #131| 92.9#
- # Excluded# 10| 7.1#
- # Total #141|100.0#
- #==============#===#=====#
++#var6 # 15.97| 8.43| .51| .71#
++#var8 # 16.56| 7.86| .53| .70#
++#var15# 16.47| 8.45| .56| .68#
++#var17# 16.60| 8.00| .57| .67#
+#=====#==========================#==============================#================================#================================#
+
+Scale: ANY
+
+4.1 RELIABILITY. Case Processing Summary
- # .748# 4#
++#==============#===#======#
++# # N | % #
++#==============#===#======#
++#Cases Valid #131| 92.91#
++# Excluded# 10| 7.09#
++# Total #141|100.00#
++#==============#===#======#
+
+4.2 RELIABILITY. Reliability Statistics
+#================#==========#
+#Cronbach's Alpha#N of items#
+#================#==========#
++# .75# 4#
+#================#==========#
+
+EOF
+if [ $? -ne 0 ] ; then fail ; fi
+
+
+pass;
projects / pspp-builds.git / commitdiff