incl:include/!exclude;
+compare=cmp:variables/!groups;
+percentiles=custom;
+ +id=var;
+plot[plt_]=stemleaf,boxplot,npplot,:spreadlevel(*d:n),histogram,all,none;
+cinterval=double;
+statistics[st_]=descriptives,:extreme(*d:n),all,none.
+
void np_plot(const struct metrics *m, const char *factorname);
+void box_plot_group(const struct factor *fctr,
+ const struct variable **vars, int n_vars,
+ const struct variable *id
+ ) ;
+
+
+void box_plot_variables(const struct factor *fctr,
+ const struct variable **vars, int n_vars,
+ const struct variable *id
+ );
+
+
/* Per Split function */
static void run_examine(const struct casefile *cf, void *cmd_);
double weight, int case_missing);
+/* Represent a factor as a string, so it can be
+ printed in a human readable fashion */
+const char * factor_to_string(const struct factor *fctr,
+ struct factor_statistics *fs,
+ const struct variable *var);
+
+
+/* Represent a factor as a string, so it can be
+ printed in a human readable fashion,
+ but sacrificing some readablility for the sake of brevity */
+const char *factor_to_string_concise(const struct factor *fctr,
+ struct factor_statistics *fs);
+
+
+
+
/* Function to use for testing for missing values */
static is_missing_func value_is_missing;
if ( ! cmd.sbc_cinterval)
cmd.n_cinterval[0] = 95.0;
-
/* If descriptives have been requested, make sure the
quartiles are calculated */
if ( cmd.a_statistics[XMN_ST_DESCRIPTIVES] )
multipass_procedure_with_splits (run_examine, &cmd);
if ( totals )
- free(totals);
+ free( totals );
+
+ if ( dependent_vars )
+ free (dependent_vars);
subc_list_double_destroy(&percentile_list);
np_plot(&totals[v], var_to_string(dependent_vars[v]));
}
+ if ( cmd.a_plot[XMN_PLT_BOXPLOT] )
+ {
+ if ( cmd.cmp == XMN_GROUPS )
+ {
+ box_plot_group(0, dependent_vars, n_dependent_vars,
+ cmd.v_id);
+ }
+ else
+ box_plot_variables(0, dependent_vars, n_dependent_vars,
+ cmd.v_id);
+ }
+
if ( cmd.a_plot[XMN_PLT_HISTOGRAM] )
{
for ( v = 0 ; v < n_dependent_vars; ++v )
struct factor_statistics **fs = fctr->fs ;
+ if ( cmd.a_plot[XMN_PLT_BOXPLOT] )
+ {
+ if ( cmd.cmp == XMN_VARIABLES )
+ box_plot_variables(fctr, dependent_vars, n_dependent_vars,
+ cmd.v_id);
+ else
+ box_plot_group(fctr, dependent_vars, n_dependent_vars,
+ cmd.v_id);
+ }
+
for ( v = 0 ; v < n_dependent_vars; ++v )
{
for ( fs = fctr->fs ; *fs ; ++fs )
{
- char buf1[100];
- char buf2[100];
- sprintf(buf1, "%s (",
- var_to_string(dependent_vars[v]));
-
- snprintf(buf2, 100, "%s = %s",
- var_to_string(fctr->indep_var[0]),
- value_to_string(&(*fs)->id[0],fctr->indep_var[0]));
-
- strcat(buf1, buf2);
-
- if ( fctr->indep_var[1] )
- {
- sprintf(buf2, "; %s = %s)",
- var_to_string(fctr->indep_var[1]),
- value_to_string(&(*fs)->id[1],
- fctr->indep_var[1]));
- strcat(buf1, buf2);
- }
- else
- {
- strcat(buf1, ")");
- }
+ const char *s = factor_to_string(fctr, *fs, dependent_vars[v]);
if ( cmd.a_plot[XMN_PLT_NPPLOT] )
- np_plot(&(*fs)->m[v],buf1);
+ np_plot(&(*fs)->m[v], s);
-
if ( cmd.a_plot[XMN_PLT_HISTOGRAM] )
{
struct normal_curve normal;
normal.stddev = (*fs)->m[v].stddev;
histogram_plot((*fs)->m[v].histogram,
- buf1, &normal, 0);
+ s, &normal, 0);
}
} /* for ( fs .... */
assert(n_dependent_vars);
totals = xmalloc( sizeof(struct metrics) * n_dependent_vars);
+ memset ( totals, 0, sizeof(struct metrics) * n_dependent_vars);
if ( lex_match(T_BY))
{
if ( value_is_missing(val,var) || case_missing )
val = 0;
-
- metrics_calc( &(*foo)->m[v], val, weight, case_no );
+
+ metrics_calc( &(*foo)->m[v], val, weight, case_no);
+
}
fctr = fctr->next;
if ( value_is_missing(val,var) || case_missing )
val = 0;
- metrics_calc(&totals[v], val, weight, case_no );
+ metrics_calc(&totals[v], val, weight, case_no);
}
output_examine();
- for ( v = 0 ; v < n_dependent_vars ; ++v )
- hsh_destroy(totals[v].ordered_data);
+
+ if ( totals )
+ {
+ int i;
+ for ( i = 0 ; i < n_dependent_vars ; ++i )
+ metrics_destroy(&totals[i]);
+ }
}
tab_title (tbl, 0, _("Extreme Values"));
-
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_text (tbl, n_cols - 1, 0, TAB_CENTER | TAT_TITLE, _("Value"));
tab_text (tbl, n_cols - 2, 0, TAB_CENTER | TAT_TITLE, _("Case Number"));
-
-
-
for ( i = 0 ; i < n_dep_var ; ++i )
{
-
-
-
-
-
-
-
/* Fill in the descriptives data */
void
populate_descriptives(struct tab_table *tbl, int col, int row,
}
+
+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 )
+ {
+ double y_min = DBL_MAX;
+ double y_max = -DBL_MAX;
+ struct chart *ch;
+
+ ch = chart_create();
+
+ const char *s = factor_to_string(fctr, *fs, 0 );
+
+ chart_write_title(ch, s);
+
+ 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);
+
+ }
+}
+
+
+
+/* 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();
+
+ boxplot_draw_yscale(ch, totals[i].max, totals[i].min);
+
+ if ( fctr )
+ {
+ int n_factors = 0;
+ int f=0;
+ for ( fs = fctr->fs ; *fs ; ++fs )
+ ++n_factors;
+
+ 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 )
+ {
+
+ const char *s = factor_to_string_concise(fctr, *fs);
+
+ 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;
+
+ boxplot_draw_boxplot(ch,
+ box_centre, box_width,
+ &(*fs)->m[i],
+ s);
+ }
+ }
+ else
+ {
+ 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]) );
+
+ }
+
+ chart_submit(ch);
+ }
+}
+
+
/* Plot the normal and detrended normal plots for m
Label the plots with factorname */
void
double yfirst=0, ylast=0;
/* Normal Plot */
- struct chart np_chart;
+ struct chart *np_chart;
/* Detrended Normal Plot */
- struct chart dnp_chart;
+ struct chart *dnp_chart;
/* The slope and intercept of the ideal normal probability line */
const double slope = 1.0 / m->stddev;
if ( m->n_data == 0 )
return ;
- chart_initialise(&np_chart);
- 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"));
+ 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_initialise(&dnp_chart);
- chart_write_title(&dnp_chart, _("Detrended Normal Q-Q Plot of %s"),
+
+ 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"));
+ 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));
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(np_chart, x_lower - slack, x_upper + slack, 5);
- chart_write_xscale(&dnp_chart, m->min, m->max, 5);
+ chart_write_xscale(dnp_chart, m->min, m->max, 5);
}
- chart_write_yscale(&np_chart, yfirst, ylast, 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;
- d_data = xmalloc (m->n_data * sizeof(double));
- 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));
+ /* We have to cache the detrended data, beacause we need to
+ find its limits before we can plot it */
+ double *d_data;
+ d_data = xmalloc (m->n_data * sizeof(double));
+ 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));
- chart_datum(&np_chart, 0, m->wvp[i]->v.f, ns);
+ chart_datum(np_chart, 0, m->wvp[i]->v.f, ns);
- d_data[i] = (m->wvp[i]->v.f - m->mean) / m->stddev - ns;
+ d_data[i] = (m->wvp[i]->v.f - m->mean) / m->stddev - ns;
- 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);
+ 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]);
+ for ( i = 0 ; i < m->n_data; ++i )
+ chart_datum(dnp_chart, 0, m->wvp[i]->v.f, d_data[i]);
- free(d_data);
+ 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);
-
- chart_finalise(&np_chart);
- chart_finalise(&dnp_chart);
+ chart_line(np_chart, slope, intercept, yfirst, ylast , CHART_DIM_Y);
+ chart_line(dnp_chart, 0, 0, m->min, m->max , CHART_DIM_X);
+ chart_submit(np_chart);
+ chart_submit(dnp_chart);
}
if ( (*p)->p == 25 )
tab_float(tbl, col + i + 1 , row + 1,
TAB_CENTER,
- m->hinges[0], 8, 2);
+ m->hinge[0], 8, 2);
if ( (*p)->p == 50 )
tab_float(tbl, col + i + 1 , row + 1,
TAB_CENTER,
- m->hinges[1], 8, 2);
+ m->hinge[1], 8, 2);
if ( (*p)->p == 75 )
tab_float(tbl, col + i + 1 , row + 1,
TAB_CENTER,
- m->hinges[2], 8, 2);
+ m->hinge[2], 8, 2);
i++;
p++;
}
+}
+const char *
+factor_to_string(const struct factor *fctr,
+ struct factor_statistics *fs,
+ const struct variable *var)
+{
+
+ static char buf1[100];
+ char buf2[100];
+
+ strcpy(buf1,"");
+
+ if (var)
+ sprintf(buf1, "%s (",var_to_string(var) );
+
+
+ snprintf(buf2, 100, "%s = %s",
+ var_to_string(fctr->indep_var[0]),
+ value_to_string(&fs->id[0],fctr->indep_var[0]));
+
+ strcat(buf1, buf2);
+
+ if ( fctr->indep_var[1] )
+ {
+ sprintf(buf2, "; %s = %s)",
+ var_to_string(fctr->indep_var[1]),
+ value_to_string(&fs->id[1],
+ fctr->indep_var[1]));
+ strcat(buf1, buf2);
+ }
+ else
+ {
+ if ( var )
+ strcat(buf1, ")");
+ }
+
+ return buf1;
}
+
+
+const char *
+factor_to_string_concise(const struct factor *fctr,
+ struct factor_statistics *fs)
+
+{
+
+ static char buf[100];
+
+ char buf2[100];
+
+ snprintf(buf, 100, "%s",
+ value_to_string(&fs->id[0], fctr->indep_var[0]));
+
+ if ( fctr->indep_var[1] )
+ {
+ sprintf(buf2, ",%s)", value_to_string(&fs->id[1], fctr->indep_var[1]) );
+ strcat(buf, buf2);
+ }
+
+
+ return buf;
+}
projects / pspp / blobdiff