Added an option to configure to build without the termcap library.
AC_CHECK_FUNC(getopt_long,,
AC_MSG_ERROR(`This application depends upon getopt_long'))
+AC_ARG_WITH(ncurses,
+[ --without-ncurses don't compile in ncurses command line editing])
+
+
+if test "x$with_ncurses" = x"yes"; then
AC_CHECK_LIB(ncurses, tgetent, LIBS="-lncurses $LIBS" termcap=yes,
AC_CHECK_LIB(termcap, tgetent, LIBS="-ltermcap $LIBS" termcap=yes,
termcap=no))
+fi
+
+
if test "$termcap" = yes; then
AC_CHECK_HEADERS(termcap.h)
AC_DEFINE(HAVE_LIBTERMCAP, 1,
#include "hash.h"
#include "casefile.h"
#include "factor_stats.h"
+#include "moments.h"
+
/* (headers) */
#include "chart.h"
*/
- printf("Sum is %g; ",(*fs)->m[0].sum);
- printf("N is %g; ",(*fs)->m[0].n);
printf("Mean is %g\n",(*fs)->m[0].mean);
fs++ ;
TAB_LEFT | TAT_TITLE,
_("Skewness"));
+
+ tab_float (tbl, col + 2,
+ row + 11,
+ TAB_CENTER,
+ m->skewness,
+ 8,3);
+
+ /* stderr of skewness */
+ tab_float (tbl, col + 3,
+ row + 11,
+ TAB_CENTER,
+ calc_seskew(m->n),
+ 8,3);
+
+
tab_text (tbl, col,
row + 12,
TAB_LEFT | TAT_TITLE,
_("Kurtosis"));
+
+
+ tab_float (tbl, col + 2,
+ row + 12,
+ TAB_CENTER,
+ m->kurtosis,
+ 8,3);
+
+ /* stderr of kurtosis */
+ tab_float (tbl, col + 3,
+ row + 12,
+ TAB_CENTER,
+ calc_sekurt(m->n),
+ 8,3);
+
+
}
#include "hash.h"
#include "algorithm.h"
#include "alloc.h"
+#include "moments.h"
#include <stdlib.h>
#include <math.h>
{
assert (fs) ;
- fs->n = 0;
fs->n_missing = 0;
- fs->ssq = 0;
- fs->sum = 0;
+
fs->min = DBL_MAX;
fs->max = -DBL_MAX;
+
+ fs->moments = moments1_create(MOMENT_KURTOSIS);
+
fs->ordered_data = hsh_create(20,
(hsh_compare_func *) compare_values,
(hsh_hash_func *) hash_value,
}
x = val->f;
- fs->n += weight;
- fs->ssq += x * x * weight;
- fs->sum += x * weight;
+
+ moments1_add(fs->moments, x, weight);
+
if ( x < fs->min) fs->min = x;
if ( x > fs->max) fs->max = x;
void
metrics_postcalc(struct metrics *m)
{
- double sample_var;
double cc = 0.0;
double tc ;
int k1, k2 ;
int i;
int j = 1;
- m->mean = m->sum / m->n;
- sample_var = ( m->ssq / m->n - m->mean * m->mean );
+ moments1_calculate (m->moments, &m->n, &m->mean, &m->var,
+ &m->skewness, &m->kurtosis);
+
+ moments1_destroy (m->moments);
- m->var = m->n * sample_var / ( m->n - 1) ;
- m->stddev = sqrt(m->var);
+ m->stddev = sqrt(m->var);
/* FIXME: Check this is correct ???
Shouldn't we use the sample variance ??? */
m->stderr = sqrt (m->var / m->n) ;
+
+
m->wvp = (struct weighted_value **) hsh_sort(m->ordered_data);
m->n_data = hsh_count(m->ordered_data);
int
factor_statistics_compare(const struct factor_statistics *f0,
- const struct factor_statistics *f1, void *aux)
+ const struct factor_statistics *f1, int width)
{
int cmp0;
assert(f0);
assert(f1);
- cmp0 = compare_values(&f0->id[0], &f1->id[0], aux);
+ cmp0 = compare_values(&f0->id[0], &f1->id[0], width);
if ( cmp0 != 0 )
return cmp0;
if ( ( f0->id[1].f != SYSMIS ) && (f1->id[1].f == SYSMIS) )
return -1;
- return compare_values(&f0->id[1], &f1->id[1], aux);
+ return compare_values(&f0->id[1], &f1->id[1], width);
}
unsigned int
-factor_statistics_hash(const struct factor_statistics *f, void *aux)
+factor_statistics_hash(const struct factor_statistics *f, int width)
{
unsigned int h;
- h = hash_value(&f->id[0], aux);
+ h = hash_value(&f->id[0], width);
if ( f->id[1].f != SYSMIS )
- h += hash_value(&f->id[1], aux);
+ h += hash_value(&f->id[1], width);
return h;
#include "hash.h"
#include "val.h"
+
+struct moments1;
+
struct metrics
{
double n;
double n_missing;
- double ssq;
-
- double sum;
-
double min;
double max;
double stddev;
+ struct moments1 *moments;
+
+ double skewness;
+ double kurtosis;
+
double trimmed_mean;
/* A hash of data for this factor.
struct factor_statistics {
- /* The value of the independent variable */
+ /* The values of the independent variables */
union value id[2];
/* The an array stats for this factor, one for each dependent var */
void factor_statistics_free(struct factor_statistics *f);
+/* Compare f0 and f1.
+ width is the width of the independent variable */
int
factor_statistics_compare(const struct factor_statistics *f0,
- const struct factor_statistics *f1, void *aux);
+ const struct factor_statistics *f1, int width);
unsigned int
-factor_statistics_hash(const struct factor_statistics *f, void *aux);
-
-
-
-
-
+factor_statistics_hash(const struct factor_statistics *f, int width);
#endif
# Maximum # 7.000 | #
# Range # 6.000 | #
# Interquartile Range # | #
-# Skewness # | #
-# Kurtosis # | #
+# Skewness # .059 | .472 #
+# Kurtosis # -.358 | .918 #
#==========================================================#=========#==========#
2.4 EXAMINE. Case Processing Summary
# Maximum # 4.000 | #
# Range # 3.000 | #
# Interquartile Range # | #
-# Skewness # | #
-# Kurtosis # | #
+# Skewness # .475 | .752 #
+# Kurtosis # -1.546 | 1.481 #
# -------------------------------------------------------#---------+----------#
# Bloggs Mean # 3.50 | .378 #
# 95% Confidence Interval for MeanLower Bound# 3.525 | #
# Maximum # 5.000 | #
# Range # 3.000 | #
# Interquartile Range # | #
-# Skewness # | #
-# Kurtosis # | #
+# Skewness # -.468 | .752 #
+# Kurtosis # -.831 | 1.481 #
# -------------------------------------------------------#---------+----------#
# Charlies Mean # 4.88 | .441 #
# 95% Confidence Interval for MeanLower Bound# 4.904 | #
# Maximum # 7.000 | #
# Range # 4.000 | #
# Interquartile Range # | #
-# Skewness # | #
-# Kurtosis # | #
+# Skewness # .304 | .752 #
+# Kurtosis # .146 | 1.481 #
#======================================================================#=========#==========#
EOF