def test_proper_initialise(self):
"""jackknife should initialise correctly"""
# Scalar
pmcc_stat = stat_maker(pmcc, data, 1)
test_knife = JackknifeStats(data.shape[1], pmcc_stat)
self.assertEqual(test_knife.n, data.shape[1])
self.assertEqual(test_knife._jackknifed_stat, None)
# Vector
mean_stat = stat_maker(mean, data, 1)
test_knife = JackknifeStats(data.shape[1], mean_stat)
self.assertEqual(test_knife.n, data.shape[1])
self.assertEqual(test_knife._jackknifed_stat, None)
def plot_sample(plot,
coll,
calc_stat,
x,
title,
xlabel,
ylabel,
color,
label,
stderr=False):
if stderr:
print '\tJackknifing the statistic and standard error'
jk = JackknifeStats(coll.N, calc_stat)
y = jk.JackknifedStat
stderr = jk.StandardError
else:
stderr = None
y = calc_stat(range(coll.N))
plot(x,
y=y,
color=color,
label=label,
stderr=stderr,
ylabel=ylabel,
xlabel=xlabel,
title=title)
def test_jackknife_stats(self):
"""jackknife results should match Sokal & Rolf example"""
# Scalar
pmcc_stat = stat_maker(pmcc, data, 1)
test_knife = JackknifeStats(data.shape[1], pmcc_stat)
self.assertAlmostEqual(test_knife.JackknifedStat, 1.2905845)
self.assertAlmostEqual(test_knife.Exception, 0.2884490)
self.assertTrue(test_knife._jackknifed_stat is not None)
# Vector
mean_stat = stat_maker(mean, data, 1)
test_knife = JackknifeStats(data.shape[1], mean_stat)
expected_jk_stat = data.mean(axis=1)
got_jk_stat = test_knife.JackknifedStat
expected_standard_err = [30.69509346, 1.87179671]
got_standard_err = test_knife.Exception
for index in [0,1]:
self.assertAlmostEqual(got_jk_stat[index], expected_jk_stat[index])
self.assertAlmostEqual(got_standard_err[index],
expected_standard_err[index])
def test_tables(self):
"""jackknife should work for calculators return scalars or vectors"""
# Scalar
pmcc_stat = stat_maker(pmcc, data, 1)
test_knife = JackknifeStats(data.shape[1], pmcc_stat)
expected_subsample_stats = [1.4151, 1.3946, 1.4314, 1.1889, 1.1323, \
1.3083, 1.3561, 1.3453, 1.2412, 1.3216, \
1.2871, 1.3664]
expected_pseudovalues = [0.1968, 0.4224, 0.0176, 2.6852, 3.3084, \
1.3718, 0.8461, 0.9650, 2.1103, 1.2253, \
1.6049, 0.7333]
test_knife.jackknife()
got_subsample_stats = test_knife._subset_statistics
got_pseudovalues = test_knife._pseudovalues
for index in range(data.shape[1]):
self.assertAlmostEqual(got_subsample_stats[index],
expected_subsample_stats[index], places=4)
self.assertAlmostEqual(got_pseudovalues[index],
expected_pseudovalues[index], places=4)
# Vector
mean_stat = stat_maker(mean, data, 1)
test_knife = JackknifeStats(data.shape[1], mean_stat)
test_knife.jackknife()
expected_pseudovalues = data.transpose()
expected_subsample_stats = [[ 198.9091, 11.8336],
[ 197.0909, 11.7609],
[ 204.2727, 12.1155],
[ 209.2727, 12.9155],
[ 178.4545, 11.0791],
[ 192.4545, 11.7882],
[ 204.2727, 13.0145],
[ 184.2727, 11.7055],
[ 206.0909, 12.7618],
[ 193.3636, 11.7436],
[ 184.2727, 11.5745],
[ 194.2727, 12.2773]]
got_subsample_stats = test_knife._subset_statistics
got_pseudovalues = test_knife._pseudovalues
for index1 in range(data.shape[1]):
for index2 in range(data.shape[0]):
self.assertAlmostEqual(got_subsample_stats[index1][index2],
expected_subsample_stats[index1][index2],
places=4)
self.assertAlmostEqual(got_pseudovalues[index1][index2],
expected_pseudovalues[index1][index2],
places=4)
def test_tables(self):
"""jackknife should work for calculators return scalars or vectors"""
# Scalar
pmcc_stat = stat_maker(pmcc, data, 1)
test_knife = JackknifeStats(data.shape[1], pmcc_stat)
expected_subsample_stats = [1.4151, 1.3946, 1.4314, 1.1889, 1.1323, \
1.3083, 1.3561, 1.3453, 1.2412, 1.3216, \
1.2871, 1.3664]
expected_pseudovalues = [0.1968, 0.4224, 0.0176, 2.6852, 3.3084, \
1.3718, 0.8461, 0.9650, 2.1103, 1.2253, \
1.6049, 0.7333]
test_knife.jackknife()
got_subsample_stats = test_knife._subset_statistics
got_pseudovalues = test_knife._pseudovalues
for index in range(data.shape[1]):
self.assertAlmostEqual(got_subsample_stats[index],
expected_subsample_stats[index], places=4)
self.assertAlmostEqual(got_pseudovalues[index],
expected_pseudovalues[index], places=4)
# Vector
mean_stat = stat_maker(mean, data, 1)
test_knife = JackknifeStats(data.shape[1], mean_stat)
test_knife.jackknife()
expected_pseudovalues = data.transpose()
expected_subsample_stats = [[ 198.9091, 11.8336],
[ 197.0909, 11.7609],
[ 204.2727, 12.1155],
[ 209.2727, 12.9155],
[ 178.4545, 11.0791],
[ 192.4545, 11.7882],
[ 204.2727, 13.0145],
[ 184.2727, 11.7055],
[ 206.0909, 12.7618],
[ 193.3636, 11.7436],
[ 184.2727, 11.5745],
[ 194.2727, 12.2773]]
got_subsample_stats = test_knife._subset_statistics
got_pseudovalues = test_knife._pseudovalues
for index1 in range(data.shape[1]):
for index2 in range(data.shape[0]):
self.assertAlmostEqual(got_subsample_stats[index1][index2],
expected_subsample_stats[index1][index2],
places=4)
self.assertAlmostEqual(got_pseudovalues[index1][index2],
expected_pseudovalues[index1][index2],
places=4)