118 lines
4.2 KiB
Python
118 lines
4.2 KiB
Python
import numpy as np
|
|
from numpy.testing import assert_almost_equal
|
|
|
|
from statsmodels.datasets import star98
|
|
from statsmodels.emplike.descriptive import DescStat
|
|
|
|
from .results.el_results import DescStatRes
|
|
|
|
|
|
class GenRes:
|
|
"""
|
|
Reads in the data and creates class instance to be tested
|
|
"""
|
|
@classmethod
|
|
def setup_class(cls):
|
|
data = star98.load()
|
|
data.exog = np.asarray(data.exog)
|
|
desc_stat_data = data.exog[:50, 5]
|
|
mv_desc_stat_data = data.exog[:50, 5:7] # mv = multivariate
|
|
cls.res1 = DescStat(desc_stat_data)
|
|
cls.res2 = DescStatRes()
|
|
cls.mvres1 = DescStat(mv_desc_stat_data)
|
|
|
|
|
|
class TestDescriptiveStatistics(GenRes):
|
|
@classmethod
|
|
def setup_class(cls):
|
|
super().setup_class()
|
|
|
|
def test_test_mean(self):
|
|
assert_almost_equal(self.res1.test_mean(14),
|
|
self.res2.test_mean_14, 4)
|
|
|
|
def test_test_mean_weights(self):
|
|
assert_almost_equal(self.res1.test_mean(14, return_weights=1)[2],
|
|
self.res2.test_mean_weights, 4)
|
|
|
|
def test_ci_mean(self):
|
|
assert_almost_equal(self.res1.ci_mean(), self.res2.ci_mean, 4)
|
|
|
|
def test_test_var(self):
|
|
assert_almost_equal(self.res1.test_var(3),
|
|
self.res2.test_var_3, 4)
|
|
|
|
def test_test_var_weights(self):
|
|
assert_almost_equal(self.res1.test_var(3, return_weights=1)[2],
|
|
self.res2.test_var_weights, 4)
|
|
|
|
def test_ci_var(self):
|
|
assert_almost_equal(self.res1.ci_var(), self.res2.ci_var, 4)
|
|
|
|
def test_mv_test_mean(self):
|
|
assert_almost_equal(self.mvres1.mv_test_mean(np.array([14, 56])),
|
|
self.res2.mv_test_mean, 4)
|
|
|
|
def test_mv_test_mean_weights(self):
|
|
assert_almost_equal(self.mvres1.mv_test_mean(np.array([14, 56]),
|
|
return_weights=1)[2],
|
|
self.res2.mv_test_mean_wts, 4)
|
|
|
|
def test_test_skew(self):
|
|
assert_almost_equal(self.res1.test_skew(0),
|
|
self.res2.test_skew, 4)
|
|
|
|
def test_ci_skew(self):
|
|
# This will be tested in a round about way since MATLAB fails when
|
|
# computing CI with multiple nuisance parameters. The process is:
|
|
#
|
|
# (1) Get CI for skewness from ci.skew()
|
|
# (2) In MATLAB test the hypotheis that skew=results of test_skew.
|
|
# (3) If p-value approx .05, test confirmed
|
|
skew_ci = self.res1.ci_skew()
|
|
lower_lim = skew_ci[0]
|
|
upper_lim = skew_ci[1]
|
|
ul_pval = self.res1.test_skew(lower_lim)[1]
|
|
ll_pval = self.res1.test_skew(upper_lim)[1]
|
|
assert_almost_equal(ul_pval, .050000, 4)
|
|
assert_almost_equal(ll_pval, .050000, 4)
|
|
|
|
def test_ci_skew_weights(self):
|
|
assert_almost_equal(self.res1.test_skew(0, return_weights=1)[2],
|
|
self.res2.test_skew_wts, 4)
|
|
|
|
def test_test_kurt(self):
|
|
assert_almost_equal(self.res1.test_kurt(0),
|
|
self.res2.test_kurt_0, 4)
|
|
|
|
def test_ci_kurt(self):
|
|
# Same strategy for skewness CI
|
|
kurt_ci = self.res1.ci_kurt(upper_bound=.5, lower_bound=-1.5)
|
|
lower_lim = kurt_ci[0]
|
|
upper_lim = kurt_ci[1]
|
|
ul_pval = self.res1.test_kurt(upper_lim)[1]
|
|
ll_pval = self.res1.test_kurt(lower_lim)[1]
|
|
assert_almost_equal(ul_pval, .050000, 4)
|
|
assert_almost_equal(ll_pval, .050000, 4)
|
|
|
|
def test_joint_skew_kurt(self):
|
|
assert_almost_equal(self.res1.test_joint_skew_kurt(0, 0),
|
|
self.res2.test_joint_skew_kurt, 4)
|
|
|
|
def test_test_corr(self):
|
|
assert_almost_equal(self.mvres1.test_corr(.5),
|
|
self.res2.test_corr, 4)
|
|
|
|
def test_ci_corr(self):
|
|
corr_ci = self.mvres1.ci_corr()
|
|
lower_lim = corr_ci[0]
|
|
upper_lim = corr_ci[1]
|
|
ul_pval = self.mvres1.test_corr(upper_lim)[1]
|
|
ll_pval = self.mvres1.test_corr(lower_lim)[1]
|
|
assert_almost_equal(ul_pval, .050000, 4)
|
|
assert_almost_equal(ll_pval, .050000, 4)
|
|
|
|
def test_test_corr_weights(self):
|
|
assert_almost_equal(self.mvres1.test_corr(.5, return_weights=1)[2],
|
|
self.res2.test_corr_weights, 4)
|