def setup_class(cls):
data = star98.load(as_pandas=False)
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)
def __init__(self):
data = star98.load()
desc_stat_data = data.exog[:50, 5]
mv_desc_stat_data = data.exog[:50, 5:7] # mv = multivariate
self.res1 = DescStat(desc_stat_data)
self.res2 = DescStatRes()
self.mvres1 = DescStat(mv_desc_stat_data)
def setup_class(cls):
data = star98.load(as_pandas=False)
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)
def __init__(self):
data = star98.load()
desc_stat_data = data.exog[:50, 5]
mv_desc_stat_data = data.exog[:50, 5:7] # mv = multivariate
self.res1 = DescStat(desc_stat_data)
self.res2 = DescStatRes()
self.mvres1 = DescStat(mv_desc_stat_data)
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)
def setup_class(cls):
from statsmodels.datasets.star98 import load
#from statsmodels.genmod.tests.results.results_glm import Star98
data = load(as_pandas=False)
exog = add_constant(data.exog, prepend=True)
offset = np.ones(len(data.endog))
exog_keep = exog[:, :-5]
cls.mod2 = GLM(data.endog, exog_keep, family=family.Binomial(),
offset=offset)
cls.mod1 = GLM(data.endog, exog, family=family.Binomial(),
offset=offset)
cls.init()
def setup_class(cls):
from statsmodels.datasets.star98 import load
#from statsmodels.genmod.tests.results.results_glm import Star98
data = load()
exog = add_constant(data.exog, prepend=True)
offset = np.ones(len(data.endog))
exog_keep = exog[:, :-5]
cls.mod2 = GLM(data.endog, exog_keep, family=family.Binomial(),
offset=offset)
cls.mod1 = GLM(data.endog, exog, family=family.Binomial(),
offset=offset)
cls.init()
def setup_class(cls):
cls.data = star98.load(as_pandas=False).exog[:30, 1:3]
cls.res1 = ANOVA([cls.data[:, 0], cls.data[:, 1]])
cls.res2 = ANOVAResults()
def setup_class(cls):
cls.data = star98.load(as_pandas=False).exog[:30, 1:3]
cls.res1 = ANOVA([cls.data[:, 0], cls.data[:, 1]])
cls.res2 = ANOVAResults()
def setup_class(cls):
cls.data = np.asarray(star98.load().exog)[:30, 1:3]
cls.res1 = ANOVA([cls.data[:, 0], cls.data[:, 1]])
cls.res2 = ANOVAResults()
def __init__(self):
self.data = star98.load().exog[:30, 1:3]
self.res1 = ANOVA([self.data[:, 0], self.data[:, 1]])
self.res2 = ANOVAResults()
def __init__(self):
self.data = star98.load().exog[:30, 1:3]
self.res1 = ANOVA([self.data[:, 0], self.data[:, 1]])
self.res2 = ANOVAResults()
