def __init__(self):
from scikits.statsmodels.datasets.longley import load
data = load()
data.exog = add_constant(data.exog)
self.res1 = OLS(data.endog, data.exog).fit()
R = np.identity(7)[:-1,:]
self.Ftest = self.res1.f_test(R)
def __init__(self):
from scikits.statsmodels.datasets.longley import load
data = load()
data.exog = add_constant(data.exog)
ols_res = OLS(data.endog, data.exog).fit()
gls_res = GLS(data.endog, data.exog).fit()
self.res1 = gls_res
self.res2 = ols_res
def __init__(self):
from scikits.statsmodels.datasets.longley import load
from results.results_regression import Longley
data = load()
data.exog = add_constant(data.exog)
res1 = OLS(data.endog, data.exog).fit()
res2 = Longley()
res2.wresid = res1.wresid # workaround hack
self.res1 = res1
self.res2 = res2
def __init__(self):
from scikits.statsmodels.datasets.longley import load
data = load()
self.data = data
data.exog = add_constant(data.exog)
self.res1 = OLS(data.endog, data.exog).fit()
# def setup(self):
# if skipR:
# raise SkipTest, "Rpy not installed"
# else:
R = np.identity(7)
self.Ttest = self.res1.t_test(R)
def __init__(self):
'''
Tests Poisson family with canonical log link.
Test results were obtained by R.
'''
from results.results_glm import Cpunish
from scikits.statsmodels.datasets.cpunish import load
self.data = load()
self.data.exog[:,3] = np.log(self.data.exog[:,3])
self.data.exog = add_constant(self.data.exog)
self.res1 = GLM(self.data.endog, self.data.exog,
family=sm.families.Poisson()).fit()
self.res2 = Cpunish()
def __init__(self):
from scikits.statsmodels.datasets.longley import load
from results.results_regression import LongleyGls
data = load()
exog = add_constant(np.column_stack(\
(data.exog[:,1],data.exog[:,4])))
tmp_results = OLS(data.endog, exog).fit()
rho = np.corrcoef(tmp_results.resid[1:],
tmp_results.resid[:-1])[0][1] # by assumption
order = toeplitz(np.arange(16))
sigma = rho**order
GLS_results = GLS(data.endog, exog, sigma=sigma).fit()
self.res1 = GLS_results
self.res2 = LongleyGls()
def __init__(self):
'''
Test Gaussian family with canonical identity link
'''
# Test Precisions
self.decimal_resids = DECIMAL_3
self.decimal_params = DECIMAL_2
self.decimal_bic = DECIMAL_0
self.decimal_bse = DECIMAL_3
from scikits.statsmodels.datasets.longley import load
self.data = load()
self.data.exog = add_constant(self.data.exog)
self.res1 = GLM(self.data.endog, self.data.exog,
family=sm.families.Gaussian()).fit()
from results.results_glm import Longley
self.res2 = Longley()
def __init__(self):
'''
Tests Gamma family with canonical inverse link (power -1)
'''
# Test Precisions
self.decimal_aic_R = -1 #TODO: off by about 1, we are right with Stata
self.decimal_resids = DECIMAL_2
from scikits.statsmodels.datasets.scotland import load
from results.results_glm import Scotvote
data = load()
data.exog = add_constant(data.exog)
res1 = GLM(data.endog, data.exog, \
family=sm.families.Gamma()).fit()
self.res1 = res1
# res2 = RModel(data.endog, data.exog, r.glm, family=r.Gamma)
res2 = Scotvote()
res2.aic_R += 2 # R doesn't count degree of freedom for scale with gamma
self.res2 = res2
def __init__(self):
'''
Test Binomial family with canonical logit link using star98 dataset.
'''
self.decimal_resids = DECIMAL_1
self.decimal_bic = DECIMAL_2
from scikits.statsmodels.datasets.star98 import load
from results.results_glm import Star98
data = load()
data.exog = add_constant(data.exog)
trials = data.endog[:,:2].sum(axis=1)
self.res1 = GLM(data.endog, data.exog, \
family=sm.families.Binomial()).fit(data_weights = trials)
#NOTE: if you want to replicate with RModel
#res2 = RModel(data.endog[:,0]/trials, data.exog, r.glm,
# family=r.binomial, weights=trials)
self.res2 = Star98()
def __init__(self):
'''
Test Negative Binomial family with canonical log link
'''
# Test Precision
self.decimal_resid = DECIMAL_1
self.decimal_params = DECIMAL_3
self.decimal_resids = -1 # 1 % mismatch at 0
self.decimal_fittedvalues = DECIMAL_1
from scikits.statsmodels.datasets.committee import load
self.data = load()
self.data.exog[:,2] = np.log(self.data.exog[:,2])
interaction = self.data.exog[:,2]*self.data.exog[:,1]
self.data.exog = np.column_stack((self.data.exog,interaction))
self.data.exog = add_constant(self.data.exog)
self.res1 = GLM(self.data.endog, self.data.exog,
family=sm.families.NegativeBinomial()).fit()
from results.results_glm import Committee
res2 = Committee()
res2.aic_R += 2 # They don't count a degree of freedom for the scale
self.res2 = res2
