def initialize(cls):
from statsmodels.discrete.discrete_model import NegativeBinomial
formula = "Days ~ C(Duration, Sum)*C(Weight, Sum)"
mod = NegativeBinomial.from_formula(formula, cls.data,
loglike_method='nb1')
cls.res = mod.fit(cov_type='HC0')
def initialize(cls):
from statsmodels.discrete.discrete_model import NegativeBinomial
formula = "Days ~ C(Duration, Sum)*C(Weight, Sum)"
mod = NegativeBinomial.from_formula(formula, cls.data,
loglike_method='nb1')
cls.res = mod.fit(cov_type='HC0')
def nb_cluster(formula, cluster, covs, coef):
"""Model a cluster of correlated features with the negative binomial"""
methylated = np.array([f.methylated for f in cluster])
counts = np.array([f.counts for f in cluster])
try:
res = [NegativeBinomial.from_formula(formula, covs, offset=np.log(count))\
.fit(disp=0) for methylation, count in izip(methylated, counts)]
except:
return dict(t=np.nan, coef=np.nan, covar="NA", p=np.nan, corr=np.nan)
methylations = methylated / counts # for correlation below.
r = get_ptc(res, coef)
nan = np.isnan(r['p'])
r['p'] = zscore_combine(r['p'][~nan], corr(methylations[~nan]))
r['t'], r['coef'] = np.mean(r['t']), np.mean(r['coef'])
return r
def nb_cluster(formula, cluster, covs, coef):
"""Model a cluster of correlated features with the negative binomial"""
methylated = np.array([f.methylated for f in cluster])
counts = np.array([f.counts for f in cluster])
try:
res = [NegativeBinomial.from_formula(formula, covs, offset=np.log(count))\
.fit(disp=0) for methylation, count in izip(methylated, counts)]
except:
return dict(t=np.nan, coef=np.nan, covar="NA", p=np.nan, corr=np.nan)
methylations = methylated / counts # for correlation below.
r = get_ptc(res, coef)
nan = np.isnan(r['p'])
r['p'] = zscore_combine(r['p'][~nan], corr(methylations[~nan]))
r['t'], r['coef'] = np.mean(r['t']), np.mean(r['coef'])
return r
test.setupClass()
data = test.data.drop([0,1,2])
res_ols = ols("np.log(Days+1) ~ C(Duration, Sum)*C(Weight, Sum)", data).fit(use_t=False)
res_glm = glm("np.log(Days+1) ~ C(Duration, Sum)*C(Weight, Sum)",
data).fit()
res_poi = Poisson.from_formula("Days ~ C(Weight) * C(Duration)", data).fit(cov_type='HC0')
res_poi_2 = poisson("Days ~ C(Weight) + C(Duration)", data).fit(cov_type='HC0')
print('\nOLS')
print(res_ols.wald_test_terms())
print('\nGLM')
print(res_glm.wald_test_terms(skip_single=False, combine_terms=['Duration', 'Weight']))
print('\nPoisson 1')
print(res_poi.wald_test_terms(skip_single=False, combine_terms=['Duration', 'Weight']))
print('\nPoisson 2')
print(res_poi_2.wald_test_terms(skip_single=False))
from statsmodels.discrete.discrete_model import NegativeBinomial
res_nb2 = NegativeBinomial.from_formula("Days ~ C(Weight) * C(Duration)", data).fit()
print('\nNegative Binomial nb2')
print(res_nb2.wald_test_terms(skip_single=False))
res_nb1 = NegativeBinomial.from_formula("Days ~ C(Weight) * C(Duration)", data,
loglike_method='nb1').fit(cov_type='HC0')
print('\nNegative Binomial nb2')
print(res_nb1.wald_test_terms(skip_single=False))
res_glm = glm("np.log(Days+1) ~ C(Duration, Sum)*C(Weight, Sum)", data).fit()
res_poi = Poisson.from_formula("Days ~ C(Weight) * C(Duration)",
data).fit(cov_type='HC0')
res_poi_2 = poisson("Days ~ C(Weight) + C(Duration)", data).fit(cov_type='HC0')
print('\nOLS')
print(res_ols.wald_test_terms())
print('\nGLM')
print(
res_glm.wald_test_terms(skip_single=False,
combine_terms=['Duration', 'Weight']))
print('\nPoisson 1')
print(
res_poi.wald_test_terms(skip_single=False,
combine_terms=['Duration', 'Weight']))
print('\nPoisson 2')
print(res_poi_2.wald_test_terms(skip_single=False))
from statsmodels.discrete.discrete_model import NegativeBinomial
res_nb2 = NegativeBinomial.from_formula("Days ~ C(Weight) * C(Duration)",
data).fit()
print('\nNegative Binomial nb2')
print(res_nb2.wald_test_terms(skip_single=False))
res_nb1 = NegativeBinomial.from_formula(
"Days ~ C(Weight) * C(Duration)", data,
loglike_method='nb1').fit(cov_type='HC0')
print('\nNegative Binomial nb2')
print(res_nb1.wald_test_terms(skip_single=False))
