def getNBPValue(mean0, var0, mean1, lower=False, log=False):
"""
Use negative binomial to calculate p-value
Reference:
http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.nbinom.html#scipy.stats.nbinom
"""
from scipy.stats import nbinom
n = len(mean0)
nb_p = [mean0[i] / var0[i] for i in range(n)]
# consisitent with R
nb_n0 = [mean0[i] * mean0[i] / (var0[i] - mean0[i]) for i in range(n)]
nb_n = [(lambda t: t if t >= 1 else 1)(x) for x in nb_n0]
#
if lower == True:
if log == False:
nb_p_low = nbinom.cdf(mean1, nb_n, nb_p)
else:
nb_p_low = nbinom.logcdf(mean1, nb_n, nb_p)
return list(nb_p_low)
else:
if log == False:
nb_p_low = nbinom.sf(mean1, nb_n, nb_p)
else:
nb_p_low = nbinom.logsf(mean1, nb_n, nb_p)
return list(nb_p_low)
def test_pmf(self):
n, p = truncatednegbin.convert_params(2, 0.5, 2)
nb_logpmf = nbinom.pmf(6, n, p) / nbinom.sf(5, n, p)
tnb_pmf = truncatednegbin.pmf(6, 2, 0.5, 2, 5)
assert_allclose(nb_logpmf, tnb_pmf, rtol=1e-7)
tnb_pmf = truncatednegbin.pmf(5, 2, 0.5, 2, 5)
assert_equal(tnb_pmf, 0)
def test_logpmf(self):
n, p = truncatednegbin.convert_params(5, 0.1, 2)
nb_logpmf = nbinom.logpmf(6, n, p) - np.log(nbinom.sf(5, n, p))
tnb_logpmf = truncatednegbin.logpmf(6, 5, 0.1, 2, 5)
assert_allclose(nb_logpmf, tnb_logpmf, rtol=1e-7)
tnb_logpmf = truncatednegbin.logpmf(5, 5, 0.1, 2, 5)
assert np.isneginf(tnb_logpmf)
def beta_on_negbinom_closed_form3(t1=25,t2=25,\
theta_base=10,m=100.0,deltheta=3):
"""
This method only works for alpha=0.5.
"""
if deltheta > theta_base:
#TODO: Replace this with exception.
print("deltheta must be smaller than theta.")
return
theta_alt = theta_base - deltheta
neg_binom_ix = 0
p2 = theta_alt / (t2 + theta_alt)
p1 = theta_base / (t1 + theta_base)
mode1 = int(p1 * (m - 1) / (1 - p1))
beta = 0
del_beta = 1
while del_beta > 1e-7 or neg_binom_ix < mode1 or neg_binom_ix < 1000:
del_beta = nbinom.pmf(neg_binom_ix,m,p2)*\
nbinom.sf(neg_binom_ix-1,m,p1)
beta += del_beta
neg_binom_ix += 1
return beta
def getNBPValue(mean0,var0,mean1, lower=False,log=False):
"""
Use negative binomial to calculate p-value
Reference:
http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.nbinom.html#scipy.stats.nbinom
"""
from scipy.stats import nbinom
n=len(mean0)
nb_p=[mean0[i]/var0[i] for i in range(n)]; # consisitent with R
nb_n0=[mean0[i]*mean0[i]/(var0[i]-mean0[i]) for i in range(n)]
nb_n=[ (lambda t: t if t>=1 else 1)(x) for x in nb_n0]
#
if lower==True:
if log==False:
nb_p_low=nbinom.cdf(mean1,nb_n,nb_p)
else:
nb_p_low=nbinom.logcdf(mean1,nb_n,nb_p)
return list(nb_p_low)
else:
if log==False:
nb_p_low=nbinom.sf(mean1,nb_n,nb_p)
else:
nb_p_low=nbinom.logsf(mean1,nb_n,nb_p)
return list(nb_p_low)
def test_issue_6682():
# Reference value from R:
# options(digits=16)
# print(pnbinom(250, 50, 32/63, lower.tail=FALSE))
assert_allclose(nbinom.sf(250, 50, 32. / 63.), 1.460458510976452e-35)
def nbinom_dist_prob(array, mean, sd):
p = numpy.float64(mean) / (sd * sd)
r = mean * p / (1.0 - p)
return 1.0 - (nbinom.sf(array + 1, r, p)) / (nbinom.sf(array, r, p))
def test_logpmf_zero(self):
n, p = truncatednegbin.convert_params(5, 1, 2)
nb_logpmf = nbinom.logpmf(1, n, p) - np.log(nbinom.sf(0, n, p))
tnb_logpmf = truncatednegbin.logpmf(1, 5, 1, 2, 0)
assert_allclose(nb_logpmf, tnb_logpmf, rtol=1e-2, atol=1e-2)
def test_pmf_zero(self):
n, p = truncatednegbin.convert_params(5, 0.1, 2)
nb_pmf = nbinom.pmf(1, n, p) / nbinom.sf(0, n, p)
tnb_pmf = truncatednegbin.pmf(1, 5, 0.1, 2, 0)
assert_allclose(nb_pmf, tnb_pmf, rtol=1e-5)
def _ppf(self, q, n, p):
return nbinom.ppf(nbinom.sf(0, n, p) * q + nbinom.pmf(0, n, p), n, p)
def _cdf(self, x, n, p):
k = floor(x)
if k == 0:
return 0.0
else:
return (nbinom.cdf(x, n, p) - nbinom.pmf(0, n, p)) / nbinom.sf(0, n, p)
def _pmf(self, x, n, p):
if x == 0:
return 0.0
else:
return nbinom.pmf(x, n, p) / nbinom.sf(0, n, p)
