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_nbinom_11465(mu, q, expected):
# test nbinom.logcdf at extreme tails
size = 20
n, p = size, size / (size + mu)
# In R:
# options(digits=16)
# pnbinom(mu=10, size=20, q=120, log.p=TRUE)
assert_allclose(nbinom.logcdf(q, n, p), expected)
def no_admixes(p, admixes, hard_cutoff=20, r=0):
if admixes > hard_cutoff:
return -float('inf')
if r > 1:
if hard_cutoff is None:
return nbinom.logpmf(admixes, n=r, p=1.0 - p)
else:
return nbinom.logpmf(admixes, n=r, p=1.0 - p) - nbinom.logcdf(
hard_cutoff, n=r, p=1.0 - p)
else:
if hard_cutoff is None:
return geom.logpmf(admixes + 1, 1.0 - p)
return geom.logpmf(admixes + 1, 1.0 - p) - geom.logcdf(
hard_cutoff + 1, 1.0 - p)
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)
