def likelihood(self, params):
'''
log-likelihood function
:argument params: a list of 2 parameters:
* theta = params[0]
* m = params[1]
:returns: log likelihood of given theta and I
'''
kda = self._kda
theta = params[0]
immig = float(params[1]) / (1 - params[1]) * (self.community.J - 1)
log_immig = log(immig)
theta_s = theta + self.community.S
poch1 = exp(self._factor + log(theta) * self.community.S - \
lpoch(immig, self.community.J) + \
log_immig * self.community.S + lngamma(theta))
gam_theta_s = gamma(theta_s)
lik = mpfr(0.0)
for abd in xrange(int(self.community.J - self.community.S)):
lik += poch1 * exp(kda[abd] + abd * log_immig) / gam_theta_s
gam_theta_s *= theta_s + abd
return -log(lik)
def likelihood (self, theta):
'''
get likelihood value of Ewens according to Parthy/Tetame (Jabot 2008)
:argument theta: value of theta
:returns: likelihood
'''
factor = lgamma (self.community.J + 1)
phi = table (self.community.abund)
phi += [0] * int (max (self.community.abund) - len (phi))
for spe in xrange (self.community.S):
factor -= log (max (1, self.community.abund[spe]))
for spe in xrange (max (self.community.abund)):
factor -= lgamma (phi[spe] + 1)
lnl = lpoch (theta, self.community.J) - log (theta) * self.community.S - factor
self._factor = factor
return lnl
