def random_community (self, inds=None, theta=None, immig=None):
'''
generates random distribution according to J, theta and I
:argument inds: number of individuals in community (J)
:argument theta: corresponding to the model
:argument immig: immigration rate (I)
:returns: distribution of abundance (list)
'''
theta = float (theta) if theta else self.theta
inds = inds or self.community.J
immig = float (immig) if immig else self.I
mcnum = [0] * int (inds)
locnum = [0] * int (inds)
mcnum[0] = 1
new = nxt = -1
for ind in xrange (inds):
if random () > immig / (ind + immig):
locnum [ind] = locnum [int (random () * ind)]
else:
new += 1
if random () <= theta / (theta + new):
nxt += 1
mcnum[new] = nxt + 1
else:
mcnum[new] = mcnum[int (random () * (new))]
locnum[ind] = mcnum[new]
return table(locnum, new + 1)
def __compute_factor(self):
self._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):
self._factor -= log (max (1, self.community.abund[spe]))
for spe in xrange (int(max(self.community.abund))):
self._factor -= lgamma (phi[spe] + 1)
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
def random_community(self, inds=None, theta=None):
'''
generates random distribution according to J and theta
:argument inds: number of individuals in community (J)
:argument theta: corresponding to the model
:returns: distribution of abundance (list)
'''
theta = float (theta) if theta else self.theta
inds = inds if inds else self.community.J
out = [0] * int (inds)
out [0] = spp = 1
for ind in xrange (inds):
if random () < theta/(theta + ind):
spp += 1
out[ind] = spp
else:
out[ind] = out [int (random () * ind)]
return table (out, spp)