Fitting negative binomial in python

Question

In scipy there is no support for fitting a negative binomial distribution using data (maybe due to the fact that the negative binomial in scipy is only discrete).

For a normal distribution I would just do:

from scipy.stats import norm
param = norm.fit(samp)

Is there something similar 'ready to use' function in any other library?

Answer 1

I know this thread is quite old, but current readers may want to look at this repo which is made for this purpose: https://github.com/gokceneraslan/fit_nbinom

There's also an implementation here, though part of a larger package: https://github.com/ernstlab/ChromTime/blob/master/optimize.py

Answer 2

Not only because it is discrete, also because maximum likelihood fit to negative binomial can be quite involving, especially with an additional location parameter. That would be the reason why .fit() method is not provided for it (and other discrete distributions in Scipy), here is an example:

In [163]:

import scipy.stats as ss
import scipy.optimize as so
In [164]:
#define a likelihood function
def likelihood_f(P, x, neg=1):
    n=np.round(P[0]) #by definition, it should be an integer 
    p=P[1]
    loc=np.round(P[2])
    return neg*(np.log(ss.nbinom.pmf(x, n, p, loc))).sum()
In [165]:
#generate a random variable
X=ss.nbinom.rvs(n=100, p=0.4, loc=0, size=1000)
In [166]:
#The likelihood
likelihood_f([100,0.4,0], X)
Out[166]:
-4400.3696690513316
In [167]:
#A simple fit, the fit is not good and the parameter estimate is way off
result=so.fmin(likelihood_f, [50, 1, 1], args=(X,-1), full_output=True, disp=False)
P1=result[0]
(result[1], result[0])
Out[167]:
(4418.599495886474, array([ 59.61196161,   0.28650831,   1.15141838]))
In [168]:
#Try a different set of start paramters, the fit is still not good and the parameter estimate is still way off
result=so.fmin(likelihood_f, [50, 0.5, 0], args=(X,-1), full_output=True, disp=False)
P1=result[0]
(result[1], result[0])
Out[168]:
(4417.1495981801972,
 array([  6.24809397e+01,   2.91877405e-01,   6.63343536e-04]))
In [169]:
#In this case we need a loop to get it right
result=[]
for i in range(40, 120): #in fact (80, 120) should probably be enough
    _=so.fmin(likelihood_f, [i, 0.5, 0], args=(X,-1), full_output=True, disp=False)
    result.append((_[1], _[0]))
In [170]:
#get the MLE
P2=sorted(result, key=lambda x: x[0])[0][1]
sorted(result, key=lambda x: x[0])[0]
Out[170]:
(4399.780263084549,
 array([  9.37289361e+01,   3.84587087e-01,   3.36856705e-04]))
In [171]:
#Which one is visually better?
plt.hist(X, bins=20, normed=True)
plt.plot(range(260), ss.nbinom.pmf(range(260), np.round(P1[0]), P1[1], np.round(P1[2])), 'g-')
plt.plot(range(260), ss.nbinom.pmf(range(260), np.round(P2[0]), P2[1], np.round(P2[2])), 'r-')
Out[171]:
[<matplotlib.lines.Line2D at 0x109776c10>]

Answer 3

Statsmodels has discrete.discrete_model.NegativeBinomial.fit(), see here: https://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.NegativeBinomial.fit.html#statsmodels.discrete.discrete_model.NegativeBinomial.fit