t-yui/fit_nbinom

Negative binomial maximum likelihood estimate implementation in Python using L-BFGS-B

fit_nbinom

Negative binomial maximum likelihood estimate implementation in Python using L-BFGS-B

installation

python setup.py install

usage

1. Fit negative-binomial model and show summary

from fit_nbinom import nbinom_mle
import numpy as np

# X is a list or a numpy array representing the data
X = np.array([16, 18, 11, 19, 20, 3, 2, 11, 8, 5])

res = nbinom_mle(X=X)

res.summary()

output:

parameter      estimate    std err    95%CI lower    95%CI upper
-----------  ----------  ---------  -------------  -------------
size            3.19747   0.584826        2.05123        4.34371
mu             11.3       0.715784        9.89709       12.7029

tablefmt argument defines format of summary table. Available format is the same as python-tabulate.

example: latex format

res.summary(tablefmt="latex")

output:

\begin{tabular}{lrrrr}
\hline
 parameter   &   estimate &   std err &   95\%CI lower &   95\%CI upper \\
\hline
 size        &    3.19747 &  0.584678 &       2.05153 &       4.34342 \\
 mu          &   11.3     &  0.715784 &       9.89709 &      12.7029  \\
\hline
\end{tabular}

2. Get estimates of parameters

parameters = res.params()
print(parameters)

output:

{'size': 3.1974721271555056, 'mu': 11.300000225025348}

3. Get standard errors of parameters

stderrs = res.stderr()
print(stderrs)

output:

{'size': 0.5846775256549968, 'mu': 0.7157840010743496}

description of output parameters

In the probability mass function of negative binomial distribution below:

• size parameter refers to
• mu parameter refers to