This notebook demos negative binomial regression using the glm submodule. It closely follows the GLM Poisson regression example by Jonathan Sedar (which is in turn insipired by a project by Ian Osvald) except the data here is negative binomially distributed instead of Poisson distributed.
Negative binomial regression is used to model count data for which the variance is higher than the mean. The negative binomial distribution can be thought of as a Poisson distribution whose rate parameter is gamma distributed, so that rate parameter can be adjusted to account for the increased variance.
In [1]:
import numpy as np
import pandas as pd
import pymc3 as pm
from scipy import stats
from scipy import optimize
import matplotlib.pyplot as plt
import seaborn as sns
import re
%matplotlib inline
In [2]:
def plot_traces(trcs, varnames=None):
'''Plot traces with overlaid means and values'''
nrows = len(trcs.varnames)
if varnames is not None:
nrows = len(varnames)
ax = pm.traceplot(trcs, varnames=varnames, figsize=(12,nrows*1.4),
lines={k: v['mean'] for k, v in
pm.df_summary(trcs,varnames=varnames).iterrows()})
for i, mn in enumerate(pm.df_summary(trcs, varnames=varnames)['mean']):
ax[i,0].annotate('{:.2f}'.format(mn), xy=(mn,0), xycoords='data',
xytext=(5,10), textcoords='offset points', rotation=90,
va='bottom', fontsize='large', color='#AA0022')
def strip_derived_rvs(rvs):
'''Remove PyMC3-generated RVs from a list'''
ret_rvs = []
for rv in rvs:
if not (re.search('_log',rv.name) or re.search('_interval',rv.name)):
ret_rvs.append(rv)
return ret_rvs
In [3]:
np.random.seed(123)
In [4]:
# Mean Poisson values
theta_noalcohol_meds = 1 # no alcohol, took an antihist
theta_alcohol_meds = 3 # alcohol, took an antihist
theta_noalcohol_nomeds = 6 # no alcohol, no antihist
theta_alcohol_nomeds = 36 # alcohol, no antihist
# Create samples
q = 1000
df_pois = pd.DataFrame({
'nsneeze': np.concatenate((np.random.poisson(theta_noalcohol_meds, q),
np.random.poisson(theta_alcohol_meds, q),
np.random.poisson(theta_noalcohol_nomeds, q),
np.random.poisson(theta_alcohol_nomeds, q))),
'alcohol': np.concatenate((np.repeat(False, q),
np.repeat(True, q),
np.repeat(False, q),
np.repeat(True, q))),
'nomeds': np.concatenate((np.repeat(False, q),
np.repeat(False, q),
np.repeat(True, q),
np.repeat(True, q)))})
In [5]:
df_pois.groupby(['nomeds', 'alcohol'])['nsneeze'].agg(['mean', 'var'])
Out[5]:
Since the mean and variance of a Poisson distributed random variable are equal, the sample means and variances are very close.
Now, suppose every subject in the dataset had the flu, increasing the variance of their sneezing (and causing an unfortunate few to sneeze over 70 times a day). If the mean number of sneezes stays the same but variance increases, the data might follow a negative binomial distribution.
In [6]:
# Gamma shape parameter
alpha = 10
def get_nb_vals(mu, alpha, size):
"""Generate negative binomially distributed samples by
drawing a sample from a gamma distribution with mean `mu` and
shape parameter `alpha', then drawing from a Poisson
distribution whose rate parameter is given by the sampled
gamma variable.
"""
g = stats.gamma.rvs(alpha, scale=mu / alpha, size=size)
return stats.poisson.rvs(g)
# Create samples
n = 1000
df = pd.DataFrame({
'nsneeze': np.concatenate((get_nb_vals(theta_noalcohol_meds, alpha, n),
get_nb_vals(theta_alcohol_meds, alpha, n),
get_nb_vals(theta_noalcohol_nomeds, alpha, n),
get_nb_vals(theta_alcohol_nomeds, alpha, n))),
'alcohol': np.concatenate((np.repeat(False, n),
np.repeat(True, n),
np.repeat(False, n),
np.repeat(True, n))),
'nomeds': np.concatenate((np.repeat(False, n),
np.repeat(False, n),
np.repeat(True, n),
np.repeat(True, n)))})
In [7]:
df.groupby(['nomeds', 'alcohol'])['nsneeze'].agg(['mean', 'var'])
Out[7]:
As in the Poisson regression example, we see that drinking alcohol and/or not taking antihistamines increase the sneezing rate to varying degrees. Unlike in that example, for each combination of alcohol and nomeds, the variance of nsneeze is higher than the mean. This suggests that a Poisson distrubution would be a poor fit for the data since the mean and variance of a Poisson distribution are equal.
In [8]:
g = sns.factorplot(x='nsneeze', row='nomeds', col='alcohol', data=df, kind='count', aspect=1.5)
# Make x-axis ticklabels less crowded
ax = g.axes[1, 0]
labels = range(len(ax.get_xticklabels(which='both')))
ax.set_xticks(labels[::5])
ax.set_xticklabels(labels[::5]);
In [9]:
fml = 'nsneeze ~ alcohol + nomeds + alcohol:nomeds'
with pm.Model() as model:
pm.glm.GLM.from_formula(formula=fml, data=df, family=pm.glm.families.NegativeBinomial())
# Old initialization
# start = pm.find_MAP(fmin=optimize.fmin_powell)
# C = pm.approx_hessian(start)
# trace = pm.sample(4000, step=pm.NUTS(scaling=C))
trace = pm.sample(2000, njobs=2)
In [10]:
rvs = [rv.name for rv in strip_derived_rvs(model.unobserved_RVs)]
plot_traces(trace[1000:], varnames=rvs);
In [11]:
# Transform coefficients to recover parameter values
np.exp(pm.df_summary(trace[1000:], varnames=rvs)[['mean','hpd_2.5','hpd_97.5']])
Out[11]:
The mean values are close to the values we specified when generating the data:
Finally, even though the sample for mu is highly skewed, its median value is close to the sample mean, and the mean of alpha is also quite close to its actual value of 10.
In [12]:
np.percentile(trace[1000:]['mu'], [25,50,75])
Out[12]:
In [13]:
df.nsneeze.mean()
Out[13]:
In [14]:
trace[1000:]['alpha'].mean()
Out[14]: