From: Bayesian Models for Astrophysical Data, Cambridge Univ. Press

(c) 2017,  Joseph M. Hilbe, Rafael S. de Souza and Emille E. O. Ishida  

 

you are kindly asked to include the complete citation if you used this material in a publication

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Code 5.18 Logistic model using pymc3

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import numpy as np
from scipy.stats import bernoulli, uniform, binom
import pymc3 as pm
import pylab as plt
import pandas

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def invlogit(x):
    """ Inverse logit function. 
    
        input: scalar
        output: scalar
    """
    return 1.0 / (1 + np.exp(-x))

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# Data
np.random.seed(13979)                                                   # set seed to replicate example
nobs= 5000                                                                      # number of obs in model 

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x1 = binom.rvs(1, 0.6, size=nobs)
x2 = uniform.rvs(size=nobs) 

 

beta0 = 2.0
beta1 = 0.75
beta2 = -5.0

 

xb = beta0 + beta1 * x1 + beta2 * x2     
exb = 1.0/(1 + np.exp(-xb))                                               # logit link function

 

by = binom.rvs(1, exb, size=nobs)

df = pandas.DataFrame({'x1': x1, 'x2': x2, 'by': by})        # re-write data

 

# Fit
niter = 5000                                                                       # parameters for MCMC

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with pm.Model() as model_glm:
    # define priors
    beta0 = pm.Flat('beta0')
    beta1 = pm.Flat('beta1')
    beta2 = pm.Flat('beta2')

    # define likelihood
    p = invlogit(beta0 + beta1 * x1 + beta2 * x2)
    y_obs = pm.Binomial('y_obs', n=np.ones(nobs), p=p, observed=by)

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    # inference
    start = pm.find_MAP()
    step = pm.NUTS()
    trace = pm.sample(niter, step, start, progressbar=True)

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# print summary to screen
pm.summary(trace)

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# show graphical output
pm.traceplot(trace)
plt.show()

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