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Poisson GLM

from spglm.glm import GLM
from spglm.family import Poisson
import libpysal
import numpy

# Load sample dataset - columbus dataset
db = libpysal.io.open(libpysal.examples.get_path("columbus.dbf"))

# Set dependent variable
y = numpy.array(db.by_col("HOVAL"))
y = numpy.reshape(y, (49, 1))
# Round dependent variable and convert to integer
# for the example since Poisson is for discrete data
y = numpy.round(y).astype(int)

# Set indepdent varibLES
X = []
X.append(db.by_col("INC"))
X.append(db.by_col("CRIME"))
X = numpy.array(X).T

# Estimate Poisson GLM

# First instantiate a GLM model object
# -- Set family to Poisson family object for Poisson GLM
model = GLM(y, X, family=Poisson())

# Then use the fit method to estimate coefficients and compute diagnostics
results = model.fit()

# Estimated prameters, intercept is always the first column on the left
print(results.params)

[ 3.92159085  0.01183491 -0.01371397]

# Parameter standard errors
print(results.bse)

[0.13049161 0.00511599 0.00193769]

# Parameter t-values
print(results.tvalues)

[30.0524361   2.31331634 -7.07748998]

# Model AIC
print(results.aic)

500.8518417993878