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Gaussian GLM

from spglm.glm import GLM
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))

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

# Estimate Gaussian GLM

# First instantiate a GLM model object
# -- Gaussian is the default family parameter so it doesn't need to be set
model = GLM(y, X)

# 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)

[46.42818268  0.62898397 -0.48488854]

# Parameter standard errors
print(results.bse)

[13.19175703  0.53591045  0.18267291]

# Parameter t-values
print(results.tvalues)

[ 3.51948437  1.17367365 -2.65440864]

# Model AIC
print(results.aic)

408.73548964604873