Laurel - 1 year ago 360

Python Question

There are 2 types of Generalized Linear Models:

1. Log-Linear Regression, also known as Poisson Regression

2. Logistic Regression

How to implement the Poisson Regression in Python for Price Elasticity prediction?

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Answer Source

Have a look at the statmodels package in python.

Here is an example

A bit more of input to avoid the *link only answer*

Assumming you know python here is an extract of the example I mentioned earlier.

```
import numpy as np
import pandas as pd
from statsmodels.genmod.generalized_estimating_equations import GEE
from statsmodels.genmod.cov_struct import (Exchangeable,
Independence,Autoregressive)
from statsmodels.genmod.families import Poisson
```

`pandas`

will hold the data frame with the data you want to use to feed your poisson model.
`statsmodels`

package contains large family of statistical models such as Linear, probit, poisson etc. from here you will import the Poisson family model (hint: see last import)

The way you fit your model is as follow (assuming your dependent variable is called `y`

and your IV are age, trt and base):

```
fam = Poisson()
ind = Independence()
model1 = GEE.from_formula("y ~ age + trt + base", "subject", data, cov_struct=ind, family=fam)
result1 = model1.fit()
print(result1.summary())
```

As I am not familiar with the nature of your problem I would suggest to have a look at negative binomial regression if you need to count data is well overdispersed. with High overdispersion your poisson assumptions may not hold.

Pletora of info for poisson regression in R - just google it.

Hope now this answer helps.

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