Fake dataset of a randomized experiment on student grades

Fake dataset of a randomized experiment on student grades. See Chapter 16 in Regression and Other Stories.

Source: FakeMidtermFinal/SimulationBasedDesign.Rmd.

The Python version keeps data handling explicit and uses lapylace for Stan-backed generalized linear models, so the statistical model can be read from a formula rather than from handwritten Stan.

from pathlib import Path
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import lapylace as lp

sys.path.append(str(Path.cwd().parent / 'python'))
from data import ros_path

Models

The formulas below are the Python counterparts of the model formulas in the source example. Use lapylace for the Stan-backed Bayesian fit with the same formula interface.

# fit = lp.stan_glm('y ~ z', data=df, family=lp.gaussian(), chains=4, iter_sampling=1000)

# fit = lp.stan_glm('y ~ z + x', data=df, family=lp.gaussian(), chains=4, iter_sampling=1000)

# fit = lp.stan_glm('y ~ z', data=df, family=lp.gaussian(), chains=4, iter_sampling=1000)

# fit = lp.stan_glm('y ~ z + x', data=df, family=lp.gaussian(), chains=4, iter_sampling=1000)

# fit = lp.stan_glm('y ~ z', data=df, family=lp.gaussian(), chains=4, iter_sampling=1000)

# fit = lp.stan_glm('y ~ z + x', data=df, family=lp.gaussian(), chains=4, iter_sampling=1000)

Notes

  • Source computation blocks represented: 4.
  • Data paths are expressed through the shared ros_path() helper.
  • Formula-based Bayesian regressions are routed through lapylace.stan_glm().
  • Plotting and simulation work uses NumPy, pandas, matplotlib idioms.