43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
# scipy-yli: Helpful SciPy utilities and recipes
|
|
# Copyright © 2022 Lee Yingtong Li (RunasSudo)
|
|
#
|
|
# This program is free software: you can redistribute it and/or modify
|
|
# it under the terms of the GNU Affero General Public License as published by
|
|
# the Free Software Foundation, either version 3 of the License, or
|
|
# (at your option) any later version.
|
|
#
|
|
# This program is distributed in the hope that it will be useful,
|
|
# but WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
|
# GNU Affero General Public License for more details.
|
|
#
|
|
# You should have received a copy of the GNU Affero General Public License
|
|
# along with this program. If not, see <https://www.gnu.org/licenses/>.
|
|
|
|
import yli
|
|
|
|
import numpy as np
|
|
from scipy import stats
|
|
|
|
def test_hdi_beta_vs_r():
|
|
"""Compare yli.hdi for beta distribution with R binom.bayes"""
|
|
|
|
# Inference on a beta-binomial posterior
|
|
n, N = 12, 250
|
|
prior_a, prior_b = 1, 1
|
|
|
|
distribution = stats.beta(n + prior_a, N - n + prior_b)
|
|
|
|
# Compute HDI
|
|
hdi = yli.hdi(distribution, 0.95)
|
|
|
|
#> library(binom)
|
|
#> binom.bayes(12, 250, conf.level=0.95, type='highest', prior.shape1=1, prior.shape2=1)
|
|
# method x n shape1 shape2 mean lower upper sig
|
|
#1 bayes 12 250 13 239 0.0515873 0.02594348 0.0793006 0.05
|
|
expected = np.array([0.02594348, 0.0793006])
|
|
|
|
# Allow 1e-5 tolerance
|
|
diff = np.abs(np.array(hdi) - expected)
|
|
assert (diff < 1e-5).all()
|