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https://github.com/kthohr/bayesianquantileregression
Bayesian Quantile Regression
https://github.com/kthohr/bayesianquantileregression
bayesian mcmc quantile-regression
Last synced: about 1 month ago
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Bayesian Quantile Regression
- Host: GitHub
- URL: https://github.com/kthohr/bayesianquantileregression
- Owner: kthohr
- License: apache-2.0
- Created: 2022-08-27T00:52:17.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-06-04T14:33:02.000Z (over 1 year ago)
- Last Synced: 2024-10-30T10:15:19.794Z (3 months ago)
- Topics: bayesian, mcmc, quantile-regression
- Language: C++
- Homepage: https://bqreg.readthedocs.io/en/latest/
- Size: 68.4 KB
- Stars: 3
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Bayesian Quantile Regression
A light-weight C++ implementation of Bayesian quantile regression using the asymmetric Laplace density representation of the problem, with bindings/wrappers for Python and R.
# Installation
Clone the library and pull the necessary submodules:
``` bash
# clone optim into the current directory
git clone https://github.com/kthohr/BayesianQuantileRegression ./BayesianQuantileRegression
# change directory
cd ./BayesianQuantileRegression
# clone necessary submodules
git submodule update --init
```
## Python wrapper
If you do not already have Eigen3 and Pybind11, clone and set environment variables:
```bash
# clone Eigen3
git clone https://gitlab.com/libeigen/eigen.git
# clone Pybind11
git clone https://github.com/pybind/pybind11.git
# set environment variables
export EIGEN_INCLUDE_PATH = "/eigen"
export PYBIND11_INCLUDE_PATH = "/pybind11/include"
```
```bash
# change directory into the Python subdirectory
cd BayesianQuantileRegression/Python
# install the package
python3 -m pip install . --user
```
## R wrapper
First install the `RcppEigen` package from R:
```R
install.packages("RcppEigen")
```
Then install the package from the `R` subdirectory:
```bash
# change directory into the R subdirectory
cd BayesianQuantileRegression/R
# install the package
R CMD INSTALL .
```
# Example
```python
# import libraries
import numpy as np
import matplotlib.pyplot as plt
from pybqreg import BayesianQuantileRegression
# generate data
n = 500
K = 3
X = np.random.normal(0.0, 1.0, [n, K])
X[:,0] = np.ones(n)
beta0 = np.random.uniform(1.0, 2.0, K)
beta0[0] = 5.0
Y = np.matmul(X, beta0) + np.random.normal(0.0, 1.0, n)
# initialize object
obj = BayesianQuantileRegression(Y,X)
# set prior pars
beta_bar = np.zeros(K)
V0 = np.zeros([K, K])
np.fill_diagonal(V0, 1.0/0.001)
prior_shape = 3
prior_scale = 3
obj.set_prior_params(beta_bar, V0, prior_shape, prior_scale)
# (optional) set the initial draw for beta
beta_hat = np.linalg.solve( np.matmul(X.transpose(),X), np.matmul(X.transpose(),Y) )
obj.set_initial_beta_draw(beta_hat)
# (optional) set the number of OpenMP threads
obj.set_omp_n_threads(4)
obj.get_omp_n_threads()
# (optional) set the seed value of the Gibbs sampler RNG
obj.set_seed_value(1111)
# set the target quantile (tau) and run the Gibbs sampler
tau = 0.5
n_burnin_draws = 10000
n_keep_draws = 10000
beta_draws, z_draws, sigma_draws = obj.fit(tau, n_burnin_draws, n_keep_draws, 0)
beta_mean = np.mean(beta_draws, axis = 1)
beta_std = np.std(beta_draws, axis = 1)
# plot beta draws
fig, axs = plt.subplots(1, 3, figsize=(15, 5), tight_layout=True)
for k in range(3):
axs[k].hist(beta_draws[k,:], bins = 200)
axs[k].set_xlim(beta_mean[k] - 3*beta_std[k], beta_mean[k] + 3*beta_std[k])
axs[k].axvline(beta_mean[k], c = 'r')
plt.show()
```
