ord_r2d2: Ordinal regression in Stan with R2D2 prior

In R2D2ordinal: Implements Pseudo-R2D2 Prior for Ordinal Regression

Ordinal regression in Stan with R2D2 prior

Description

This function carries out a Bayesian ordinal regression model in Stan using the proposed psuedo-R2D2 prior

Usage

ord_r2d2(
  x,
  y,
  K,
  a = 1,
  b = 10,
  hyper = NULL,
  alpha = rep(1, K),
  nsims = 1000,
  nreps = 5,
  no_cores = 10,
  progress = FALSE,
  ...
)

Arguments

x	covariate matrix
y	response variables
K	number of response categories
a	hyper-parameter of prior for R2 ~ Beta(a,b)
b	hyper-parameter of prior for R2 ~ Beta(a,b)
hyper	hyper-parameters for W prior
alpha	prior hyper-parameters for prior Dirichlet distribution on response probabilities
nsims	number of times to simulate data
nreps	number of times to run the algorithm (default = 5)
no_cores	number of cores to parallelize data-generation process
progress	logical. if TRUE, shows the progress bars from the posterior sampling.
...	optional hyper-parameters for Stan fitting

Value

Stan model fit

Examples

# X are covariates, Y are responses, K is number of response categories
# This example will yield low R2 values as the response are independent of the covariates.
set.seed(1234)
n = 100
p = 5
X = matrix(rnorm(n*p), nrow = n, ncol=p)
K = 3
Y = sample(1:K, 100, replace=TRUE)
a = 1
b = 5
# Pre-computed hyperparameters
fit <- ord_r2d2(X, Y, K, hyper=c(0.002, 0.989, 1.013), no_cores=1)
out <- rstan::extract(fit)
# Plot histogram of posterior W
hist(out$W, xlab="W")

R2D2ordinal documentation built on April 3, 2025, 7:50 p.m.