clm_prior: Prior Specification for clmstan

In clmstan: Cumulative Link Models with 'CmdStanR'

Prior Specification for clmstan

Description

Create prior specifications for cumulative link models in clmstan.

Default priors:

• Regression coefficients (beta): normal(0, 2.5)

• Cutpoints (c): normal(0, 10) for flexible, normal(0, 5) for symmetric

• Interval (d): gamma(2, 0.5) for equidistant threshold

Link parameter priors (when estimated):

Link	Parameter	Default Prior
tlink	df	gamma(2, 0.1)
aranda_ordaz	lambda	gamma(0.5, 0.5)
gev	xi	normal(0, 2)
sp	r	gamma(0.5, 0.5)
log_gamma	lambda	normal(0, 1)
aep	theta1, theta2	gamma(2, 1)

Usage

clm_prior(
  beta_sd = NULL,
  c_sd = NULL,
  c1_mu = NULL,
  c1_sd = NULL,
  d_alpha = NULL,
  d_beta = NULL,
  cpos_sd = NULL,
  df_alpha = NULL,
  df_beta = NULL,
  lambda_ao_alpha = NULL,
  lambda_ao_beta = NULL,
  lambda_lg_mu = NULL,
  lambda_lg_sd = NULL,
  xi_mu = NULL,
  xi_sd = NULL,
  r_alpha = NULL,
  r_beta = NULL,
  theta1_alpha = NULL,
  theta1_beta = NULL,
  theta2_alpha = NULL,
  theta2_beta = NULL
)

Arguments

beta_sd	SD for normal prior on regression coefficients. Default: 2.5 (weakly informative)
c_sd	SD for normal prior on cutpoints (flexible threshold). Default: 10
c1_mu	Mean for normal prior on first cutpoint (equidistant threshold). Default: 0
c1_sd	SD for normal prior on first cutpoint (equidistant threshold). Default: 10
d_alpha	Gamma shape for interval d (equidistant threshold). Default: 2
d_beta	Gamma rate for interval d (equidistant threshold). Default: 0.5
cpos_sd	SD for half-normal prior on positive cutpoints (symmetric threshold). Default: 5
df_alpha	Gamma shape for tlink df. Default: 2
df_beta	Gamma rate for tlink df. Default: 0.1
lambda_ao_alpha	Gamma shape for aranda_ordaz lambda. Default: 0.5
lambda_ao_beta	Gamma rate for aranda_ordaz lambda. Default: 0.5
lambda_lg_mu	Normal mean for log_gamma lambda. Default: 0
lambda_lg_sd	Normal SD for log_gamma lambda. Default: 1
xi_mu	Normal mean for GEV xi. Default: 0
xi_sd	Normal SD for GEV xi. Default: 2
r_alpha	Gamma shape for SP r. Default: 0.5
r_beta	Gamma rate for SP r. Default: 0.5
theta1_alpha	Gamma shape for AEP theta1. Default: 2
theta1_beta	Gamma rate for AEP theta1. Default: 1
theta2_alpha	Gamma shape for AEP theta2. Default: 2
theta2_beta	Gamma rate for AEP theta2. Default: 1

Value

An object of class "clm_prior" containing prior specifications.

Examples

# Create a prior object (does not require Stan)
my_prior <- clm_prior(beta_sd = 2, c_sd = 5)
print(my_prior)

## Not run: 
# Examples below require CmdStan and compiled Stan models
data(wine, package = "ordinal")

# Default priors (no customization needed)
fit <- clm_stan(rating ~ temp, data = wine,
                chains = 2, iter = 500, warmup = 250, refresh = 0)

# Custom prior for regression coefficients
fit2 <- clm_stan(rating ~ temp, data = wine,
                 prior = clm_prior(beta_sd = 1),
                 chains = 2, iter = 500, warmup = 250, refresh = 0)

## End(Not run)

clmstan documentation built on June 29, 2026, 5:06 p.m.