clmstan (version 0.1.1)

clm_prior: 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
)

Value

An object of class "clm_prior" containing prior specifications.

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

Examples

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

if (FALSE) {
# 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)
}