clm_stan: Fit a Cumulative Link Model using CmdStanR

In clmstan: Cumulative Link Models with 'CmdStanR'

Fit a Cumulative Link Model using CmdStanR

Description

Fit a Cumulative Link Model using CmdStanR

Usage

clm_stan(
  formula,
  data,
  link = "logit",
  base = "logit",
  threshold = "flexible",
  link_param = NULL,
  prior = NULL,
  chains = 4,
  iter = 2000,
  warmup = NULL,
  ...
)

Arguments

formula	A formula specifying the model (response ~ predictors)
data	A data frame containing the variables in the formula
link	Link function. One of "logit" (default), "probit", "cloglog", "loglog", "cauchit", "tlink", "gev", "aep", "sp", "aranda_ordaz", "log_gamma"
base	Base distribution for SP link. One of "logit" (default), "probit", "cloglog", "loglog", "cauchit", "tlink". Ignored for other link functions.
threshold	Threshold structure. One of "flexible" (default), "equidistant", "symmetric"
link_param	A list of link parameters. For flexible links, values can be: Numeric: Use as fixed value (e.g., list(df = 8)) "estimate": Estimate the parameter with Bayesian inference
prior	Prior specification. Can be either: A clm_prior object created by clm_prior() A distribution-based prior using prior() with distribution functions (normal(), gamma(), student_t(), cauchy())
chains	Number of MCMC chains (default: 4)
iter	Total iterations per chain (default: 2000)
warmup	Warmup iterations per chain. If NULL (default), uses floor(iter/2)
...	Additional arguments passed to cmdstanr::sample()

Value

An object of class "clmstan"

Examples

## Not run: 
# Fit a proportional odds model
library(ordinal)
data(wine)
fit <- clm_stan(rating ~ temp + contact, data = wine, link = "logit")
print(fit)

# Fit with t-link (fixed df)
fit_t <- clm_stan(rating ~ temp, data = wine, link = "tlink",
                  link_param = list(df = 8))

# Fit with GEV link (estimate xi)
fit_gev <- clm_stan(rating ~ temp, data = wine, link = "gev",
                    link_param = list(xi = "estimate"))

## End(Not run)

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