Fit a one-arm Dirichlet process mixture without a GPD tail

dpmix() is the one-step convenience wrapper for the bulk-only model. It combines bundle and mcmc for one-arm data.

Usage

dpmix(
  y = NULL,
  X = NULL,
  treat = NULL,
  data = NULL,
  mcmc = list(),
  formula = NULL,
  ...
)

Arguments

y

Either a response vector or a bundle object.

X

Optional design matrix/data.frame.

treat

Optional binary treatment indicator. If supplied, this wrapper errors; use dpmix.causal() for causal models.

data

Optional data.frame used with formula.

mcmc

Named list of run arguments passed to mcmc() (including optional performance controls such as parallel_chains, workers, timing, and z_update_every).

formula

Optional formula.

...

Additional build arguments passed to build_nimble_bundle.

Value

A fitted object of class "mixgpd_fit".

Details

The fitted model targets the posterior predictive bulk distribution $$f(y \mid x) = \int f(y \mid x, \theta)\,d\Pi(\theta),$$ without the spliced tail augmentation used by dpmgpd.

Use this wrapper when the outcome support is adequately modeled by the bulk kernel alone. If you need threshold exceedance modeling or extreme-quantile extrapolation, use dpmgpd instead.

See also

build_nimble_bundle, bundle, dpmgpd, predict.mixgpd_fit, summary.mixgpd_fit.