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Declare the observables of a bmm model for posterior predictive checks

Source: R/pp_observables.R
pp_observables.Rd

pp_observables() returns the model's observable declaration used by pp_check.bmmfit() when resp_var is specified, or NULL for models that delegate fully to brms::pp_check(). pp_simulate() draws all observables jointly from the posterior predictive distribution.

Usage

pp_observables(model)

pp_simulate(model, prep)

Arguments

model

A bmmodel object.

prep

A brmsprep object from brms::prepare_predictions().

Value

pp_observables() returns NULL for a model that delegates fully to brms::pp_check(), or a list with elements observed (a named character vector mapping observable names to brms standata slots), checks (a named list of check definitions, each with a compute closure, a label and a default plot type: a bayesplot ppc_* type or bmm's "bars_binned") and, optionally, defaults and y_placeholders (see Details). pp_simulate() returns a named list of ndraws x nobs matrices, one per simulated observable.

Details

A pp_observables() method returns list(observed, checks):

  • observed: named character vector mapping observable names to slots of the brms standata ("Y", "vreal1", "vint1", "trials", "dec"). The observable mapped to "Y" is the default check.

  • checks: named list of entries built by the internal .pp_observable() constructor. Each compute closure receives a named list keyed by names(observed) and must be elementwise, so the identical closure produces y from length-N vectors and yrep from ndraws x N matrices.

Two optional elements serve observed data that holds placeholders rather than observations, such as the summaries of an unused ezdm() boundary:

  • defaults: named vector giving, for observables whose slot a fit saved by an older bmm version lacks, the value to use for every observation.

  • y_placeholders: a function of the fit's data that returns TRUE if the "Y" slot holds placeholders. brms would plot them as data, so pp_check.bmmfit() without resp_var then checks the observable mapped to "Y" itself.

A pp_simulate() method returns a named list of ndraws x nobs matrices drawn jointly, typically through the internal .pp_simulate_joint() helper around the model's r*() function. Simulating observables independently would break their joint distribution (e.g. rt and response under the DDM). Names not in observed are ignored; declared observables that are not simulated (design quantities such as trial counts) are filled in from the data.

Register exactly one method per model at the most general class level where the declaration is identical across versions.

These two generics are exported so that model methods defined outside bmm can be registered against them, but they are an internal developer interface documented for bmm's own model authors and carry no stability guarantee across releases.