TreeBUGS: posteriorPredictive – R documentation

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posteriorPredictive

Get Posterior Predictive Samples

Description

Draw predicted frequencies based on posterior distribution of (a) individual estimates (default) or (b) for a new participant (if numItems is provided; does not consider continuous or discrete predictors in traitMPT).

Usage

posteriorPredictive(
  fittedModel,
  M = 100,
  numItems = NULL,
  expected = FALSE,
  nCPU = 4
)

Arguments

`fittedModel`	fitted latent-trait or beta MPT model (`traitMPT`, `betaMPT`)
`M`	number of posterior predictive samples. As a maximum, the number of posterior samples in `fittedModel` is used.
`numItems`	optional: a vector with the number of items per MPT tree to sample predicted data for a new participant (first, a participant vector θ is sampled from the hierarchical posterior; second, frequencies are generated).
`expected`	if `TRUE`, the expected frequencies per person are returned (without additional sampling from a multinomial distribution)
`nCPU`	number of CPUs used for parallel sampling. For large models and many participants, this requires considerable computer-memory resources (as a remedy, use `nCPU=1`).

Value

by default, a list of M posterior-predictive samples (i.e., matrices) with individual frequencies (rows=participants, columns=MPT categories). For M=1, a single matrix is returned. If numItems is provided, a matrix with samples for a new participant is returned (rows=samples)

Examples

## Not run: 
# add posterior predictive samples to fitted model
#     (facilitates plotting using ?plotFit)
fittedModel$postpred$freq.pred <-
     posteriorPredictive(fittedModel, M=1000)

## End(Not run)

TreeBUGS

Hierarchical Multinomial Processing Tree Modeling

v1.4.7

GPL-3

Authors

Daniel W. Heck [aut, cre] (<https://orcid.org/0000-0002-6302-9252>), Nina R. Arnold [aut, dtc], Denis Arnold [aut], Alexander Ly [ctb], Marius Barth [ctb] (<https://orcid.org/0000-0002-3421-6665>)

Initial release

2021-01-08