posteriorPredictive: Get Posterior Predictive Samples

In TreeBUGS: Hierarchical Multinomial Processing Tree Modeling

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 \theta 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 documentation built on May 31, 2023, 9:21 p.m.