TreeBUGS: simpleMPT – R documentation

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simpleMPT

C++ Sampler for Standard (Nonhierarchical) MPT Models

Description

Fast Gibbs sampler in C++ that is tailored to the standard fixed-effects MPT model (i.e., fixed-effects, non-hierarchical MPT). Assumes independent parameters per person if a matrix of frequencies per person is supplied.

Usage

simpleMPT(
  eqnfile,
  data,
  restrictions,
  n.iter = 2000,
  n.burnin = 500,
  n.thin = 3,
  n.chains = 3,
  ppp = 0,
  alpha = 1,
  beta = 1,
  parEstFile,
  posteriorFile,
  cores = 1
)

Arguments

`eqnfile`	The (relative or full) path to the file that specifies the MPT model (standard .eqn syntax). Note that category labels must start with a letter (different to multiTree) and match the column names of `data`. Alternatively, the EQN-equations can be provided within R as a character value (cf. `readEQN`). Note that the first line of an .eqn-file is reserved for comments and always ignored.
`data`	The (relative or full) path to the .csv file with the data (comma separated; category labels in first row). Alternatively: a data frame or matrix (rows=individuals, columns = individual category frequencies, category labels as column names)
`restrictions`	Specifies which parameters should be (a) constant (e.g., `"a=b=.5"`) or (b) constrained to be identical (e.g., `"Do=Dn"`) or (c) treated as fixed effects (i.e., identical for all participants; `"a=b=FE"`). Either given as the path to a text file with restrictions per row or as a list of restrictions, e.g., `list("D1=D2","g=0.5")`. Note that numbers in .eqn-equations (e.g., `d(1-g).50`) are directly interpreted as equality constraints.
`n.iter`	Number of iterations per chain (including burnin samples). See `run.jags` for details.
`n.burnin`	Number of samples for burnin (samples will not be stored and removed from n.iter)
`n.thin`	Thinning rate.
`n.chains`	number of MCMC chains (sampled in parallel).
`ppp`	number of samples to compute posterior predictive p-value (see `posteriorPredictive`)
`alpha`	first shape parameter(s) for the beta prior-distribution of the MPT parameters θ_s (can be a named vector to use a different prior for each MPT parameter)
`beta`	second shape parameter(s)
`parEstFile`	Name of the file to with the estimates should be stored (e.g., "parEstFile.txt")
`posteriorFile`	path to RData-file where to save the model including MCMC posterior samples (an object named `fittedModel`; e.g., `posteriorFile="mcmc.RData"`)
`cores`	number of CPUs to be used

Details

Beta distributions with fixed shape parameters α and β are used. The default α=1 and β=1 assumes uniform priors for all MPT parameters.

Author(s)

Daniel Heck

Examples

## Not run: 
# fit nonhierarchical MPT model for aggregated data (see ?arnold2013):
EQNfile <- system.file("MPTmodels/2htsm.eqn", package="TreeBUGS")
d.encoding <- subset(arnold2013, group == "encoding", select = -(1:4))
fit <- simpleMPT(EQNfile, colSums(d.encoding),
                restrictions=list("D1=D2=D3","d1=d2","a=g"))
# convergence
plot(fit)
summary(fit)

## 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