TreeBUGS (version 1.5.0)

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 \(\theta_s\) (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

Author

Daniel Heck

Details

Beta distributions with fixed shape parameters \(\alpha\) and \(\beta\) are used. The default \(\alpha=1\) and \(\beta=1\) assumes uniform priors for all MPT parameters.

Examples

if (FALSE) {
# 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)
}