Creates control specifics for estimation options for estimating Bayesian latent variable models.
blatentControl(
calculateDIC = TRUE,
calculateWAIC = TRUE,
defaultPriors = setDefaultPriors(),
defaultInitializeParameters = setDefaultInitializeParameters(),
estimateLatents = TRUE,
estimator = "blatent",
estimatorType = "R",
estimatorLocation = "",
executableName = "",
fileSaveLocation = paste0(getwd(), "/"),
HDPIntervalValue = 0.95,
maxTuneChains = 0,
minTuneChains = 0,
missingMethod = "omit",
nBurnin = 1000,
nChains = 4,
nCores = -1,
nSampled = 1000,
nThin = 5,
nTuneIterations = 0,
parallel = FALSE,
posteriorPredictiveChecks = setPosteriorPredictiveCheckOptions(),
seed = NULL
)A list of values containing named entries for all arguments shown above.
Calculates DIC following Markov chain. DIC will be marginalized for models with latent variables. Defaults to TRUE.
Calculates WAIC following Markov chain. WAIC will be marginalized for models with latent variables. Defaults to TRUE.
Sets priors for all parameters that are not specified in priorsList of
blatentEstimate. Defaults to list set by
setDefaultPriors function. Values in list currently allowed are
normalMean for the mean of a normal distribution (defaults to 0).
normalVariance for the variance of a normal distribution (defaults to 1000).
normalCovariance for the covariance of a multivariate normal distribution
(defaults to 0).
List of values that sets distributions used to initialize
parameters. Defaults to list set by setDefaultInitializeParameters
function. Values in list currently allowed are:
normalMean for the mean of a normal distribution (defaults to 0).
normalVariance for the variance of a normal distribution (defaults to 1).
normalCovariance for the covariance of a multivariate normal distribution
(defaults to 0).
Estimate latent variables summaries for each observation following MCMC estimation. Defaults to TRUE.
Sets the estimation algorithm to be used. Currently, one option is available that works. The eventual values will be:
"blatentEstimator" Sets the estimation algorithm to be used to the R package blatentEstimator, which must be installed (default).
"GPDCM" Gibbs Probit Diagnostic Classification Model is allowed but not functional.
Sets location of estimator. Currently, only one option (the default) works.
"R" Sets estimation via R packages (default).
"external" for estimation routines external to R. Currently external syntax does not work.
Sets the path to the location of estimator executable, if estimatorType is
"external". Currently set to "".
Sets the name for the executable file for the estimator. Defaults to
""
Sets the path for output files used for external estimation routines.
Only used when estimatorType = "external".
Sets the value for all highest density posterior interval parameter summaries. Defaults to 0.95.
Sets the maximum number of tuning chains for MCMC sampling algorithm, if needed. Currently,
no Metropolis steps exist in algorithm, so is unused. Defaults to 0.
Sets the minimum number of tuning chains for MCMC sampling algorithm, if needed.
Currently, no Metropolis steps exist in algorithm, so is unused. Defaults to 0.
Sets the way missing observed variables are treated within algorithm. Defaults to "skip".
Current options are:
"skip" Skips all missing variables in model likelihoods. For dependent variables
predicted variables with missing values, omits any case with missing values.
"imputeBayes" Model-based imputation using Bayes theorem.
Sets the number of burnin iterations. Defaults to 1000.
Sets the number of independent Markov chains run by the program. Defaults to 4.
Sets the number of cores used in parallel processing if option parallel is
TRUE. Defaults to -1. Values are semi-indicative of how many
processors will be used:
-1 indicates that all but one available processor will be used.
0 indicates that all available processors will be used.
>0 indicates that specific number of processors will be used, if available.
Note: currently, parallel processing is unavailable, so this is unused.
Sets the number of posterior draws to sample, per chain. Defaults to 1000.
Sets the thinning interval, saving only the posterior draws that comes at this value.
Defaults to 5.
Sets the number of iterations per tuning chain, if needed. Currently,
no Metropolis steps exist in algorithm, so is unused. Defaults to 0.
If TRUE, enables parallel processing of estimation and PPCM analyses.
Currently, parallel processing is unavailable, so this is unused. Defaults to FALSE.
List of values that sets options for posterior predictive model checks.
Defaults to list set by setPosteriorPredictiveCheckOptions
function. Values in list currently allowed are:
Sets the random number seed for the analysis. Defaults to NULL, which does not
set the seed and uses current session value per each analysis.