JointAI (version 0.6.1)

sharedParams: Parameters used by several functions in JointAI.

Description

Parameters used by several functions in JointAI.

Arguments

object

object inheriting from class 'JointAI'

no_model

names of variables for which no model should be specified. Note that this is only possible for completely observed variables and implies the assumptions of independence between the excluded variable and the incomplete variables.

subset

subset of parameters/variables/nodes (columns in the MCMC sample). Uses the same logic as the argument monitor_params in *_imp.

exclude_chains

optional vector of the index numbers of chains that should be excluded

start

the first iteration of interest (see window.mcmc)

end

the last iteration of interest (see window.mcmc)

n.adapt

the number of iterations for adaptation of the MCMC samplers (see also adapt)

n.iter

the number of iterations of the MCMC chain (after adaptation; see also coda.samples)

n.chains

the number of MCMC chains to be used

quiet

if TRUE then messages generated during compilation will be suppressed, as well as the progress bar during adaptation (see jags.model)

thin

thinning interval (see window.mcmc)

nrow, ncol

optional number of rows and columns in the plot layout; automatically chosen if unspecified

use_ggplot

logical; Should ggplot be used instead of the base graphics?

warn

logical; should warnings be given? Default is TRUE. (Note: this applies only to warnings given directly by JointAI.)

mess

logical; should messages be given? Default is TRUE. (Note: this applies only to messages given directly by JointAI.)

xlab, ylab

labels for the x- and y-axis

use_level

logical; should the multi-level structure be taken into account? This requires specification of the argument idvar.

idvar

name of the column that specifies the multi-level grouping structure

keep_aux

logical; Should constant effects of auxiliary variables be kept in the output?

ridge

logical; should the parameters of the main model be penalized using ridge regression? Default is FALSE

parallel

logical; should the chains be sampled using parallel computation? Default is FALSE

n.cores

number of cores to use for parallel computation; if left empty all except two cores will be used

seed

optional seed value for reproducibility

ppc

logical: should monitors for posterior predictive checks be set? (not yet used)