Parameters used by several functions in JointAI

- object
object inheriting from class 'JointAI'

- no_model
optional; vector of 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.

- timevar
name of the variable indicating the time of the measurement of a time-varying covariate in a proportional hazards survival model (also in a joint model). The variable specified in "timevar" will automatically be added to "no_model".

- assoc_type
named vector specifying the type of the association used for a time-varying covariate in the linear predictor of the survival model when using a "JM" model. Implemented options are "underl.value" (linear predictor; default for covariates modelled using a Gaussian, Gamma, beta or log-normal distribution) covariates) and "obs.value" (the observed/imputed value; default for covariates modelled using other distributions).

- subset
subset of parameters/variables/nodes (columns in the MCMC sample). Follows the same principle 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
number of iterations for adaptation of the MCMC samplers (see

`adapt`

)- n.iter
number of iterations of the MCMC chain (after adaptation; see

`coda.samples`

)- n.chains
number of MCMC chains

- quiet
logical; if

`TRUE`

then messages generated by**rjags**during compilation as well as the progress bar for the adaptive phase will be suppressed, (see`jags.model`

)- progress.bar
character string specifying the type of progress bar. Possible values are "text" (default), "gui", and "none" (see

`update`

). Note: when sampling is performed in parallel it is not possible to display a progress bar.- thin
thinning interval (integer; see

`window.mcmc`

). For example,`thin = 1`

(default) will keep the MCMC samples from all iterations;`thin = 5`

would only keep every 5th iteration.- nrow
optional; number of rows in the plot layout; automatically chosen if unspecified

- ncol
optional; number of 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`

.- mess
logical; should messages be given? Default is

`TRUE`

.- xlab, ylab
labels for the x- and y-axis

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

- seed
optional; seed value (for reproducibility)

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

- rd_vcov
optional character string or list (of lists or character strings) specifying the structure of the variance covariance matrix/matrices of the random effects for multivariate mixed models. Options are

`"full`

,`"blockdiag"`

(default) and`"indep"`

. Different structures can be specified per grouping level (in multi-level models with more than two levels) by specifying a list with elements per grouping level. To specify different structures for different outcomes, a list (maybe nested in the list per grouping level) can be specified. This list should have the type of structure as names and contain vectors of variable names that belong to the respective structure.