Parameters used by several functions in JointAI
object inheriting from class 'JointAI'
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.
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".
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 of parameters/variables/nodes (columns in the MCMC
sample). Follows the same principle as the argument
optional vector of the index numbers of chains that should be excluded
the first iteration of interest
the last iteration of interest
number of iterations for adaptation of the MCMC samplers
number of iterations of the MCMC chain (after adaptation;
number of MCMC chains
TRUE then messages generated by
rjags during compilation as well as the progress bar
for the adaptive phase will be suppressed,
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.
thinning interval (integer; see
thin = 1 (default) will keep the MCMC samples
from all iterations;
thin = 5 would only keep every 5th
optional; number of rows in the plot layout; automatically chosen if unspecified
optional; number of columns in the plot layout; automatically chosen if unspecified
logical; Should ggplot be used instead of the base graphics?
logical; should warnings be given? Default is
logical; should messages be given? Default is
labels for the x- and y-axis
name of the column that specifies the multi-level grouping structure
optional; seed value (for reproducibility)
logical: should monitors for posterior predictive checks be set? (not yet used)
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
"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.