lm, glm, clm, lme, glme, clmm, survreg or coxph (with attributes family and link for GLM-type models

original (incomplete, but pre-processed) data

named vector specifying the the types of all sub-models

a list of the fixed effects formulas of the sub-model(s) for which the use had specified a formula

a list of the random effects formulas of the sub-model(s) for which the use had specified a formula

a list (for internal use) containing the data and information extracted from the data and model formulas, split up into

• a named vector identifying the levels (in the hierarchy) of all variables (Mlvls)

• a vector of the id variables that were extracted from the random effects formulas (idvar)

• a list of grouping information for each grouping level of the data (groups)

• a named vector identifying the hierarchy of the grouping levels (group_lvls)

• a named vector giving the number of observations on each level of the hierarchy (N)

• the name of the time variable (only for survival models with time-varying covariates) (timevar)

• a formula of auxiliary variables (auxvars)

• a list specifying the reference categories and dummy variables for all factors involved in the models (refs)

• a list of linear predictor information (column numbers per design matrix) for all sub-models (lp_cols)

• a list identifying information for interaction terms found in the model formulas (interactions)

• a data.frame containing information on transformations of incomplete variables (trafos)

• a data.frame containing information on transformations of all variables (fcts_all)

• a logical indicator if parameter for posterior predictive checks should be monitored (ppc; not yet used)

• a vector specifying if shrinkage of regression coefficients should be performed, and if so for which models and what type of shrinkage (shrinkage)

• the number of degrees of freedom to be used in the spline specification of the baseline hazard in proportional hazards survival models (df_basehaz)

• a list of matrices, one per level of the data, specifying centring and scaling parameters for the data (scale_pars)

• a list containing information on the outcomes (mostly relevant for survival outcomes; outcomes)

• a list of terms objects, needed to be able to build correct design matrices for the Gauss-Kronrod quadrature when, for example, splines are used to model time in a joint model (terms_list)

a list of matrices specifying the indices of the regression coefficients for each of the main models per design matrix

a list of matrices specifying the indices of regression coefficients for each covariate model per design matrix

The JAGS model as character string.

a list containing MCMC sampling related information with elements

• modelfile: path and name of the JAGS model file

• n.chains: number of MCMC chains

• n.adapt: number of iterations in the adaptive phase

• n.iter: number of iterations in the MCMC sample

• variable.names: monitored nodes

• thin: thinning interval of the MCMC sample

• inits: a list containing the initial values that were passed to rjags

the named list of parameter groups to be monitored

list with data that was passed to rjags

a list containing the values of the hyper-parameters used

a list with information used to write the imputation model syntax

a list relating the regression coefficient vectors used in the JAGS model to the names of the corresponding covariates

the JAGS model (an object of class 'jags', created by rjags)

MCMC sample on the sampling scale (included only if keep_scaled_sample = TRUE)

MCMC sample, scaled back to the scale of the data

a list with information on the computational setting (start_ime: date and time the calculation was started, duration: computational time of the model (adaptive + sampling phase), JointAI_version: package version, future: the call to future::plan(), if any was found (i.e., the specification for parallel computation))

fitted/predicted values (if available)

residuals (if available)

the original call