An object returned by one of the main functions
`*_imp`

.

`analysis_type`

`lm`

,`glm`

,`clm`

,`lme`

,`glme`

,`clmm`

,`survreg`

or`coxph`

(with attributes`family`

and`link`

for GLM-type models`formula`

The formula used in the (analysis) model.

`data`

original (incomplete, but pre-processed) data

`models`

named vector specifying the the types of all sub-models

`fixed`

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

`random`

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

`Mlist`

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`

)

`par_index_main`

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

`par_index_other`

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

`jagsmodel`

The JAGS model as character string.

`mcmc_settings`

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**

`monitor_params`

the named list of parameter groups to be monitored

`data_list`

list with data that was passed to

**rjags**`hyperpars`

a list containing the values of the hyper-parameters used

`info_list`

a list with information used to write the imputation model syntax

`coef_list`

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

`model`

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

**rjags**)`sample`

MCMC sample on the sampling scale (included only if

`keep_scaled_sample = TRUE`

)`MCMC`

MCMC sample, scaled back to the scale of the data

`comp_info`

a list with information on the computational setting (

`start_time`

: date and time the calculation was started,`duration`

: computational time of the model adaptive and sampling phase,`JointAI_version`

: package version,`R_version`

: the`R.version.string`

,`parallel`

: whether parallel computation was used,`workers`

: if parallel computation was used, the number of workers)`fitted.values`

fitted/predicted values (if available)

`residuals`

residuals (if available)

`call`

the original call