JointAI (version 1.0.6)

JointAIObject: Fitted object of class 'JointAI'

Description

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

Value

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