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
|