JM_imp_adaptive: Run a joint model with adaptively increasing the number of...
In NErler/simvalidator: Validation of Statistical Models via Simulation

JM_imp_adaptive

R Documentation

Run a joint model with adaptively increasing the number of iterations

Description

Run a joint model with adaptively increasing the number of iterations

Usage

JM_imp_adaptive(
  formula,
  data,
  df_basehaz = 6,
  n.chains = 3,
  n.adapt = 100,
  n.iter = 0,
  thin = 1,
  monitor_params = c(analysis_main = TRUE),
  auxvars = NULL,
  timevar = NULL,
  refcats = NULL,
  models = NULL,
  no_model = NULL,
  assoc_type = NULL,
  trunc = NULL,
  shrinkage = FALSE,
  ppc = TRUE,
  seed = NULL,
  scale_vars = NULL,
  hyperpars = NULL,
  modelname = NULL,
  modeldir = NULL,
  keep_model = FALSE,
  overwrite = NULL,
  quiet = TRUE,
  progress.bar = "text",
  warn = TRUE,
  mess = TRUE,
  keep_scaled_mcmc = FALSE,
  inits_iter = 200,
  what = c("RinvD", "invD", "tau", "b"),
  extra_iter = NULL,
  minsize = 500L,
  step = 200L,
  subset = NULL,
  cutoff = 1.2,
  prop = 0.8,
  gr_max = 1.5,
  max_try = 5L,
  cc = FALSE,
  ...
)

Arguments

`formula`	a two sided model formula (see `formula`) or a list of such formulas; (more details below).
`data`	a `data.frame` containing the original data (more details below)
`df_basehaz`	degrees of freedom for the B-spline used to model the baseline hazard in proportional hazards models (`coxph_imp` and `JM_imp`)
`n.chains`	number of MCMC chains
`n.adapt`	number of iterations for adaptation of the MCMC samplers (see `adapt`)
`n.iter`	number of iterations of the MCMC chain (after adaptation; see `coda.samples`)
`thin`	thinning interval (integer; see `window.mcmc`). For example, `thin = 1` (default) will keep the MCMC samples from all iterations; `thin = 5` would only keep every 5th iteration.
`monitor_params`	named list or vector specifying which parameters should be monitored (more details below)
`auxvars`	optional; one-sided formula of variables that should be used as predictors in the imputation procedure (and will be imputed if necessary) but are not part of the analysis model(s). For more details with regards to the behaviour with non-linear effects see the vignette on Model Specification
`timevar`	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".
`refcats`	optional; either one of `"first"`, `"last"`, `"largest"` (which sets the category for all categorical variables) or a named list specifying which category should be used as reference category per categorical variable. Options are the category label, the category number, or one of "first" (the first category), "last" (the last category) or "largest" (chooses the category with the most observations). Default is "first". If reference categories are specified for a subset of the categorical variables the default will be used for the remaining variables. (See also `set_refcat`)
`models`	optional; named vector specifying the types of models for (incomplete) covariates. This arguments replaces the argument `meth` used in earlier versions. If `NULL` (default) models will be determined automatically based on the class of the respective columns of `data`.
`no_model`	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.
`assoc_type`	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).
`shrinkage`	optional; either a character string naming the shrinkage method to be used for regression coefficients in all models or a named vector specifying the type of shrinkage to be used in the models given as names.
`ppc`	logical: should monitors for posterior predictive checks be set? (not yet used)
`seed`	optional; seed value (for reproducibility)
`warn`	logical; should warnings be given? Default is `TRUE`.
`mess`	logical; should messages be given? Default is `TRUE`.
`inits_iter`	number of iteration used for the model that generates the initial values
`what`	vector of node types
`extra_iter`	number of iterations that should be added to the model if the Gelman-Rubin criterion is too large
`minsize`	the minimum number of iterations to be considered
`step`	the step size in which iterations are omitted as burn-in
`subset`	subset of parameters on which the Gelman-Rubin criterion should be evaluated. Follows the logic used in JointAI
`cutoff`	the cut-off used for the Gelman Rubin criterion
`prop`	proportion of parameters that need to be below the `cutoff`
`gr_max`	maximum allowed value for the Gelman-Rubin criterion
`max_try`	maximum number of runs of `JointAI::add_samples()`
`cc`	logical: should the model be run as a complete case analysis?
`...`	additional, optional arguments `trunc` named list specifying limits of truncation for the distribution of the named incomplete variables (see the vignette ModelSpecification) `hyperpars` list of hyper-parameters, as obtained by `default_hyperpars()` `scale_vars` named vector of (continuous) variables that will be centred and scaled (such that mean = 0 and sd = 1) when they enter a linear predictor to improve convergence of the MCMC sampling. Default is that all numeric variables and integer variables with >20 different values will be scaled. If set to `FALSE` no scaling will be done. `custom` named list of JAGS model chunks (character strings) that replace the model for the given variable. `append_data_list` list that will be appended to the list containing the data that is passed to rjags (`data_list`). This may be necessary if additional data / variables are needed for custom (covariate) models. `progress.bar` 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. `quiet` logical; if `TRUE` then messages generated by rjags during compilation as well as the progress bar for the adaptive phase will be suppressed, (see `jags.model`) `keep_scaled_mcmc` should the "original" MCMC sample (i.e., the scaled version returned by `coda.samples()`) be kept? (The MCMC sample that is re-scaled to the scale of the data is always kept.) `modelname` character string specifying the name of the model file (including the ending, either .R or .txt). If unspecified a random name will be generated. `modeldir` directory containing the model file or directory in which the model file should be written. If unspecified a temporary directory will be created. `overwrite` logical; whether an existing model file with the specified `<modeldir>/<modelname>` should be overwritten. If set to `FALSE` and a model already exists, that model will be used. If unspecified (`NULL`) and a file exists, the user is asked for input on how to proceed. `keep_model` logical; whether the created JAGS model file should be saved or removed from (`FALSE`; default) when the sampling has finished.