Updates a fitted RoBSA object

Source: R/main.R

update.RoBSA can be used to

  1. add an additional model to an existing "RoBSA" object by specifying the distribution, and either null or alternative priors for each parameter and prior weight of the model,

  2. change the prior weights of fitted models by specifying a vector prior_weights of the same length as the fitted models,

  3. refitting models that failed to converge with updated settings of control parameters,

  4. or changing the convergence criteria and recalculating the ensemble results by specifying new control argument and setting refit_failed == FALSE.

# S3 method for RoBSA
update(
  object,
  refit_failed = TRUE,
  formula = NULL,
  priors = NULL,
  test_predictors = "",
  distribution = NULL,
  model_weights = 1,
  prior_beta_null = get_default_prior_beta_null(),
  prior_beta_alt = get_default_prior_beta_alt(),
  prior_factor_null = get_default_prior_factor_null(),
  prior_factor_alt = get_default_prior_factor_alt(),
  prior_intercept = get_default_prior_intercept(),
  prior_aux = get_default_prior_aux(),
  chains = NULL,
  adapt = NULL,
  burnin = NULL,
  sample = NULL,
  thin = NULL,
  autofit = NULL,
  parallel = NULL,
  autofit_control = NULL,
  convergence_checks = NULL,
  save = "all",
  seed = NULL,
  silent = TRUE,
  ...
)

Arguments

object

a fitted RoBSA object

refit_failed

whether failed models should be refitted. Relevant only if new priors or prior_weights are not supplied. Defaults to TRUE.

formula

formula for the survival model

priors

names list of prior distributions for each predictor. It allows users to specify both the null and alternative hypothesis prior distributions by assigning a named list (with "null" and "alt" object) to the predictor

test_predictors

vector of predictor names to be tested with Bayesian model-averaged testing. Defaults to NULL, no parameters are tested.

distribution

a distribution of the new model.

model_weights

either a single value specifying prior model weight of a newly specified model using priors argument, or a vector of the same length as already fitted models to update their prior weights.

prior_beta_null

default prior distribution for the null hypotheses of continuous predictors

prior_beta_alt

default prior distribution for the alternative hypotheses of continuous predictors

prior_factor_null

default prior distribution for the null hypotheses of categorical predictors

prior_factor_alt

default prior distribution for the alternative hypotheses of categorical predictors

prior_intercept

named list containing prior distribution for the intercepts (with names corresponding to the distributions)

prior_aux

named list containing prior distribution for the auxiliary parameters (with names corresponding to the distributions)

chains

a number of chains of the MCMC algorithm.

adapt

a number of adaptation iterations of the MCMC algorithm. Defaults to 500.

burnin

a number of burnin iterations of the MCMC algorithm. Defaults to 2000.

sample

a number of sampling iterations of the MCMC algorithm. Defaults to 5000.

thin

a thinning of the chains of the MCMC algorithm. Defaults to 1.

autofit

whether the model should be fitted until the convergence criteria (specified in autofit_control) are satisfied. Defaults to TRUE.

parallel

whether the individual models should be fitted in parallel. Defaults to FALSE. The implementation is not completely stable and might cause a connection error.

autofit_control

allows to pass autofit control settings with the set_autofit_control() function. See ?set_autofit_control for options and default settings.

convergence_checks

automatic convergence checks to assess the fitted models, passed with set_convergence_checks() function. See ?set_convergence_checks for options and default settings.

save

whether all models posterior distributions should be kept after obtaining a model-averaged result. Defaults to "all" which does not remove anything. Set to "min" to significantly reduce the size of final object, however, some model diagnostics and further manipulation with the object will not be possible.

seed

a seed to be set before model fitting, marginal likelihood computation, and posterior mixing for reproducibility of results. Defaults to NULL - no seed is set.

silent

whether all print messages regarding the fitting process should be suppressed. Defaults to TRUE. Note that parallel = TRUE also suppresses all messages.

...

additional arguments.

Value

update.RoBSA returns an object of class 'RoBSA'.

Details

See RoBSA() for more details.