Control MCMC fitting process

set_autofit_control and set_convergence_checks control settings for the autofit process of the MCMC JAGS sampler and specify termination criteria and convergence checks.

Usage

set_autofit_control(
  max_Rhat = 1.05,
  min_ESS = 500,
  max_error = NULL,
  max_SD_error = NULL,
  max_time = list(time = 60, unit = "mins"),
  sample_extend = 1000,
  restarts = 10,
  max_extend = 10
)

set_convergence_checks(
  max_Rhat = 1.05,
  min_ESS = 500,
  max_error = NULL,
  max_SD_error = NULL,
  remove_failed = FALSE,
  balance_probability = TRUE
)

Arguments

max_Rhat

maximum value of the R-hat diagnostic. Defaults to 1.05.

min_ESS

minimum estimated sample size. Defaults to 500.

max_error

maximum value of the MCMC error. Defaults to NULL. Be aware that PEESE publication bias adjustment can have estimates on different scale than the rest of the output, resulting in relatively large max MCMC error.

max_SD_error

maximum value of the proportion of MCMC error of the estimated SD of the parameter. Defaults to NULL.

max_time

list with the time and unit specifying the maximum autofitting process per model. Passed to difftime function (possible units are "secs", "mins", "hours", "days", "weeks", "years"). Defaults to list(time = 60, unit = "mins").

sample_extend

number of samples to extend the fitting process if the criteria are not satisfied. Defaults to 1000.

restarts

number of times new initial values should be generated in case a model fails to initialize. Defaults to 10.

max_extend

number of times after which the automatic fitting function is stopped. Defaults to 10.

remove_failed

whether models not satisfying the convergence checks should be removed from the inference. Defaults to FALSE - only a warning is raised.

balance_probability

whether prior model probability should be balanced across the combinations of models with the same H0/H1 for effect / heterogeneity / bias in the case of non-convergence. Defaults to TRUE.

Value

set_autofit_control returns a list of autofit control settings and set_convergence_checks returns a list of convergence checks settings.

Details

set_autofit_control controls the automatic model fitting process, determining when to stop iterating based on convergence criteria. set_convergence_checks sets thresholds for determining whether a model has converged adequately.

The autofit control manages computational resources by setting maximum time limits and determining how many additional samples to draw if convergence criteria are not met. The convergence checks determine the quality standards that fitted models must meet.

See also

RoBMA(), update.RoBMA()

Examples

# Set custom autofit control with shorter time limit
autofit_ctrl <- set_autofit_control(
  max_time = list(time = 30, unit = "mins"),
  sample_extend = 2000
)

# Set custom convergence checks with stricter criteria
conv_checks <- set_convergence_checks(
  max_Rhat = 1.01,
  min_ESS = 1000
)

if (FALSE) { # \dontrun{
# Use in RoBMA function
fit <- RoBMA(d = c(0.5, 0.3, 0.1), 
             se = c(0.2, 0.15, 0.1),
             autofit_control = autofit_ctrl,
             convergence_checks = conv_checks)
} # }