add_samples: Continue sampling from an object of class JointAI

In JointAI: Joint Analysis and Imputation of Incomplete Data

Continue sampling from an object of class JointAI

Description

This function continues the sampling from the MCMC chains of an existing object of class 'JointAI'.

Usage

add_samples(object, n.iter, add = TRUE, thin = NULL,
  monitor_params = NULL, progress.bar = "text", mess = TRUE)

Arguments

object	object inheriting from class 'JointAI'
n.iter	the number of additional iterations of the MCMC chain
add	logical; should the new MCMC samples be added to the existing samples (TRUE; default) or replace them? If samples are added the arguments monitor_params and thin are ignored.
thin	thinning interval (see window.mcmc); ignored when add = TRUE.
monitor_params	named list or vector specifying which parameters should be monitored. For details, see *_imp and the vignette Parameter Selection. Ignored when add = TRUE.
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.
mess	logical; should messages be given? Default is TRUE.

See Also

*_imp

The vignette Parameter Selection contains some examples on how to specify the argument monitor_params.

Examples

# Example 1:
# Run an initial JointAI model:
mod <- lm_imp(y ~ C1 + C2, data = wideDF, n.iter = 100)

# Continue sampling:
mod_add <- add_samples(mod, n.iter = 200, add = TRUE)


# Example 2:
# Continue sampling, but additionally sample imputed values.
# Note: Setting different parameters to monitor than in the original model
# requires add = FALSE.
imps <- add_samples(mod, n.iter = 200, monitor_params = c("imps" = TRUE),
                    add = FALSE)

JointAI documentation built on April 27, 2023, 5:15 p.m.