JointAI (version 1.0.2)

add_samples: 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", "gui", and "none" (see update). Note: when sampling is performed in parallel it is currently 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

# NOT RUN {
# 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)

# }