Checks and lists settings for the JAGS_fit function.
JAGS_check_and_list_fit_settings(
chains,
adapt,
burnin,
sample,
thin,
autofit,
parallel,
cores,
silent,
seed,
check_mins = list(chains = 1, adapt = 50, burnin = 50, sample = 100, thin = 1),
call = ""
)
JAGS_check_and_list_autofit_settings(
autofit_control,
skip_sample_extend = FALSE,
call = ""
)number of chains to be run, defaults to 4
number of samples used for adapting the MCMC chains, defaults to 500
number of burnin iterations of the MCMC chains, defaults to 1000
number of sampling iterations of the MCMC chains, defaults to 4000
thinning interval for the MCMC samples, defaults to 1
whether the models should be refitted until convergence criteria
specified in autofit_control. Defaults to FALSE.
whether the chains should be run in parallel FALSE
number of cores used for multithreading if parallel = TRUE,
defaults to chains
whether the function should proceed silently, defaults to TRUE
seed for random number generation by JAGS_fit(): an
integer between -.Machine$integer.max and .Machine$integer.max.
Draws are reproducible for the same seed, chains, and R
random-number generator settings (RNGkind()), and, with
parallel = TRUE, the same cores. Parallel draws need not equal
serial draws with the same seed, because the backend passes each chain's
initial state to its worker in a serialized text form. Initial values are
drawn from the priors after set.seed(seed), and each chain's JAGS
random-number seed is drawn from the same seed with
sample.int(.Machine$integer.max, chains) (see
JAGS_get_inits). A chain's JAGS seed therefore does not
depend on the number of chains, but the JAGS generator does: with more
than four chains it changes from "base::Super-Duper" to
"lecuyer::RngStream", so the draws of a chain differ between fits
with up to four and with more chains. The chains of adjacent seeds (e.g.,
seed and seed + 1) do not share random-number streams.
Automatic restarts after a failed initialization (see restarts in
autofit_control) draw their seeds from a separate stream of the
same seed (the first L'Ecuyer-CMRG substream), so the i-th restart does
not repeat the fit with seed + i. A seeded fit restores the
caller's random-number state (.Random.seed and RNGkind())
when it returns. With seed = NULL, the initial values are drawn
from one seed taken from the caller's random-number stream (see
JAGS_get_inits), which the fit then continues to use.
named list of minimal values for which should some input be checked. Defaults to:
1
50
50
100
1
string to be placed as a prefix to the error call.
a list of arguments controlling the autofit function.
Convergence thresholds and max_time$time must be finite and non-missing.
Set a convergence threshold or max_time to NULL to disable that check.
Possible options are:
maximum R-hat error for the autofit function.
Defaults to 1.05. R-hat is the larger of the point estimate and
the upper confidence limit of the potential scale reduction factor,
whereas the summary tables show the point estimate (see
JAGS_check_convergence()).
minimum effective sample size. Defaults to 500.
maximum MCMC error. Defaults to 0.01.
maximum MCMC error as the proportion of standard
deviation of the parameters. Defaults to 0.05.
list with fields time and unit, specifying
when the automatic fitting function is stopped. The unit value
corresponds to the units argument of difftime.
number of times after which the automatic fitting function is stopped.
number of samples between each convergence check. Defaults to
1000.
maximum number of fitting attempts when the model fails
to initialize; every failed attempt but the last is retried with new
initial values (restarts = 1 fits once, without a retry). A
failed attempt that is retried is reported as a warning of the fit.
Defaults to 10.
whether model indicator variables should be included
in convergence checks, including when monitor selects a narrower
parameter set. Auxiliary inclusion-probability coordinates remain excluded
unless requested explicitly. Defaults to FALSE.
optional character vector selecting parameters for
convergence checks. Base names select all indexed elements. Requests are
resolved against the monitored nodes that the summary tables show,
including the derived and auxiliary ones that the default selection
excludes. Base names that are not
monitored are rejected before sampling (error class
BayesTools_parameter_not_found); an indexed element such as
"mu[2]" is resolved against the fitted columns after the first
run and fails then when it does not exist. Defaults to NULL, which
checks the default selection of JAGS_check_convergence(): the sampled
parameters of the priors, add_parameters that depend on a
stochastic node, and the stochastic nodes that formulas monitor (such as
latent random effects and LKJ primitives). Deterministic nodes that
formulas monitor (such as random-effect correlation matrices and their
Cholesky factors) are derived and not checked by default. The role of
every fitted coordinate is stored in the convergence_role column
of parameter_coordinates().
whether undefined diagnostics for requested
sampled parameters may be ignored. Defaults to FALSE.
whether sample_extend
is allowed to be NULL and skipped in the check
JAGS_check_and_list_fit_settings invisibly returns a
list of checked fit settings. JAGS_check_and_list_autofit_settings
invisibly returns a list of checked autofit settings.