Control parameters for tinyVAST
tinyVASTcontrol(
nlminb_loops = 1,
newton_loops = 0,
eval.max = 1000,
iter.max = 1000,
getsd = TRUE,
silent = getOption("tinyVAST.silent", TRUE),
trace = getOption("tinyVAST.trace", 0),
verbose = getOption("tinyVAST.verbose", FALSE),
profile = c(),
tmb_par = NULL,
tmb_map = NULL,
gmrf_parameterization = c("separable", "projection"),
reml = FALSE,
getJointPrecision = FALSE,
calculate_deviance_explained = TRUE,
run_model = TRUE,
suppress_nlminb_warnings = TRUE
)An object (list) of class tinyVASTcontrol, containing either default or
updated values supplied by the user for model settings
Integer number of times to call stats::nlminb().
Integer number of Newton steps to do after running
stats::nlminb().
Maximum number of evaluations of the objective function
allowed. Passed to control in stats::nlminb().
Maximum number of iterations allowed. Passed to control in
stats::nlminb().
Boolean indicating whether to call TMB::sdreport()
Disable terminal output for inner optimizer?
Parameter values are printed every trace iteration
for the outer optimizer. Passed to
control in stats::nlminb().
Output additional messages about model steps during fitting?
Parameters to profile out of the likelihood (this subset will be appended to random with Laplace approximation disabled).
list of parameters for starting values, with shape identical
to tinyVAST(...)$internal$parlist
input passed to TMB::MakeADFun as argument map, over-writing
the version tinyVAST(...)$tmb_inputs$tmb_map and allowing detailed control
over estimated parameters (advanced feature)
Parameterization to use for the Gaussian Markov
random field, where the default separable constructs a full-rank and
separable precision matrix, and the alternative projection constructs
a full-rank and IID precision for variables over time, and then projects
this using the inverse-cholesky of the precision, where this projection
allows for rank-deficient covariance.
Logical: use REML (restricted maximum likelihood) estimation rather than maximum likelihood? Internally, this adds the fixed effects to the list of random effects to integrate over.
whether to get the joint precision matrix. Passed
to sdreport.
whether to calculate proportion of deviance
explained. See deviance_explained()
whether to run the model of export TMB objects prior to compilation (useful for debugging)
whether to suppress uniformative warnings
from nlminb arising when a function evaluation is NA, which
are then replaced with Inf and avoided during estimation