tinyVASTcontrol | R Documentation |
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,
suppress_user_warnings = FALSE,
get_rsr = FALSE,
extra_reporting = FALSE,
use_anisotropy = FALSE,
sar_adjacency = "queen",
barrier_stiffness = 0.01
)
nlminb_loops |
Integer number of times to call |
newton_loops |
Integer number of Newton steps to do after running
|
eval.max |
Maximum number of evaluations of the objective function
allowed. Passed to |
iter.max |
Maximum number of iterations allowed. Passed to |
getsd |
Boolean indicating whether to call |
silent |
Disable terminal output for inner optimizer? |
trace |
Parameter values are printed every |
verbose |
Output additional messages about model steps during fitting? |
profile |
Character-vector passed to TMB::MakeADFun and see description
there. Fixed effects that are highly correlated with random effects
can often be estimated faster (i.e., with fewer iterations) by adding
them to |
tmb_par |
list of parameters for starting values, with shape identical
to |
tmb_map |
input passed to TMB::MakeADFun as argument |
gmrf_parameterization |
Parameterization to use for the Gaussian Markov
random field, where the default |
reml |
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. |
getJointPrecision |
whether to get the joint precision matrix. Passed
to |
calculate_deviance_explained |
whether to calculate proportion of deviance
explained. See |
run_model |
whether to run the model of export TMB objects prior to compilation (useful for debugging) |
suppress_nlminb_warnings |
whether to suppress uniformative warnings
from |
suppress_user_warnings |
whether to suppress warnings from package author regarding dangerous or non-standard options |
get_rsr |
Experimental option, whether to report restricted spatial regression (RSR) adjusted estimator for covariate responses |
extra_reporting |
Whether to report a much larger set of quantities via
|
use_anisotropy |
Whether to estimate two parameters representing geometric anisotropy |
sar_adjacency |
Whether to use queen or rook adjacency when defining a Simultaneous Autoregressive spatial precision from a sfc_GEOMETRY (default is queen) |
barrier_stiffness |
The ratio of local stiffness (the scale of diffusion
rate and resulting decorrelation distance) for barriers relative to normal areas
in the SPDE method when using |
An object (list) of class tinyVASTcontrol
, containing either default or
updated values supplied by the user for model settings
Bakka, H., Vanhatalo, J., Illian, J., Simpson, D., Rue, H. (2019). Non-stationary Gaussian models with physical barriers. Spatial Statistics, 29, 268-288. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.spasta.2019.01.002")}
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