Detailed control for dsem structure — dsem

Define a list of control parameters. Note that the format of this input is likely to change more rapidly than that of dsem

Usage

dsem_control(
  nlminb_loops = 1,
  newton_loops = 1,
  trace = 0,
  eval.max = 1000,
  iter.max = 1000,
  getsd = TRUE,
  quiet = FALSE,
  run_model = TRUE,
  gmrf_parameterization = c("separable", "projection"),
  constant_variance = c("conditional", "marginal", "diagonal"),
  use_REML = TRUE,
  profile = NULL,
  parameters = NULL,
  map = NULL,
  getJointPrecision = FALSE,
  extra_convergence_checks = TRUE,
  lower = -Inf,
  upper = Inf,
  suppress_nlminb_warnings = TRUE
)

Arguments

nlminb_loops: Integer number of times to call nlminb.
newton_loops: Integer number of Newton steps to do after running nlminb.
trace: Parameter values are printed every trace iteration for the outer optimizer. Passed to control in nlminb.
eval.max: Maximum number of evaluations of the objective function allowed. Passed to control in nlminb.
iter.max: Maximum number of iterations allowed. Passed to control in nlminb.
getsd: Boolean indicating whether to call sdreport
quiet: Boolean indicating whether to run model printing messages to terminal or not;
run_model: Boolean indicating whether to estimate parameters (the default), or instead to return the model inputs and compiled TMB object without running;
gmrf_parameterization: Parameterization to use for the Gaussian Markov random field, where the default separable constructs a precision matrix that must be full rank, 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 can be rank-deficient.
constant_variance: Whether to specify a constant conditional variance \( \mathbf{\Gamma \Gamma}^t\) using the default constant_variance="conditional", which results in a changing marginal variance along the specified causal graph when lagged paths are present. Alternatively, the user can specify a constant marginal variance using constant_variance="diagonal" or constant_variance="marginal", such that \( \mathbf{\Gamma}\) and \(\mathbf{I-P}\) are rescaled to achieve this constraint. All options are equivalent when the model includes no lags (only simultaneous effects) and no covariances (no two-headed arrows). "diagonal" and "marginal" are equivalent when the model includes no covariances. Given some exogenous covariance, constant_variance = "diagonal" preserves the conditional correlation and has changing conditional variance, while constant_variance = "marginal" has changing conditional correlation along the causal graph.
use_REML: Boolean indicating whether to treat non-variance fixed effects as random, either to motigate bias in estimated variance parameters or improve efficiency for parameter estimation given correlated fixed and random effects
profile: Parameters to profile out of the likelihood (this subset will be appended to random with Laplace approximation disabled).
parameters: list of fixed and random effects, e.g., as constructed by dsem and then modified by hand (only helpful for advanced users to change starting values or restart at intended values)
map: list of fixed and mirrored parameters, constructed by dsem by default but available to override this default and then pass to MakeADFun
getJointPrecision: whether to get the joint precision matrix. Passed to sdreport.
extra_convergence_checks: Boolean indicating whether to run extra checks on model convergence.
lower: vectors of lower bounds, replicated to be as long as start and passed to nlminb. If unspecified, all parameters are assumed to be unconstrained.
upper: vectors of upper bounds, replicated to be as long as start and passed to nlminb. If unspecified, all parameters are assumed to be unconstrained.
suppress_nlminb_warnings: whether to suppress uniformative warnings from nlminb arising when a function evaluation is NA, which are then replaced with Inf and avoided during estimation

Value

An S3 object of class "dsem_control" that specifies detailed model settings, allowing user specification while also specifying default values