dsem_control: Detailed control for dsem structure
In dsem: Fit Dynamic Structural Equation Models

dsem_control

R Documentation

Detailed control for dsem structure

Description

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
)

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 = "marginal"` 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.