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Create list of control options (to pass to emuFit())

Source: R/control_fn.R
control_fn.Rd

Create list of control options (to pass to emuFit())

Usage

control_fn(
  control = list(),
  max_step = 1,
  ignore_stop = FALSE,
  use_fullmodel_info = FALSE,
  use_fullmodel_cov = FALSE,
  use_both_cov = FALSE,
  inner_maxit = 25,
  inner_tol = 1,
  c1 = 1e-04,
  trackB = FALSE,
  return_nullB = FALSE,
  return_score_components = FALSE,
  return_both_score_pvals = FALSE,
  B_null_tol = 0.001,
  rho_init = 1,
  tau = 2,
  kappa = 0.8,
  constraint_tol = 1e-05,
  ntries = 4
)

Arguments

control

Current control list (optional), will augment it with missing arguments

max_step

Maximum stepsize; update directions computed during estimation (under the alternative). Will be rescaled if a step in any parameter exceeds this value. Defaults to 1.

ignore_stop

whether to ignore stopping criteria and run maxit iterations (helpful for diagnostic plots to determine convergence).

use_fullmodel_info

Used in estimation under the null hypothesis. Whether to use information matrix from estimation under the alternative hypothesis to construct the robust score statistic (instead of information matrix from estimation under the null hypothesis). Defaults to FALSE.

use_fullmodel_cov

Used in estimation under the null hypothesis. Whether to use covariance matrix from estimation under the alternative hypothesis to construct the robust score statistic (instead of covariance matrix from estimation under the null hypothesis). Defaults to FALSE.

use_both_cov

Used in estimation under the null hypothesis. Whether to do score test twice, once with covariance matrix under the alternative hypothesis and once with covariance matrix under the null hypothesis. Defaults to FALSE.

inner_maxit

Used in estimation under the null hypothesis. Maximum number of iterations within each inner loop of estimation under null hypothesis algorithm. Default is 25.

inner_tol

Used in estimation under the null hypothesis. Convergence tolerance within each inner loop of estimation under null hypothesis algorithm. Default is 1.

c1

Used in estimation under the null hypothesis. Parameter for Armijo line search. Default is 1e-4.

trackB

Used in estimation under the null hypothesis. When TRUE will track the value of B in each iteration of optimization algorithm. Defaults to FALSE.

return_nullB

Used in estimation under the null hypothesis. When TRUE will return the final value of B under each null hypothesis tested. Defaults to FALSE.

return_score_components

Used in estimation under the null hypothesis. When TRUE will return the components of the robust score test statistic for each null hypothesis tested. Defaults to FALSE.

return_both_score_pvals

Used in estimation under the null hypothesis, with use_both_cov. Defaults to FALSE.

B_null_tol

Used in estimation under the null hypothesis, for the augmented Lagrangian algorithm. numeric: convergence tolerance for null model fits for score testing (if max of absolute difference in B across outer iterations is below this threshold, we declare convergence). Default is 0.001.

rho_init

Used in estimation under the null hypothesis, for the augmented Lagrangian algorithm. Value at which to initiate rho parameter in augmented Lagrangian algorithm. Default is 1.

tau

Used in estimation under the null hypothesis, for the augmented Lagrangian algorithm. Value to scale rho by in each iteration of augmented Lagrangian algorithm that does not move estimate toward zero sufficiently. Default is 2.

kappa

Used in estimation under the null hypothesis, for the augmented Lagrangian algorithm. Value between 0 and 1 that determines the cutoff on the ratio of current distance from feasibility over distance in last iteration triggering scaling of rho. If this ratio is above kappa, rho is scaled by tau to encourage estimate to move toward feasibility.

constraint_tol

Used in estimation under the null hypothesis, for the augmented Lagrangian algorithm. Constraint tolerance for fits under null hypotheses (tested element of B must be equal to constraint function to within this tolerance for a fit to be accepted as a solution to constrained optimization problem). Default is 1e-5.

ntries

Used in estimation under the null hypothesis, for the augmented Lagrangian algorithm. The number of times to try optimization. Successive tries will change tau and inner_maxit and retry.

Value

A list containing control options, to have more control over optimization algorithms used by radEmu. This can be passed into emuFit().