Validate a sample-size plan for a Gaussian graphical model

Checks a design plan by simulating studies at the recommended n and reporting how often the criterion actually reaches its target threshold. Usually, the planning search uses fewer replications than this check, so the two probabilities will not match exactly.

Usage

# S3 method for class 'ggm_design'
validate(
  plan,
  H = 500L,
  J = 100L,
  which_n = NULL,
  scope = NULL,
  seed = NULL,
  ...
)

Arguments

plan

A ggm_design object, as returned by design. It also inherits from bgm_design.

H, J

Outer and inner Monte Carlo replication counts, as in design. Defaults 500 and 100, larger than the planning defaults, since the check is run at a single n.

which_n

Which of the plan's recommended sizes to validate at. NULL (default) picks "global" for a DPIR plan and "h1" for a BFDA plan. DPIR plans also accept "pw", the parameterwise size; BFDA plans also accept "h0"; BSDA plans have only one size.

scope

BFDA only: whether to check the edge the plan was built around ("planning_edge") or every present edge in the graph ("all_edges"), the stricter guarantee check. NULL (default) means "planning_edge". This argument is ignored for DPIR plans.

seed

Random seed for reproducibility.

...

Ignored, present for consistency with the generic.

Value

A ggm_design_validation object, which also inherits from bgm_design_validation. Its n_star component is the size that was checked and results holds the probability achieved there. print and summary methods are available.

See also

Other sample size planning: bsda_control(), design(), design.ggm_elicited(), validate()