Summarise a study design output

Print a structured summary of a movedesign_output object produced by md_replicate() or md_stack(). The summary reports the study design, replication settings, estimation performance for each target metric, and a convergence assessment.

This method runs automatically when calling summary(output) on a movedesign_output object.

Usage

# S3 method for class 'movedesign_output'
summary(
  object,
  verbose = FALSE,
  m = NULL,
  ci = 0.95,
  tol = 0.05,
  n_converge = 9,
  plot = TRUE,
  pal = c("#007d80", "#A12C3B"),
  ...
)

Arguments

object

A movedesign_output object returned by md_replicate() or md_stack().

verbose

Logical. If TRUE, run md_check() and print the full convergence diagnostics. This can also display a convergence plot when plot = TRUE. If FALSE (default), only the convergence status is printed.

m

Numeric (Optional). If provided, restricts the results to a specific population sample size (m). Defaults to NULL, which checks up to the maximum population sample size.

ci

Confidence level for the intervals. Applied to both the narrow confidence bars and wide prediction bands. Must be between 0 and 1. Default: 0.95 (95%).

tol

Numeric. The tolerance threshold for absolute change in the cumulative mean to declare convergence. Defaults to 0.05.

n_converge

Integer. Number of consecutive steps within tolerance required to confirm convergence.

plot

Logical. If TRUE (default), generates a plot of stepwise changes in the cumulative mean, highlighting when convergence is achieved.

pal

Character vector of color(s) of the plot, such as c("#007d80", "#A12C3B")) (default).

...

Additional arguments

See also

md_replicate(), md_stack() to generate results. md_check() to inspect convergence directly. md_compare() to compare designs after convergence.

Examples

if(interactive()) {

data(buffalo)

input <- md_prepare(
  species = "African buffalo",
  data = buffalo,
  n_individuals = 5,
  dur = list(value = 1, unit = "month"),
  dti = list(value = 1, unit = "day"),
  set_target = "hr",
  which_meta = "mean")

output <- md_replicate(input, n_replicates = 20)

# Print standard summary:
summary(output)

# Run full convergence diagnostics:
summary(output, verbose = TRUE, tol = 0.05)

}