Preview plot for movedesign workflow outputs (single replicate)

Generates a quick visualization of relative error for home range or movement speed estimation from a single replicate of a movedesign workflow. The plot can display either the estimates from that replicate for a random combination of individuals, or, when resampling is enabled, summaries derived from repeated draws of individuals at each population sample size (based on the specified number of resamples).

This functions shows preliminary outputs only based on the output of md_run() (a movedesign_preprocess object) and should not be used to evaluate study design by itself. Instead, users should run md_replicate() and check for convergence with md_check().

Usage

md_plot_preview(
  obj,
  n_resamples = NULL,
  error_threshold = 0.05,
  pal = c("#007d80", "#A12C3B")
)

Arguments

obj: An object of class movedesign_preprocess (output of md_run()).
n_resamples: Numeric. Must be a positive value. Defines how many combinations are generated for each population sample size, with each combination producing a new population-level estimate.
error_threshold: Numeric. Error threshold (e.g. 0.05 for 5%) to display as a reference in the plot.
pal: Character vector of two colors for within/outside threshold (default: c("#007d80", "#A12C3B")).

Value

A ggplot object displaying relative error by population sample size, with point estimate and confidence intervals for mean estimates, and horizontal error threshold lines.

Details

This plot summarizes a single replicate. Credible intervals and robust study design conclusions generally require multiple replicates generated with md_replicate().

Examples

if (interactive()) {
  input <- md_prepare(
    data = buffalo,
    models = models,
    species = "buffalo",
    n_individuals = 5,
    dur = list(value = 1, unit = "month"),
    dti = list(value = 1, unit = "day"),
    add_individual_variation = TRUE,
    grouped = TRUE,
    set_target = "hr",
    which_meta = "mean"
  )

  output <- md_run(input)
  md_plot_preview(output, error_threshold = 0.05)
}