Plot a design-simulation study

Usage

# S3 method for class 'mfrm_design_evaluation'
plot(
  x,
  facet = c("Rater", "Criterion", "Person"),
  metric = c("separation", "reliability", "infit", "outfit", "misfitrate",
    "severityrmse", "severitybias", "convergencerate", "elapsedsec", "mincategorycount",
    "designdensity", "plannedmissingrate", "linkpersons", "linkfraction", "linkraters",
    "mincommonpersons", "zerocommonpairs", "pairsshorttarget"),
  x_var = c("n_person", "n_rater", "n_criterion", "raters_per_person"),
  group_var = NULL,
  draw = TRUE,
  ...
)

Arguments

x: Output from evaluate_mfrm_design().
facet: Facet to visualize.
metric: Metric to plot.
x_var: Design variable used on the x-axis. When x was generated from a sim_spec with custom public facet names, the corresponding aliases (for example n_judge, n_task, judge_per_person) are also accepted. Role keywords (person, rater, criterion, assignment) are accepted as an abstraction over the current two-facet schema.
group_var: Optional design variable used for separate lines. The same alias rules as x_var apply.
draw: If TRUE, draw with base graphics; otherwise return plotting data.
...: Reserved for generic compatibility.

Value

If draw = TRUE, invisibly returns a plotting-data list. If draw = FALSE, returns that list directly. The returned list includes resolved canonical variables (x_var, group_var) together with public labels (x_label, group_label), design_variable_aliases, and design_descriptor, plus planning_scope, planning_constraints, and planning_schema.

Details

This method is designed for quick design-planning scans rather than polished publication graphics.

Useful first plots are:

rater metric = "separation" against x_var = "n_person"
criterion metric = "severityrmse" against x_var = "n_person" when you want aligned recovery error rather than raw location shifts
rater metric = "convergencerate" against x_var = "raters_per_person"
sparse linked metric = "plannedmissingrate", "mincommonpersons", "zerocommonpairs", or "pairsshorttarget" to review planned missingness and rater-pair linkage separately from recovery metrics

Examples

if (FALSE) { # \dontrun{
sim_eval <- suppressWarnings(evaluate_mfrm_design(
  n_person = c(8, 12),
  n_rater = 2,
  n_criterion = 2,
  raters_per_person = 1,
  reps = 1,
  maxit = 30,
  seed = 123
))
p <- plot(sim_eval, facet = "Rater", metric = "separation", x_var = "n_person", draw = FALSE)
c(p$facet, p$x_var)
} # }