Rater-severity trajectory across an ordered wave / occasion variable

Plots each rater's severity estimate across a user-supplied ordering variable (e.g. Session, Wave, AdminDate), producing one line per rater. When the ordering column is time-like (numeric or date), the x-axis is drawn on that scale; otherwise the values are rendered as discrete ordered categories. Useful for rater training / drift feedback loops.

Usage

plot_rater_trajectory(
  fits,
  facet = "Rater",
  ci_level = 0.95,
  preset = c("standard", "publication", "compact", "monochrome"),
  draw = TRUE
)

Arguments

fits: A named list of mfrm_fit objects, one per wave. Names become the x-axis labels in their supplied order. Fits are assumed to have been placed on a common scale via anchor-linking or an equivalent post-hoc transformation (see the caveat above).
facet: Facet whose levels are tracked (default "Rater").
ci_level: Confidence level for the per-wave CI ribbons drawn around each trajectory (default 0.95).
preset: Visual preset.
draw: If TRUE, draw with base graphics.

Value

An mfrm_plot_data object whose data slot is a long data.frame with Wave, Level, Estimate, SE, CI_Lower, CI_Upper columns.

Anchor-linking caveat

Each wave is fit independently under its own sum-to-zero identification, so the per-wave severity logits live on separate scales unless you actively link them. Before interpreting movement across waves as rater drift, link the waves by either (i) holding common anchors fixed across fits (see mfrmr_linking_and_dff for the supported linking route), or (ii) harmonizing the scale post-hoc with a Stocking-Lord type transformation and reviewing the result via plot_anchor_drift(). The trajectory plot itself does not perform linking; it only visualizes the supplied fits on their as-fit scales.

Examples

if (FALSE) { # \dontrun{
toy <- load_mfrmr_data("example_core")
fit_a <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score",
                  method = "JML", maxit = 30)
fit_b <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score",
                  method = "JML", maxit = 30)
p <- plot_rater_trajectory(list(T1 = fit_a, T2 = fit_b), draw = FALSE)
head(p$data$data)
# Look for: stable trajectories (small wave-to-wave shifts within
#   each rater's CI ribbon) once the waves are anchor-linked. A
#   rater whose line drifts >0.5 logits across waves is the typical
#   "calibration drift" signal. Without anchor linking the per-wave
#   logits are on different scales and the picture cannot be read
#   as drift; see the Anchor-linking caveat in the docstring.
} # }