Compare D-study confidence interval methods

Compare future plans on one shared coefficient or SEM axis. Each plan has adjacent method rows, distinguished by color, point shape and line type. Method agreement in a single dataset is not evidence of coverage.

Usage

# S3 method for class 'mfrm_dstudy_intervals'
plot(
  x,
  draw = TRUE,
  preset = "standard",
  ncol = 2L,
  layout = c("combined", "panels"),
  ...
)

Arguments

x

A result from confint.mfrm_multivariate_d_study().

draw

Logical; FALSE returns the plot data without opening a device.

preset

Package plot style, including "standard" (color) and "monochrome". Methods also have distinct point shapes.

ncol

Positive integer number of columns for layout = "panels". Unused by the default combined view.

layout

"combined" (default) groups methods within each plan on one axis, with a common method legend. "panels" retains one panel per future plan, with at most ncol columns.

...

Reserved; additional arguments are rejected.

Value

Invisibly, a ggplot object; plot_data() retains the exact table, unavailable rows, source intervals, design_grid, weights and notes. as_ggplot() remains idempotent on the returned object. No title or explanatory footnote is drawn.

Details

All methods concern the same score/composite, metric and source data. A filled point has an interval; an open point is an available estimate without an interval. Unavailable intervals are explicitly labeled in the combined view, or marked by an asterisk in the panel view, even if their point estimates are missing. An interval can exist without an admissible ANOVA/MINQUE point estimate. Missing values remain missing; no substitute interval or zero is drawn. The shared axis includes [0,1] for coefficients and [-1,1] for G/Phi differences, expanding to retain all finite estimates and accepted bounds. Delta normal-reference bounds outside [0,1] remain visible; they flag limitations of that approximation, not a larger population parameter space. SEM and SEM-difference axes instead span the estimates and intervals in score units, including zero. Negative SEM differences favor the comparison plan. An SEM interval is uncertainty in measurement error, not an interval for a Person's score. Chi-square and MLS labels retain the calculation used for each plan; their assumptions and coverage claims differ. Method limitations, confidence level, normal-effect assumptions and negative components remain in the returned notes and source object. Plotting neither refits a model nor recalculates an interval. Save the complete interval or plot object with saveRDS() for later use. Nested plan labels show child counts per parent, rather than total child pool sizes; the reference plan uses the same count convention. A mean-square-region difference is a conservative enclosure from shared source uncertainty, not a tight joint image or a paired-delta interval. Its returned source retains the individual projections and reference. REML profile and residual-contrast rows retain the original source points; the separate IntervalCenter and method diagnostics are available in the source interval object. A point can lie outside this separately calculated interval. For GENOVA-adjusted results, points use the selected component treatment; intervals retain their original-source population basis. IntervalEstimate and IntervalBasis in the source table distinguish them. Drawing the two together does not make the original sampling SE an adjusted-estimator SE. Unresolved numerical candidates are retained in the source object but are not drawn as accepted confidence bounds. For score and MLS intervals, an empty confidence set is labeled "Empty set"; it is distinct from an estimated coefficient of zero. MLS rows with FormulaComplete = FALSE retain candidate values where a squared width is undefined. Their conservative bounds are drawn, while the saved interval object and notes retain the reason and affected ranges.

Session plot defaults

See mfrmr_visual_diagnostics for options(mfrmr.plot_preset), argument precedence, and appearance when saving or reusing a plot.

See also

confint.mfrm_multivariate_d_study()

Examples

# Supplementary workflow: run explicitly to keep routine checks brief.
# example("plot.mfrm_dstudy_intervals", run.dontrun = TRUE)
if (FALSE) { # \dontrun{
tasks <- read.csv(system.file("extdata", "mgenova-table12.csv", package = "mfrmr"))
g <- mfrm_g_study(tasks, c("V", "W"), rater = NULL)
d <- mfrm_d_study(g, data.frame(Tasks = c(6, 12)))
ci <- confint(d, assumption = "normal")
p <- plot(ci, draw = FALSE)
plot_data(p, "notes")
if (requireNamespace("ggplot2", quietly = TRUE)) as_ggplot(p)
} # }