plot_information: Plot design-weighted precision curves

In mfrmr: Estimation and Diagnostics for Many-Facet Measurement Models

Plot design-weighted precision curves

Description

Visualize the design-weighted precision curve and optionally per-facet-level contribution curves from compute_information().

Usage

plot_information(
  x,
  type = c("tif", "iif", "se", "sem", "csem", "both"),
  facet = NULL,
  draw = TRUE,
  ...
)

Arguments

x	Output from compute_information().
type	"tif" for the overall precision curve (default), "iif" for facet-level contribution curves, "se" / "sem" / "csem" for the conditional standard error of measurement implied by that curve, or "both" for precision with conditional SEM on a secondary axis.
facet	For type = "iif", which facet to plot. If NULL, the first facet is used.
draw	If TRUE (default), draw the plot. If FALSE, return reusable mfrm_plot_data invisibly.
...	Additional graphical parameters.

Value

Invisibly, an mfrm_plot_data object.

Plot types

• "tif": overall design-weighted precision across theta.

• "se" / "sem" / "csem": conditional SEM across theta.

• "both": precision and conditional SEM together, useful for presentations.

• "iif": facet-level contribution curves for one selected facet in a supported RSM, PCM, or bounded GPCM fit.

Which type should I use?

• Use "tif" for a quick overall read on precision.

• Use "sem" or "csem" when standard-error language is easier to communicate than precision.

• Use "both" when you want both views in one figure.

• Use "iif" when you want to see which facet levels are shaping the total precision curve.

Interpreting output

• The total curve peaks where the realized design is most precise.

• Conditional SEM is derived as 1 / sqrt(precision); lower is better.

• Facet-level curves show which facet levels contribute most to that realized precision at each theta.

• For bounded GPCM, those contributions include the squared discrimination scaling implied by the fitted slope_facet.

• If the precision peak sits far from the bulk of person measures, the realized design may be poorly targeted.

Returned data when draw = FALSE

draw = FALSE returns an mfrm_plot_data object. The underlying plotting data are stored in ⁠$data$plot⁠. For type = "tif", "se", or "both", those rows come from x$tif. For type = "iif", the returned rows come from x$iif filtered to the requested facet. The plot data also include plot_long, information_long, conditional_sem, summary, and settings so ggplot2, plotly, Quarto, and table workflows can reuse the information and conditional-SEM series without parsing the drawn figure.

Typical workflow

1. Compute information with compute_information().

2. Plot with plot_information(info) for the total precision curve.

3. Use plot_information(info, type = "iif", facet = "Rater") for facet-level contributions.

4. Use draw = FALSE when you want reusable plot data for custom graphics or reporting helpers.

See Also

compute_information(), fit_mfrm()

Examples

toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score",
                 method = "JML", model = "RSM", maxit = 30)
info <- compute_information(fit)
tif_data <- plot_information(info, type = "tif", draw = FALSE)
head(tif_data$data$plot)
iif_data <- plot_information(info, type = "iif", facet = "Rater", draw = FALSE)
head(iif_data$data$plot)

mfrmr documentation built on June 13, 2026, 1:07 a.m.