Bubble chart of measure estimates and fit statistics

Produces a Rasch-convention bubble chart where each element is a circle positioned at its measure estimate (x) and fit mean-square (y). Bubble radius reflects approximate measurement precision or sample size.

Usage

plot_bubble(
  x,
  diagnostics = NULL,
  fit_stat = c("Infit", "Outfit"),
  view = c("measure", "infit_outfit"),
  bubble_size = NULL,
  facets = NULL,
  fit_range = c(0.5, 1.5),
  top_n = 60,
  main = NULL,
  palette = NULL,
  draw = TRUE,
  preset = c("standard", "publication", "compact", "monochrome")
)

Arguments

x

Output from fit_mfrm or diagnose_mfrm.

diagnostics

Optional output from diagnose_mfrm when x is an mfrm_fit object. If omitted, diagnostics are computed automatically.

fit_stat

Fit statistic for the y-axis: "Infit" (default) or "Outfit". Ignored when view = "infit_outfit" because that view always plots Infit on x and Outfit on y.

view

Layout. "measure" (default, the historical mfrmr layout) plots Measure (logit) on x and the chosen fit_stat MnSq on y. "infit_outfit" plots Infit MnSq on x and Outfit MnSq on y, matching the Winsteps Table 30.2 "Most-misfitting Persons / Items" scatter that many MFRM and Rasch users expect, and defaults bubble_size = "N".

bubble_size

Variable controlling bubble radius: "SE" (default for view = "measure"), "N" (observation count; default for view = "infit_outfit"), or "equal" (uniform size).

facets

Character vector of facets to include. NULL (default) includes all non-person facets.

fit_range

Numeric length-2 vector defining the heuristic fit-review band shown as a shaded region (default c(0.5, 1.5)).

top_n

Maximum number of elements to plot (default 60).

main

Optional custom plot title.

palette

Optional named colour vector keyed by facet name.

draw

If TRUE (default), render the plot using base graphics.

preset

Visual preset ("standard", "publication", "compact", or "monochrome").

Value

Invisibly, an object of class mfrm_plot_data.

Details

When x is an mfrm_fit object and diagnostics is omitted, the function computes diagnostics internally via diagnose_mfrm(). For repeated plotting in the same workflow, passing a precomputed diagnostics object avoids that extra work.

The x-axis shows element measure estimates on the logit scale (one logit = one unit change in log-odds of responding in a higher category). The y-axis shows the selected fit mean-square statistic. A shaded band between fit_range[1] and fit_range[2] highlights a common heuristic review range.

Bubble radius options:

• "SE": inversely proportional to standard error—larger circles indicate more precisely estimated elements under the current SE approximation.

• "N": proportional to observation count—larger circles indicate elements with more data.

• "equal": uniform size, useful when SE or N differences distract from the fit pattern.

Person estimates are excluded by default because they typically outnumber facet elements and obscure the display.

Interpreting the plot

Points near the horizontal reference line at 1.0 are closer to model expectation on the selected MnSq scale. Points above 1.5 suggest underfit relative to common review heuristics; these elements may have inconsistent scoring. Points below 0.5 suggest overfit relative to common review heuristics; these may indicate redundancy or restricted range. Points are colored by facet for easy identification.

Typical workflow

1. Fit a model with fit_mfrm().

2. Compute diagnostics once with diagnose_mfrm().

3. Call plot_bubble(fit, diagnostics = diag) to inspect the most extreme elements.

See also

diagnose_mfrm, plot_unexpected, plot_fair_average

Examples

if (FALSE) { # interactive()
toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score",
                method = "JML", model = "RSM", maxit = 30)
diag <- diagnose_mfrm(fit, residual_pca = "none")
p <- plot_bubble(fit, diagnostics = diag, draw = FALSE)
head(p$data$table[, c("Facet", "Level", "Estimate", "Infit", "Outfit")])
# Look for (default `view = "measure"`): bubbles inside the shaded
#   0.5-1.5 fit-review band. Bubbles above the band are underfit
#   (noisy elements); below the band are overfit (overly predictable).
#
# For the Winsteps Table 30 layout pass `view = "infit_outfit"`:
p_io <- plot_bubble(fit, diagnostics = diag, view = "infit_outfit",
                     draw = FALSE)
p_io$data$view
# Look for: bubbles clustered inside the central [0.5, 1.5] x [0.5, 1.5]
#   square. Points outside the upper-right corner have both Infit
#   AND Outfit > 1.5 (consistent underfit); points outside the
#   lower-left have both < 0.5 (consistent overfit). Bubble size in
#   this view defaults to N (observation count) so the visual
#   weighting matches how seriously the misfit should be taken.
}