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Plot a data-description object

Source: R/api-estimation.R
plot.mfrm_data_description.Rd

Plot a data-description object

Usage

# S3 method for class 'mfrm_data_description'
plot(
  x,
  y = NULL,
  type = c("score_distribution", "facet_levels", "missing"),
  main = NULL,
  palette = NULL,
  label_angle = 45,
  draw = TRUE,
  ...
)

Arguments

x

Output from describe_mfrm_data().

y

Reserved for generic compatibility.

type

Plot type: "score_distribution", "facet_levels", or "missing".

main

Optional title override.

palette

Optional named colors (score, facet, missing).

label_angle

X-axis label angle for bar plots.

draw

If TRUE, draw using base graphics.

...

Reserved for generic compatibility.

Value

A plotting-data object of class mfrm_plot_data.

Details

This method draws quick pre-fit quality views from describe_mfrm_data():

  • score distribution balance

  • facet-level structure size

  • missingness by selected columns

Interpreting output

  • "score_distribution": bar chart of weighted observation counts per score category. Y-axis is WeightedN (sum of weights for each category). Categories with very few observations (< 10) may produce unstable threshold estimates. A roughly uniform or unimodal distribution is ideal; heavy floor/ceiling effects compress the measurement range.

  • "facet_levels": bar chart showing the number of distinct levels per facet. Useful for verifying that the design structure matches expectations (e.g., expected number of raters or criteria). Very large numbers of levels increase computation time and may require higher maxit in fit_mfrm().

  • "missing": bar chart of missing-value counts per input column. Columns with non-zero counts should be investigated before fitting—rows with missing scores, persons, or facet IDs are dropped during estimation.

Typical workflow

  1. Run describe_mfrm_data() before fitting.

  2. Inspect summary(ds) and plot(ds, type = "missing").

  3. Check category/facet balance with other plot types.

  4. Fit model after resolving obvious data issues.

See also

describe_mfrm_data(), plot()

Examples

toy <- load_mfrmr_data("example_core")
ds <- describe_mfrm_data(toy, "Person", c("Rater", "Criterion"), "Score")
p <- plot(ds, draw = FALSE)