Build an APA-oriented table handoff using base R structures

Build an APA-oriented table handoff using base R structures

Usage

apa_table(
  x,
  which = NULL,
  diagnostics = NULL,
  digits = 2,
  caption = NULL,
  note = NULL,
  bias_results = NULL,
  context = list(),
  whexact = FALSE,
  branch = c("apa", "facets")
)

Arguments

x

A data.frame, mfrm_fit, summary() output supported by build_summary_table_bundle(), an mfrm_summary_table_bundle, diagnostics list, bias-result list, saved RSM/PCM fixed-facet intervals, or saved GPCM slope/curve/bootstrap inference.

which

Optional table selector when x has multiple tables.

diagnostics

Optional diagnostics from diagnose_mfrm() (used when x is mfrm_fit and which targets diagnostics tables).

digits

Uniform number of rounding digits for numeric columns.

caption

Optional caption text.

note

Optional note text.

bias_results

Optional output from estimate_bias() used when auto-generating APA metadata for fit-based tables.

context

Optional context list forwarded when auto-generating APA metadata for fit-based tables.

whexact

Logical forwarded to APA metadata helpers.

branch

Output branch: "apa" for manuscript-oriented labels, "facets" for FACETS-aligned labels.

Value

A list of class apa_table with fields:

• table (data.frame)

• which

• caption

• note

• digits

• branch, style

Details

This helper avoids styling dependencies and returns a reproducible base data.frame plus manuscript-oriented metadata. It does not claim complete APA 7 or JARS compliance: digits applies the same rounding rule to every numeric column, so statistic-specific formatting (for example, exact p-value, confidence-interval, and effect-size conventions) and the target journal's final typography still require human review.

Supported which values:

• For mfrm_fit: "summary", "person", "facets", "steps"

• For summary() outputs or mfrm_summary_table_bundle: names listed in build_summary_table_bundle(x)$table_index

• For diagnostics list: "overall_fit", "measures", "fit", "reliability", "facets_chisq", "bias", "interactions", "interrater_summary", "interrater_pairs", "obs"

• For bias-result list: "table", "summary", "chi_sq"

• For RSM/PCM fixed-facet intervals: "intervals" (default), "settings", "contrasts", and "clusters" when present. Method, confidence level and unavailable reasons remain with the selected estimates and bounds.

• For GPCM inference: "intervals" or "curves"; bootstrap results also retain "trials", "checks" and "source_checks" when recorded, "sampling", and "availability" for slope intervals or "test" for a null-model LRT. Extended results also expose "settings", and "clusters"/"contrasts" when present. Saved profile intervals also expose "profile", "profile_endpoints", "profile_checks" and "wald". Target and method columns are preserved.

Interpreting output

• table: plain data.frame ready for export or further formatting.

• which: source component that produced the table.

• caption/note: manuscript-oriented metadata stored with the table.

Typical workflow

1. Build table object with apa_table(...).

2. Inspect quickly with summary(tbl).

3. Render base preview via plot(tbl, ...) or export tbl$table.

See also

fit_mfrm(), diagnose_mfrm(), build_apa_outputs(), reporting_checklist(), mfrmr_reporting_and_apa

Examples

# \donttest{
# Load the package and example ratings
library(mfrmr)
toy <- load_mfrmr_data("example_operational")

# Fit the model
fit <- fit_mfrm(
  data = toy,
  person = "Person",
  facets = c("Rater", "Criterion"),
  score = "Score",
  method = "MML",
  model = "RSM"
)

# Turn one summary table into a table with a caption and note
results <- summary(fit)
tbl <- apa_table(results, which = "facet_overview",
                 caption = "Distribution of estimates within each facet")
tbl # Prints the table, caption, and note
#> Distribution of estimates within each facet
#>      Facet Levels MeanEstimate SDEstimate MinEstimate MaxEstimate Span
#>  Criterion      3            0        0.3       -0.34        0.22 0.57
#>      Rater      6            0        0.4       -0.61        0.41 1.02
#> Note. No population model was requested; MML used an unconditional normal person distribution.

# Extract the ordinary data frame for further formatting or export
tbl$table
#>       Facet Levels MeanEstimate SDEstimate MinEstimate MaxEstimate Span
#> 1 Criterion      3            0        0.3       -0.34        0.22 0.57
#> 2     Rater      6            0        0.4       -0.61        0.41 1.02
# This table summarizes facets; use as.data.frame(fit) for individual estimates
# }