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Summarize an APA/FACETS table object

Source: R/api-reports.R
summary.apa_table.Rd

Summarize an APA/FACETS table object

Usage

# S3 method for class 'apa_table'
summary(object, digits = 3, top_n = 8, ...)

Arguments

object

Output from apa_table().

digits

Number of digits used for numeric summaries.

top_n

Maximum numeric columns shown in numeric_profile.

...

Reserved for generic compatibility.

Value

An object of class summary.apa_table.

Details

Compact summary helper for QA of table data before manuscript export.

Interpreting output

  • overview: table size/composition and missingness.

  • numeric_profile: quick distribution summary of numeric columns.

  • caption/note: text metadata readiness.

Typical workflow

  1. Build table with apa_table().

  2. Run summary(tbl) and inspect overview.

  3. Use plot.apa_table() for quick numeric checks if needed.

See also

apa_table(), plot()

Examples

# \donttest{
toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score", method = "JML", maxit = 30)
#> Warning: Optimization convergence review did not produce an inference-ready numerical solution (code = 1, status = iteration_limit). Optimizer reached the iteration limit before the terminal gradient became small enough for review-only acceptance. Inspect the model specification, data support, and starting values. Do not interpret estimates until the review is resolved.
tbl <- apa_table(fit, which = "summary")
summary(tbl)
#> APA Table Summary
#>  Branch Style   Which Rows Columns NumericColumns MissingValues
#>     apa   apa summary    1      52             30             9
#> 
#> Caption
#>  - Table 1
#> Facet Summary (Measures, Precision, Fit, Reliability)
#> 
#> Note
#>  - Measures are reported in logits; higher person values indicate higher ability, and higher non-person facet values indicate greater severity/difficulty (all non-person facets used the default negative orientation in this fit). Model S.E. = exploratory standard error; Real S.E. = fit-adjusted exploratory standard error; MnSq = mean-square fit. Report CI_Lower / CI_Upper (95%, Normal approximation) alongside measures for rows flagged CIEligible. Model = RSM; estimation = JML; N = 768 observations from 48 persons on a 4-category scale (1-4).
#> 
#> Numeric profile
#>            Column N    Mean SD     Min     Max
#>               AIC 1 1753.90 NA 1753.90 1753.90
#>               BIC 1 2013.95 NA 2013.95 2013.95
#>        Categories 1    4.00 NA    4.00    4.00
#>   ConvergenceCode 1    1.00 NA    1.00    1.00
#>      EMIterations 0      NA NA      NA      NA
#>  EMRelativeChange 0      NA NA      NA      NA
#>   EffectiveReltol 1    0.00 NA    0.00    0.00
#>      ExtremeHighN 1    0.00 NA    0.00    0.00
# }