Export MFRM results to CSV files

Writes tidy CSV files suitable for import into spreadsheet software or further analysis in other tools.

Usage

export_mfrm(
  fit,
  diagnostics = NULL,
  output_dir = ".",
  prefix = "mfrm",
  tables = c("person", "facets", "summary", "steps", "measures"),
  overwrite = FALSE,
  acknowledge_sensitive = FALSE
)

Arguments

fit

Output from fit_mfrm.

diagnostics

Optional output from diagnose_mfrm. When provided, enriches facet estimates with SE, fit statistics, and writes the full measures table.

output_dir

Directory for CSV files. Created if it does not exist.

prefix

Filename prefix (default "mfrm").

tables

Character vector of tables to export. Any subset of "person", "facets", "summary", "steps", "measures". Default exports all available tables.

overwrite

If FALSE (default), refuse to overwrite existing files.

acknowledge_sensitive

Logical; set to TRUE only after acknowledging that these tables can contain direct person identifiers, person-level estimates, and original facet labels. This suppresses the privacy warning; it does not deidentify any file.

Value

Invisibly, a data.frame listing written files, their paths, and explicit privacy/data-handling metadata. Deidentified and ShareableWithoutReview are always FALSE.

Exported files

{prefix}_person_estimates.csv

Person ID, Estimate, SD.

{prefix}_facet_estimates.csv

Facet, Level, Estimate, and optionally SE, Infit, Outfit, PTMEA when diagnostics supplied.

{prefix}_fit_summary.csv

One-row model summary.

{prefix}_step_parameters.csv

Step/threshold parameters.

{prefix}_measures.csv

Full measures table (requires diagnostics).

Interpreting output

The returned data.frame tells you exactly which files were written and where. This is convenient for scripted pipelines where the output directory is created on the fly. The files are analysis tables, not a deidentified sharing package; review each file under the applicable data-handling policy before sharing it.

Typical workflow

1. Fit a model with fit_mfrm().

2. Optionally compute diagnostics with diagnose_mfrm() when you want enriched facet or measures exports.

3. Call export_mfrm(...) and inspect the returned Path column.

See also

fit_mfrm, diagnose_mfrm, as.data.frame.mfrm_fit

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"
)

# Compute diagnostics once for the following checks
diagnostics <- diagnose_mfrm(fit)

# Use a new temporary folder for this example; choose a permanent one for your work
output_dir <- tempfile("mfrmr-tables-")
files <- export_mfrm(
  fit,
  diagnostics = diagnostics,
  output_dir = output_dir,
  acknowledge_sensitive = TRUE # Synthetic data; exported tables retain person IDs
)

# Preview filenames; full paths remain in files$Path
data.frame(Table = files$Table, File = basename(files$Path))
#>      Table                      File
#> 1   person mfrm_person_estimates.csv
#> 2   facets  mfrm_facet_estimates.csv
#> 3  summary      mfrm_fit_summary.csv
#> 4    steps  mfrm_step_parameters.csv
#> 5 measures         mfrm_measures.csv
# Open a path from files$Path in a spreadsheet app or with read.csv()
# }