Convert mfrm_fit to a tidy data.frame

Returns all facet-level estimates (person and others) in a single tidy data.frame. Person rows retain their original identifiers; review or transform them before writing the result outside a controlled analysis environment.

Usage

# S3 method for class 'mfrm_fit'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

x: An mfrm_fit object from fit_mfrm.
row.names: Ignored (included for S3 generic compatibility).
optional: Ignored (included for S3 generic compatibility).
...: Additional arguments (ignored).

Value

A data.frame with columns Facet, Level, Estimate, and Extreme. The Extreme column is populated for person rows from the extreme-score flag ("Min" / "Max" / NA); non-person facet rows carry NA in that column by design. Corrected-JML fits additionally retain estimator, correction order, point/covariance status, uncertainty target and structural RootSE columns. Person RootSE is missing; extreme Person flags are "low" / "high" / "none". The estimation_note attribute explains the local-root uncertainty and remaining bias. Ordinary diagnostic attachments do not apply to this estimator; see the Corrected JML section of fit_mfrm.

Details

This method returns four columns (Facet, Level, Estimate, Extreme) so that the result is easy to inspect, join, or write to disk.

Interpreting output

Person estimates are returned with Facet = "Person". All non-person facets are stacked underneath in the same schema.

Typical workflow

Fit a model with fit_mfrm().
Convert with as.data.frame(fit) for a compact long-format export.
Join additional diagnostics later if you need SE or fit statistics.

Examples

library(mfrmr)

# Saved synthetic MML/RSM analysis of example_operational.
# For your own data, fit a model first; see ?fit_mfrm.
example_fit <- readRDS(system.file("examples", "operational-mml.rds", package = "mfrmr"))
fit <- example_fit$fit

# Extract estimates and select the rows to display
estimates <- as.data.frame(fit)
head(subset(estimates, Facet == "Person")) # First six persons
#>    Facet Level    Estimate Extreme
#> 1 Person  P001  0.28429588    none
#> 2 Person  P002  0.66118004    none
#> 3 Person  P003  0.02177773    none
#> 4 Person  P004  0.22410785    none
#> 5 Person  P005 -0.17496065    none
#> 6 Person  P006  0.67681003    none
subset(estimates, Facet == "Rater")       # All raters
#>    Facet Level   Estimate Extreme
#> 49 Rater   R01 -0.6059776    <NA>
#> 50 Rater   R02 -0.3820356    <NA>
#> 51 Rater   R03  0.2120388    <NA>
#> 52 Rater   R04  0.1799462    <NA>
#> 53 Rater   R05  0.1842365    <NA>
#> 54 Rater   R06  0.4117917    <NA>
subset(estimates, Facet == "Criterion")   # All criteria
#>        Facet        Level   Estimate Extreme
#> 55 Criterion      Content -0.3441471    <NA>
#> 56 Criterion     Language  0.1204520    <NA>
#> 57 Criterion Organization  0.2236950    <NA>