Package 'epmfd'

Description

clean_epmfd() removes individuals flagged as misfitting according to a chosen decision rule and returns a cleaned dataset that can be passed directly to scale_epmfd().

Usage

clean_epmfd(misfit, criterion = c("union", "intersection"), clean_item = FALSE)

Arguments

Details

The function uses logical misfit indicators stored in misfit$table, including:

• misfit_any: TRUE if at least one statistic flagged the person.

• Statistic-specific columns (e.g., Gnp, U3p, lpz) indicating per-statistic misfit decisions.

The set of statistics actually considered is taken from misfit$stats. Under the "intersection" rule, a person is removed only if all of those statistics are TRUE. Internally, rowSums(..., na.rm = TRUE) is used so that NA values do not force removal (i.e., NA behaves as “not flagged” in the intersection count).

Only items listed in misfit$scaled$kept are retained in the output. Person identifiers from the original raw object are preserved for the kept rows.

Value

An epmfd_clean list with:

• raw: An epmfd_raw object containing only the retained persons and items, directly usable in scale_epmfd().

• clean_data: The cleaned raw data frame (persons × kept items).

• n_removed: Number of persons removed.

• criterion: The applied decision rule.

• misfit: The original epmfd_misfit object (as provided).

Criterion

• "union" (default): A person is removed if at least one statistic (e.g., Gnp, U3p, lpz) flags them as misfitting. This is stricter.

• "intersection": A person is removed only if all statistics flag them as misfitting. This is more lenient.

See Also

misfit_epmfd(), scale_epmfd()

Examples

library(epmfd)
data<-load_epmfd(sampledata)
scaling_data<-scale_epmfd(data)
misfit_result<-misfit_epmfd(scaling_data)
clean_data<-clean_epmfd(misfit_result)
head(clean_data$clean_data)
dim(data$data)  # the dimension of raw data
dim(clean_data$clean_data)  # the dimension of clean data

Description

export_epmfd() writes commonly used tables from ⁠epmfd_*⁠ objects to CSV / Excel / SPSS files, and (optionally) saves the object itself as an RDS.

Usage

export_epmfd(
  object,
  dir = NULL,
  prefix = NULL,
  format = c("csv", "xlsx", "sav"),
  save_rds = FALSE,
  include_misfit = FALSE
)

Arguments

Details

What is produced depends on the object class:

• epmfd_clean: cleaned person-by-item data (clean); if include_misfit = TRUE and a misfit object is attached, also misfit.

• epmfd_misfit: if include_misfit = TRUE, misfit.

• epmfd_scaled: item status summary (scale).

When format = "sav", logical columns are converted to labelled factors (FALSE/TRUE) for SPSS compatibility. Writing .sav does not support list columns; the function aborts if such columns are present.

Value

If dir is NULL, a named list containing the tables that would be written (e.g., clean, misfit, scale). If dir is non-NULL, (invisibly) a character vector of file paths that were written.

File naming (when dir is provided)

Files are named ⁠<prefix>_<name>.<format>⁠ under dir. For example: study1_clean.csv, study1_misfit.xlsx, or study1_scale.sav.

See Also

saveRDS(), readr, openxlsx, haven

Examples

# Minimal toy objects created inside the example ----
  set.seed(1)
  toy_clean <- data.frame(
    I1 = sample(0:1, 6, TRUE),
    I2 = sample(0:1, 6, TRUE)
  )
  toy_misfit <- data.frame(
    person = 1:6, Gpn = runif(6), U3p = runif(6)
  )

  clean_obj <- structure(
    list(clean_data = toy_clean,
         misfit     = list(table = toy_misfit)),
    class = "epmfd_clean"
  )

  misfit_obj <- structure(
    list(table = toy_misfit, method = "mokken"),
    class = "epmfd_misfit"
  )

  scaled_obj <- structure(
    list(kept = c("I1", "I2"), removed = character()),
    class = "epmfd_scaled"
  )

  # 1) No writing: return list
  lst <- export_epmfd(clean_obj, dir = NULL, include_misfit = TRUE)
  str(lst)

  # 2) Write to a temporary directory (CRAN policy)
  tmpdir <- tempdir()
  export_epmfd(clean_obj,  dir = tmpdir, prefix = "study1", format = "csv",
               save_rds = TRUE)

  # Optional formats guarded by Suggests (run only if installed)
  if (requireNamespace("haven", quietly = TRUE)) {
    export_epmfd(misfit_obj, dir = tmpdir, format = "sav",
                 include_misfit = TRUE)
  }
  if (requireNamespace("openxlsx", quietly = TRUE)) {
    export_epmfd(scaled_obj, dir = tmpdir, prefix = "scaleA",
                 format = "xlsx")
  }

Description

load_epmfd() prepares raw item-response data for subsequent functions in the epmfd workflow. It validates input, ensures that all item responses fall within the expected range of categories, converts items to ordered factors, and attaches person IDs.

Usage

load_epmfd(data, id_col = NULL, likert_levels = NULL)

Arguments

Details

Each column of data is validated to ensure responses are within 1:K. Values outside this range cause an error. Missing values are allowed and reported.

Value

An object of class epmfd_raw, a list with elements:

• data: A data.frame of ordered-factor responses

• id: Vector of person IDs

• K: Maximum number of categories per item

See Also

scale_epmfd(), misfit_epmfd()

Examples

# Example: 5 persons × 3 items, responses 1–4
df <- data.frame(
  Pid = paste0("P", 1:5),
  Item1 = c(1, 2, 3, 2, 1),
  Item2 = c(2, 3, 4, 2, 1),
  Item3 = c(3, 4, 1, 2, 2)
)

raw <- load_epmfd(df, id_col = "Pid", likert_levels = 4)
str(raw)

Description

misfit_epmfd() computes selected person-fit statistics for polytomous responses and returns an epmfd_misfit object with scores, thresholds, and logical flags per person.

Usage

misfit_epmfd(object, stats = c("auto", "lpz", "Gnp", "U3p"), cut.off = "auto")

Arguments

Details

Auto vs manual decision for misfit_final:

• If stats contains "auto":

  • for "mokken": misfit_final = Gnp & U3p

  • for "mirt": misfit_final = (lpz & Gnp & U3p) if any such rows exist, otherwise fallback to lpz only.

• If stats is manual (no "auto"): misfit_final is the AND over the selected statistics (if only one selected, it is used directly).

Polytomous PerFit statistics assume a common design K (number of categories) across items. This function uses object$raw$K as the global design K and maps item responses to 0..K-1 without compressing per-item gaps (unused categories are allowed and do not trigger an error).

Value

An epmfd_misfit list with:

• scaled: the input epmfd_scaled object

• method: detected method ("mirt" or "mokken")

• stats: actually computed statistics (subset of c("lpz","Gnp","U3p"))

• thresholds: named list of lists with value and tail

• scores: named list of numeric score vectors per statistic

• table: a tibble with id, one logical column per statistic, misfit_any (OR over selected stats), and misfit_final (see Details)

Examples

library(epmfd)
data<-load_epmfd(sampledata)
scaling_data<-scale_epmfd(data)
misfit_result<-misfit_epmfd(scaling_data)
misfit_result
plot_misfit(misfit_result,threeD=TRUE)

Description

Quick visual summaries for three object classes:

• epmfd_scaled: Item-level retention summary (Kept vs Removed) and a quality-statistic histogram (either discrimination a for mirt or scalability H_i for mokken).

• epmfd_misfit: Bar plot of misfit counts per statistic and a global bar summarizing overall misfit ratio.

• epmfd_clean: Bar plot comparing remaining vs removed persons.

Usage

## S3 method for class 'epmfd_scaled'
plot(x, ...)

## S3 method for class 'epmfd_misfit'
plot(x, ...)

## S3 method for class 'epmfd_clean'
plot(x, ...)

Arguments

Details

If the patchwork package is installed, paired plots are stacked vertically and returned as a single patchwork object; otherwise a list of two ggplot2 objects is returned.

Value

A single ggplot2 object, a patchwork object (if available), or a list of ggplot2 objects—depending on the class and whether combined layout is possible.

Dependencies

These methods use ggplot2. For epmfd_scaled objects fitted with mirt, the method accesses model coefficients via mirt if that package is installed (it is not required for mokken). Stacking multiple plots uses patchwork when available.

See Also

plot_misfit for 2D/3D scatter visualizations of person-level misfit, and misfit_epmfd / clean_epmfd for producing the inputs to these plots.

Examples

# Scaled object
p_scaled <- plot(scaled_obj)               # item retention + quality histogram

# Misfit object
p_mf <- plot(misfit_obj)                   # per-statistic counts + overall ratio

# Cleaned object
p_cl <- plot(clean_obj)                    # remaining vs removed persons

# Add ggplot2 options through '...'
plot(misfit_obj, alpha = 0.8)

Description

plot_misfit() visualizes person-level misfit statistics stored in an epmfd_misfit object. It supports:

• 2D: scatter plots for all pairwise combinations of the selected statistics (or a single 2D plot if exactly two are given). Points are coloured by the joint exceedance logic (see any). Axis titles are the statistic names; the main title shows the cut-offs in parentheses, and dashed lines mark the cut-offs per axis.

• 3D: an interactive scatter using plotly if three statistics are supplied; optionally adds three semi-transparent planes at the x/y/z cut-offs when planes = TRUE.

Usage

plot_misfit(
  object,
  stats = NULL,
  threeD = FALSE,
  any = FALSE,
  planes = TRUE,
  label_ids = FALSE,
  ...
)

Arguments

Details

Cut-off logic. For each selected statistic, a person is deemed to exceed if its score is greater than the cut-off for upper-tailed statistics or less than the cut-off for lower-tailed statistics. In 2D, dashed vertical and horizontal lines indicate the cut-offs; the plot title shows "Y (cutY) vs X (cutX)" with formatted values. In 3D, axis titles include the cut-off values in parentheses, and (optionally) three grey planes make the cut-offs explicit.

Returned value. With two statistics, a single ggplot is returned; with three statistics and threeD = FALSE, a named list of ggplots is returned for all 2D pairs. With threeD = TRUE and three statistics, a plotly object is returned.

Dependencies. This function uses ggplot2 for 2D plots and, for 3D, plotly (required only when threeD = TRUE). Optional labels in 2D use ggrepel when installed.

Value

A ggplot object (2D), a named list of ggplots (all 2D pairs), or a plotly object (3D), depending on stats and threeD.

See Also

misfit_epmfd() for computing statistics and thresholds; clean_epmfd() for removing misfitting persons.

Examples

# Suppose 'mf' is an epmfd_misfit with scores Gnp, U3p, lpz

# 2D: single plot
plot_misfit(mf, stats = c("Gnp","U3p"), any = TRUE)

# 2D: all pairwise plots
plot_misfit(mf, stats = c("Gnp","U3p","lpz"))

# 3D: with cut-off planes
plot_misfit(mf, stats = c("Gnp","U3p","lpz"), threeD = TRUE, planes = TRUE)

# 3D: points only (no planes)
plot_misfit(mf, stats = c("Gnp","U3p","lpz"), threeD = TRUE, planes = FALSE)

Description

Prints summary information for an epmfd_misfit object.

Usage

## S3 method for class 'epmfd_misfit'
print(x, ...)

Arguments

Value

The input object x, returned (invisibly) after printing.

Description

A small toy dataset included in the epmfd package, containing polytomous item responses from simulated persons.

Usage

sampledata

Format

A data frame with 20 persons (rows) and 6 items (columns). Each item takes ordered values 1–5.

Examples

data(sampledata)
head(sampledata)

Description

scale_epmfd() fits either a parametric graded response model (GRM, via mirt) or a nonparametric Mokken model (via mokken) to polytomous item-response data and filters out weak items based on user-specified thresholds.

Usage

scale_epmfd(
  object,
  method = c("auto", "mirt", "mokken"),
  a_thr = 0.5,
  H_thr = 0.3
)

Arguments

Details

The function converts ordered factors to numeric before analysis.

• For GRM (mirt), items are filtered by their discrimination parameter a. The overall model fit is attempted using mirt::M2(); if this fails (e.g., due to insufficient df), a warning is issued and model_fit = NULL.

• For Mokken, item scalability coefficients H_i are computed and compared to H_thr.

Value

An object of class epmfd_scaled, a list containing:

• raw: the original epmfd_raw object

• method: scaling method actually used ("mirt" or "mokken")

• kept: names of items retained

• removed: names of items removed

• model: fitted GRM model (for "mirt"), else NULL

• ai: item parameter estimates (for "mirt")

• a_thr: discrimination threshold used (for "mirt")

• model_fit: results of mirt::M2() (if available)

• Hi: vector of item scalability coefficients (for "mokken")

• H_thr: scalability threshold used (for "mokken")

• items: the vector containing all items names.

See Also

load_epmfd(), misfit_epmfd(), plot.epmfd_scaled()

Examples

library(epmfd)
data<-load_epmfd(sampledata)
scale_epmfd(data)

Description

Summary method for epmfd_clean objects

Usage

## S3 method for class 'epmfd_clean'
summary(object, ...)

Arguments

Value

  Invisibly returns a named list with summary numbers.