Package {IRTC}

Marginal Maximum Likelihood Estimation for Item Response Models

Description

A self-contained marginal maximum likelihood estimation package for unidimensional and multidimensional item response models. It supports Rasch / 1PL, partial credit, rating scale, 2PL and generalized partial credit models, latent regression, multiple groups, case weights, and a bounded-memory streaming engine for large simple-structure models.

Details

Two entry points are provided.

For statisticians, the expert functions irtc.mml and irtc.mml.2pl return an object of class irtc with summary, print, logLik, anova and plot methods.

For non-specialists and automated pipelines, the one-stop function irtc reads data from common file formats (irtc_read), scores raw responses (irtc_score), checks the data (irtc_check_data) and enriches the fitted model with classical statistics (irtc_ctt), item fit (irtc_itemfit) and plain-language quality ratings (irtc_quality). Results can be exported as three Excel workbooks (irtc_excel), audience-specific Word/HTML reports (irtc_report), a layered text summary (plain_summary) or machine-readable structures (irtc_results, irtc_json; schema reference in inst/llms.txt). User-facing output is bilingual; see irtc_lang.

Author(s)

Kunxiang Ma and WEIAN DATA TECH (Beijing) Co., Ltd.

See Also

irtc, irtc.mml, irtc.mml.2pl

Simulated Polytomous Item Response Data

Description

A simulated dataset of polytomous item responses suitable for fitting partial credit and generalised partial credit models, provided as an example dataset for IRTC.

Usage

data(data.gpcm)

Format

A data frame with 392 persons (rows) and 3 polytomously scored items (columns Comfort, Work, Benefit), each scored in four ordered categories 0, 1, 2, 3.

Details

The data were simulated from a partial credit model with three step parameters per item. The generating script is ‘scripts/gen_data.R’ in the package sources.

Source

Simulated by IRTC. See ‘scripts/gen_data.R’ in the package sources for the reproducible generating code.

Simulated Multiple-Choice Item Response Data

Description

A simulated dataset of multiple-choice item responses provided in both raw and scored form, provided as an example dataset for IRTC.

Usage

data(data.mc)

Format

A list with two components:

raw

A character matrix (143 persons x 30 items) of raw multiple-choice responses coded "A"–"D", with "9" denoting an omitted or not-reached response.

scored

A data frame of the corresponding dichotomously scored (0/1) responses, where 1 marks the keyed (correct) option.

Details

Correctness was simulated under a Rasch model; raw responses were then built from a randomly assigned answer key with distractors drawn at random. The generating script is ‘scripts/gen_data.R’ in the package sources.

Source

Simulated by IRTC. See ‘scripts/gen_data.R’ in the package sources for the reproducible generating code.

Simulated Dichotomous Rasch Data

Description

A simulated dataset of dichotomous (0/1) item responses generated under the Rasch model, provided as an example dataset for IRTC.

Usage

data(data.sim.rasch)

Format

A matrix with 2000 persons (rows) and 40 dichotomous items (columns), with entries coded 0 or 1.

Details

The data were simulated with person abilities drawn from a standard normal distribution and item difficulties spread evenly over [-2.2, 2.2]. The generating script is ‘scripts/gen_data.R’ in the package sources.

Source

Simulated by IRTC. See ‘scripts/gen_data.R’ in the package sources for the reproducible generating code.

One-Stop Item Response Model Estimation

Description

Reads data from a file or R object, cleans it, optionally scores raw multiple-choice responses against an answer key, checks it, estimates the requested item response model and enriches the result with classical item statistics, item fit and plain-language quality ratings.

Designed for non-specialists and automated callers; professional users keep full control because all additional arguments are passed unchanged to irtc.mml (for "1PL", "PCM", "PCM2", "RSM") or irtc.mml.2pl (for "2PL", "GPCM").

Usage

irtc(data, model, key = NULL, rules = NULL, q = NULL,
    on_mismatch = c("warn", "error"),
    rare_categories = c("collapse", "prior"), id = NULL, weights = NULL,
    sheet = 1, missing_codes = c(-9, -99, 99, 999), check = TRUE,
    quality = TRUE, verbose = TRUE, ...)

Arguments

Details

Items without any observed responses or without variance are removed before estimation (with a warning of class irtc_warning_estimation). All errors raised by the usability layer are structured conditions with fields code, reason and fix; see the package file inst/llms.txt for the full list of codes.

The language of user-facing output is controlled by options(irtc.lang = "zh") (default) or "en".

Value

An object of class irtc (see irtc.mml) with an additional list component usability containing model, data_log (cleaning log), check (the irtc_check_data result), removed_items, ctt, itemfit and quality.

See Also

irtc_read, irtc_check_data, plain_summary, irtc_excel, irtc_report, irtc_results

Examples

set.seed(1)
theta <- rnorm(150)
resp <- as.data.frame(sapply(seq(-1, 1, length.out = 5), function(b) {
    as.numeric(runif(150) < plogis(theta - b))
}))
colnames(resp) <- paste0("I", 1:5)

mod <- irtc(resp, model = "1PL", verbose = FALSE)
plain_summary(mod, lang = "en")
mod$usability$quality

Marginal Maximum Likelihood Estimation of Fixed-Slope Item Response Models

Description

Estimates unidimensional and multidimensional fixed-slope item response models by marginal maximum likelihood (MML). Supported models include the Rasch / 1PL model, the partial credit model ("PCM", "PCM2") and the rating scale model ("RSM"). Latent regression on covariates, multiple groups, case weights and user-specified design matrices are supported.

Usage

irtc.mml(resp, Y = NULL, group = NULL, irtmodel = "1PL",
   formulaY = NULL, dataY = NULL, ndim = 1, pid = NULL,
   xsi.fixed = NULL, xsi.inits = NULL, beta.fixed = NULL, beta.inits = NULL,
   variance.fixed = NULL, variance.inits = NULL, est.variance = TRUE,
   constraint = "cases", A = NULL, B = NULL, B.fixed = NULL, Q = NULL,
   est.slopegroups = NULL, E = NULL, pweights = NULL,
   userfct.variance = NULL, variance.Npars = NULL, item.elim = TRUE,
   verbose = TRUE, control = list())

## S3 method for class 'irtc'
summary(object, file = NULL, ...)

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

## S3 method for class 'irtc'
logLik(object, ...)

## S3 method for class 'irtc'
anova(object, ...)

Arguments

Value

An object of class irtc, a list with (among others) the components:

References

Adams, R. J., Wilson, M., & Wang, W. C. (1997). The multidimensional random coefficients multinomial logit model. Applied Psychological Measurement, 21(1), 1–23. doi:10.1177/0146621697211001

See Also

irtc.mml.2pl for models with estimated item slopes.

Examples

#############################################################################
# EXAMPLE 1: Unidimensional Rasch model
#############################################################################

data(data.sim.rasch)
mod1 <- irtc.mml(resp = data.sim.rasch)
summary(mod1)

#############################################################################
# EXAMPLE 2: Partial credit model
#############################################################################

data(data.gpcm)
mod2 <- irtc.mml(resp = data.gpcm, irtmodel = "PCM")
summary(mod2)

Marginal Maximum Likelihood Estimation of Slope (Discrimination) Item Response Models

Description

Estimates unidimensional and multidimensional item response models with estimated item slopes (discriminations) by marginal maximum likelihood (MML). Supported models are the two-parameter logistic model ("2PL"), the generalized partial credit model ("GPCM"), their design-matrix and slope-group variants ("GPCM.design", "2PL.groups", "GPCM.groups").

Usage

irtc.mml.2pl(resp, Y = NULL, group = NULL, irtmodel = "2PL",
   formulaY = NULL, dataY = NULL, ndim = 1, pid = NULL,
   xsi.fixed = NULL, xsi.inits = NULL, beta.fixed = NULL, beta.inits = NULL,
   variance.fixed = NULL, variance.inits = NULL, est.variance = FALSE,
   A = NULL, B = NULL, B.fixed = NULL, Q = NULL, est.slopegroups = NULL,
   E = NULL, gamma.init = NULL, pweights = NULL, userfct.variance = NULL,
   variance.Npars = NULL, item.elim = TRUE, verbose = TRUE,
   method = c("auto", "grid", "streaming"), control = list())

Arguments

Value

An object of class irtc (see irtc.mml) with, in addition, the estimated slope array B and the slope parameter vector. Streaming-engine fits also carry a routing component (chosen engine and reasoning) and, when fast=TRUE with verification on, an accuracy_report: a measured, stratified approximation check (per-metric error quantiles plus a met verdict), not a strict mathematical error bound.

See Also

irtc.mml for fixed-slope (Rasch / PCM / RSM) models.

Examples

data(data.sim.rasch)
mod1 <- irtc.mml.2pl(resp = data.sim.rasch, irtmodel = "2PL")
summary(mod1)

data(data.gpcm)
mod2 <- irtc.mml.2pl(resp = data.gpcm, irtmodel = "GPCM")
summary(mod2)

Pre-Estimation Data Diagnostics

Description

Checks a response data set before model estimation and returns both a human-readable report and a machine-readable issue table, so that survey staff and automated callers (e.g. AI agents) can verify data quality and act on concrete fixes.

Checked, among others: number of items and persons, non-numeric (raw) response columns, negative or non-integer values, items without responses or without variance, extreme missingness, sparse categories, persons without any responses and duplicated person IDs.

Usage

irtc_check_data(x, key = NULL, verbose = TRUE)

## S3 method for class 'irtc_check'
print(x, lang = irtc_lang(), ...)

Arguments

Value

An object of class irtc_check: a list with ok (logical: no error-level issues), n_persons, n_items, n_errors, n_warnings and issues, a data frame with columns code, severity ("error", "warning", "info"), scope, where, message_en, message_zh, fix_en, fix_zh.

See Also

irtc_read, irtc

Examples

d <- data.frame(i1 = c(0, 1, 1, 0), i2 = c(1, 1, 1, 1),
    i3 = c(0, NA, 1, -9))
chk <- irtc_check_data(d, verbose = FALSE)
chk$ok
chk$issues[, c("code", "severity", "where")]
print(chk, lang = "en")

Classical Test Theory Item Statistics

Description

Computes classical item statistics: item means and difficulty (share of the maximum score), corrected item-total (item-rest) correlations, Cronbach's alpha and alpha-if-item-deleted.

Usage

irtc_ctt(x, key = NULL)

## S3 method for class 'irtc_ctt'
print(x, lang = irtc_lang(), ...)

Arguments

Value

An object of class irtc_ctt: a list with items (data frame with columns item, N, miss_rate, max_score, M, pvalue, discr, alpha_if_deleted), alpha, n_persons and n_items.

See Also

irtc_quality, irtc_itemfit

Examples

set.seed(1)
theta <- rnorm(200)
resp <- as.data.frame(sapply(seq(-1, 1, length.out = 6), function(b) {
    as.numeric(runif(200) < plogis(theta - b))
}))
ctt <- irtc_ctt(resp)
ctt$alpha
ctt$items

Export Three Excel Result Workbooks

Description

One call writes three separate .xlsx files (requires the optional openxlsx package):

1. <prefix>_item_quality.xlsx - a plain-language item quality table (score rate, difficulty label, discrimination, fit, colour-coded overall rating, reasons and advice) plus a notes sheet explaining every column, for judging item quality without statistical training.

2. <prefix>_item_parameters.xlsx - the IRT difficulty / discrimination table with a frozen column schema (schema_version, analysis_id, model, item_id, n_obs, p_value, slope_a, difficulty_b, se_b, tau_k), designed for cross-year anchor-item linking: files from different administrations can be merged directly on item_id.

3. <prefix>_person_ability.xlsx - a flat person ability table (ID, number answered, raw score, EAP, standard error, percentile, T score) whose rows stay in input order so it can be pasted next to a master sample sheet.

Usage

irtc_excel(mod, dir = ".", prefix = "IRTC", lang = irtc_lang(),
    resp = NULL, overwrite = FALSE, verbose = TRUE)

irtc_param_table(mod, resp = NULL)

irtc_person_table(mod, lang = irtc_lang())

Arguments

Value

irtc_excel invisibly returns a named character vector with the three file paths (quality, parameters, ability). irtc_param_table and irtc_person_table return the underlying data frames and work without openxlsx.

See Also

irtc, irtc_report, irtc_results

Examples

set.seed(1)
theta <- rnorm(150)
resp <- as.data.frame(sapply(seq(-1, 1, length.out = 5), function(b) {
    as.numeric(runif(150) < plogis(theta - b))
}))
mod <- irtc(resp, model = "1PL", verbose = FALSE)

## data frames behind the workbooks (no extra packages needed)
irtc_param_table(mod)
head(irtc_person_table(mod, lang = "en"))

if (requireNamespace("openxlsx", quietly = TRUE)) {
    out <- irtc_excel(mod, dir = tempdir(), overwrite = TRUE,
        verbose = FALSE)
    basename(out)
}

Item Fit Statistics (Infit and Outfit)

Description

Residual-based item fit for a fitted model: infit and outfit mean squares with Wilson-Hilferty t statistics, evaluated at the EAP person estimates. Values near 1 indicate good fit; the conventional normal range is 0.7 to 1.3.

Usage

irtc_itemfit(mod, resp = NULL)

## S3 method for class 'irtc_itemfit'
print(x, lang = irtc_lang(), ...)

Arguments

Value

A data frame of class irtc_itemfit with columns item, N, outfit, outfit_t, infit, infit_t.

See Also

irtc_quality, irtc_ctt

Examples

set.seed(1)
theta <- rnorm(200)
resp <- as.data.frame(sapply(seq(-1, 1, length.out = 5), function(b) {
    as.numeric(runif(200) < plogis(theta - b))
}))
mod <- irtc.mml(resp = resp, verbose = FALSE)
irtc_itemfit(mod)

Output Language of the IRTC Usability Layer

Description

Returns the current output language, "zh" (Chinese, the default) or "en" (English). Set it globally with options(irtc.lang = "en"). The language affects user-facing text (messages, printed summaries, Excel headers, reports); machine-readable schemas (irtc_results, the linking table of irtc_excel) never depend on it.

Usage

irtc_lang()

Value

"zh" or "en".

Examples

old <- options(irtc.lang = "en")
irtc_lang()
options(old)

Plain-Language Item Quality Ratings

Description

Combines classical difficulty and discrimination (irtc_ctt) with IRT item fit (irtc_itemfit) into a four-level quality rating per item - "good", "acceptable", "review", "revise" - with bilingual reasons and advice that non-specialists can act on. Negative discrimination (usually a wrong answer key) always leads to "revise".

Usage

irtc_quality(mod, resp = NULL, thresholds = NULL)

irtc_quality_thresholds()

## S3 method for class 'irtc_quality'
print(x, lang = irtc_lang(), ...)

Arguments

Value

A data frame of class irtc_quality with columns item, N, pvalue, discr, outfit, infit, rating, reasons_en, reasons_zh, advice_en, advice_zh, and attributes alpha (Cronbach's alpha) and thresholds. irtc_quality_thresholds() returns the default threshold list.

See Also

irtc, irtc_excel

Examples

set.seed(1)
theta <- rnorm(300)
resp <- as.data.frame(sapply(seq(-1, 1, length.out = 6), function(b) {
    as.numeric(runif(300) < plogis(theta - b))
}))
resp$noise <- rbinom(300, 1, 0.5)  # a broken item
mod <- irtc.mml(resp = resp, verbose = FALSE)
qual <- irtc_quality(mod)
qual[, c("item", "pvalue", "discr", "rating")]
print(qual, lang = "en")

Read and Clean Item Response Data from Many Formats

Description

Unified data import with automatic cleaning. Supports Excel (.xlsx, .xls; requires readxl), delimited text (.csv, .tsv, .txt, .dat; delimiter and UTF-8/GBK encoding are detected automatically), SPSS/Stata/SAS files (.sav, .por, .dta, .sas7bdat, .xpt; requires haven) and in-memory data frames, matrices and tibbles.

Every cleaning action is recorded in a bilingual log attached to the returned object, so results remain traceable.

Usage

irtc_read(x, sheet = 1, id = NULL, weights = NULL,
    missing_codes = c(-9, -99, 99, 999),
    na_strings = c("", "NA", "N/A", "n/a", ".", "*",
        "\u7f3a\u5931", "\u65e0", "\u7a7a"),
    guess_id = TRUE, guess_weights = TRUE, clean = TRUE, recode = TRUE,
    verbose = TRUE)

## S3 method for class 'irtc_data'
print(x, lang = irtc_lang(), ...)

Arguments

Value

An object of class irtc_data: a list with components resp (cleaned response data frame), pid (person IDs or NULL), weights (sampling weights or NULL), dropped (set-aside non-response columns), log (cleaning log data frame with columns step, code, message_en, message_zh) and source.

See Also

irtc, irtc_score, irtc_check_data

Examples

path <- tempfile(fileext = ".csv")
writeLines(c("id,i1,i2,i3", "s1,0,1,99", "s2,1,-9,1", "s3,1,0,0"), path)
d <- irtc_read(path, verbose = FALSE)
d$resp
d$pid
print(d, lang = "en")
unlink(path)

Read a Q Matrix from a File and Align It with the Response Data

Description

irtc_read_q reads a Q (item-by-dimension) matrix from any file format supported by irtc_read (Excel, delimited text, SPSS/Stata/SAS) or from a data frame / matrix. The dimension column headers become the dimension names used in all downstream person-level output (ability and standard-error headers). An optional partial-credit column declares which items are scored in multiple levels, and optionally their maximum scores.

irtc_align_q compares the Q-matrix items with the response-data items. By default mismatches raise warnings (W420, W421) and the analysis continues with the shared items; on_mismatch = "error" turns any mismatch into an error (E422).

Usage

irtc_read_q(x, sheet = 1)

irtc_align_q(data, q, on_mismatch = c("warn", "error"))

## S3 method for class 'irtc_qmatrix'
print(x, lang = irtc_lang(), ...)

Arguments

Details

Expected table layout for irtc_read_q:

• An item-ID column named item, item_id, "题目", "题号" (etc.); if no such name is found, the first non-numeric column is used.

• One or more numeric dimension columns; each header is a dimension name (e.g. "代数", algebra). Values are usually 0/1 loadings. Empty cells count as 0. Every item must load on at least one dimension.

• Optionally one partial-credit column named partial, max_score, "分部计分", "满分" (etc.). Values may be 0/1, TRUE/FALSE, yes/no, "是"/"否", or integer maximum scores (a value >= 2 declares a partial-credit item and records its maximum score).

Value

irtc_read_q returns an object of class irtc_qmatrix: a list with components Q (numeric item-by-dimension matrix with dimnames), partial (named logical vector), max_score (named integer vector, NA when not declared), log and source.

irtc_align_q returns a list with components data (the input data restricted to the shared items), q (the aligned irtc_qmatrix, rows in data column order), common, q_only and data_only (character vectors of item names).

See Also

irtc, irtc_read

Examples

qdf <- data.frame(item = c("I1", "I2", "I3"),
    algebra = c(1, 1, 0), geometry = c(0, 0, 1),
    partial = c(0, 1, 0))
qm <- irtc_read_q(qdf)
print(qm, lang = "en")

resp <- data.frame(I1 = c(0, 1), I2 = c(2, 0), I4 = c(1, 1))
al <- suppressWarnings(irtc_align_q(resp, qm))
al$q_only
al$data_only

Generate a Word or HTML Analysis Report

Description

Writes an analysis report with figures (Wright map, ability distribution, item quality summary, item characteristic curves) and tables. Three audience layouts are available: "decision" (a short executive summary with recommendations), "survey" (a plain-language full report) and "stat" (a complete technical report including item parameters, item fit and model information). Every layout includes a Model diagnostics section (convergence, information criteria with interpretation, EAP reliability band, item-fit reading) and a Data processing transparency section (weights summary, Q-matrix item alignment, unobserved-category collapses, dropped items, scoring summary, and - for "stat"/"survey" - the full cleaning log), detailed for statisticians and condensed for other audiences.

HTML reports are fully self-contained single files (images embedded); Word reports require the optional officer package.

Usage

irtc_report(mod, file, format = NULL,
    audience = c("survey", "decision", "stat"), lang = irtc_lang(),
    resp = NULL, title = NULL, overwrite = FALSE, verbose = TRUE)

Arguments

Value

Invisibly, the output file path.

See Also

irtc, plain_summary, irtc_excel, plot.irtc

Examples

set.seed(1)
theta <- rnorm(150)
resp <- as.data.frame(sapply(seq(-1, 1, length.out = 5), function(b) {
    as.numeric(runif(150) < plogis(theta - b))
}))
mod <- irtc(resp, model = "1PL", verbose = FALSE)

file <- tempfile(fileext = ".html")
irtc_report(mod, file, audience = "decision", lang = "en",
    verbose = FALSE)
file.exists(file)
unlink(file)

Machine-Readable Results with a Stable Schema

Description

Extracts fitted-model results as tidy data frames with a stable, documented schema aimed at automated callers (pipelines, AI agents). Column names are fixed English snake_case identifiers and never depend on the language option. irtc_json exports the same content as JSON (requires the optional jsonlite package).

Schema v1.1 components: model_info (one row: schema and package version, model, sizes, deviance, AIC/BIC, EAP reliability, iterations, and dimension_names when a Q matrix was supplied), items (per item: parameters, classical statistics, fit, quality rating and bilingual reasons/advice, plus a status column and, for polytomous items, semantic step difficulties and rare-category annotations), persons (per person: ID, number answered, raw score, EAP, SE, percentile, T score; ability/SE headers use the Q dimension names when available), cleaning_log and check_issues (present when the model was fitted via irtc). Schema 1.1 is additive over 1.0: existing column names and meanings are unchanged. Items declared in the Q matrix but not estimated keep a row with status "dropped_no_response" or "declared_not_estimated". See the package file inst/llms.txt for the full schema reference.

Usage

irtc_results(mod, resp = NULL)

irtc_json(mod, file = NULL, resp = NULL, pretty = TRUE)

## S3 method for class 'irtc_results'
print(x, lang = irtc_lang(), ...)

Arguments

Value

irtc_results returns an object of class irtc_results, a list of data frames as described above. irtc_json returns a JSON string, or invisibly the file path when file is given.

See Also

irtc, irtc_excel

Examples

set.seed(1)
theta <- rnorm(150)
resp <- as.data.frame(sapply(seq(-1, 1, length.out = 5), function(b) {
    as.numeric(runif(150) < plogis(theta - b))
}))
mod <- irtc(resp, model = "1PL", verbose = FALSE)

res <- irtc_results(mod)
res$model_info
head(res$items)

if (requireNamespace("jsonlite", quietly = TRUE)) {
    json <- irtc_json(mod)
    substr(json, 1, 80)
}

Score Raw Responses with an Answer Key or Scoring Rules

Description

Converts raw multiple-choice responses (e.g. "A", "B", "C", "D") to 0/1 scores using an answer key, or maps responses to partial-credit scores through a rules table. Responses are normalised before matching: whitespace is trimmed, case is ignored and full-width characters are converted to half-width.

Both key and rules may also be file paths in any format supported by irtc_read. A key file needs an item column (item, "题目", ...) and an answer column (answer, "答案", ...); an optional partial-answer column (partial_answer, "部分正确答案", ...) lists answers worth partial credit, several separated by |, ;, , or "、". Items with partial answers are scored full = 2, partial = 1, other = 0; items without stay 0/1. For more than three score levels supply an explicit rules table instead.

Usage

irtc_score(resp, key = NULL, rules = NULL, na_as_wrong = FALSE,
    sheet = 1)

Arguments

Value

The same type as the input: an irtc_data object with scored resp and an extended log, or a data frame with a "scoring_log" attribute.

See Also

irtc_read, irtc

Examples

resp <- data.frame(Q1 = c("A", "b", "C"), Q2 = c("c", "C", "D"),
    stringsAsFactors = FALSE)
irtc_score(resp, key = c(Q1 = "A", Q2 = "C"))

rules <- data.frame(item = "Q1", response = c("A", "B", "C"),
    score = c(2, 1, 0))
irtc_score(resp["Q1"], rules = rules)

Plain-Language Layered Summary of a Fitted Model

Description

Prints a layered, plain-language summary of a fitted item response model: conclusion first, then analysis overview, item quality, ability distribution and suggested next steps. Intended for survey staff and decision makers; statisticians can use summary(mod) for the full technical output.

Usage

plain_summary(mod, lang = irtc_lang())

Arguments

Value

Invisibly, the list of summary sections (each with title and body); the same content that irtc_report uses.

See Also

irtc, irtc_report

Examples

set.seed(1)
theta <- rnorm(150)
resp <- as.data.frame(sapply(seq(-1, 1, length.out = 5), function(b) {
    as.numeric(runif(150) < plogis(theta - b))
}))
mod <- irtc(resp, model = "1PL", verbose = FALSE)
plain_summary(mod, lang = "en")

Diagnostic Plots for Fitted Item Response Models

Description

Base-graphics plots for a fitted model: "wright" (item difficulty vs person ability map), "ability" (person ability histogram, one panel per dimension), "quality" (item quality rating summary) and "icc" (item characteristic curves; unidimensional models only). The same figures are embedded in irtc_report reports.

Usage

## S3 method for class 'irtc'
plot(x, type = c("wright", "ability", "quality", "icc"),
    lang = irtc_lang(), items = NULL, resp = NULL, ...)

Arguments

Value

Invisibly, x.

See Also

irtc, irtc_report

Examples

set.seed(1)
theta <- rnorm(150)
resp <- as.data.frame(sapply(seq(-1, 1, length.out = 5), function(b) {
    as.numeric(runif(150) < plogis(theta - b))
}))
mod <- irtc(resp, model = "1PL", verbose = FALSE)
plot(mod, type = "wright", lang = "en")
plot(mod, type = "icc", lang = "en", items = 1:2)