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exametrika (version 1.6.0)

plot.exametrika: Plot Method for Objects of Class "exametrika"

Description

Creates visualizations for objects with class "exametrika". The calculation results of the exametrika package have an exametrika class attribute, along with the specific analysis model class (IRT, GRM, LCA, LRA, Biclustering, IRM, LDLRA, LDB, BINET). Each model has its own compatible plot types, accessible by specifying the 'type' parameter.

Usage

# S3 method for exametrika
plot(
  x,
  type = c("IRF", "TRF", "IIF", "TIF", "IIC", "ICC", "TIC", "IRP", "TRP", "LCD", "CMP",
    "FRP", "RMP", "LRD", "Array", "CRV", "RRV", "FieldPIRP", "LDPSR", "ScoreFreq",
    "ScoreRank", "ICRP", "ICBR"),
  items = NULL,
  students = NULL,
  nc = 1,
  nr = 1,
  overlay = FALSE,
  colors = NULL,
  cell_width = 3,
  cell_height = 1,
  filename = NULL,
  width = 800,
  height = 600,
  dpi = 300,
  ...
)

Value

Produces visualizations based on the model class and specified type:

IRT models

IRF (Item Response Function), TRF (Test Response Function), IIF (Item Information Function), TIF (Test Information Function)

LCA/LRA models

IRP (Item Reference Profile), TRP (Test Reference Profile), LCD/LRD (Latent Class/Rank Distribution), CMP/RMP (Class/Rank Membership Profile)

Biclustering/IRM models

Array plots showing clustering patterns, FRP, TRP, etc.

LDLRA/LDB/BINET models

Network and profile plots specific to each model

Arguments

x

An object of class "exametrika"

type

Character string specifying the plot type. Available types vary by model:

IRF, ICC

Item Response Function. Also known as 'ICC' (Item Characteristic Curve).

TRF

Test Response Function.

IIF, IIC

Item Information Function. Also known as 'IIC' (Item Information Curve).

TIF, TIC

Test Information Function. Also known as 'TIC' (Test Information Curve).

IRP

Item Reference Profile. Line graph with items and latent classes/ranks on the horizontal axis, and membership probability on the vertical axis.

CRV, RRV

Class/Rank Reference Vector. Plots correct answer rates for each class or rank, with fields on the horizontal axis and correct answer rates on the vertical axis.

TRP

Test Reference Profile. Shows latent classes/ranks on the horizontal axis, displaying members per class/rank as a bar graph and expected test scores as a line graph.

LCD

Latent Class Distribution. Displays latent classes on the horizontal axis, showing members per class as a bar graph and cumulative membership probability as a line.

LRD

Latent Rank Distribution. Similar to LCD but with ranks instead of classes on the horizontal axis.

CMP

Class Membership Profile. Line graph showing class membership probabilities of students.

RMP

Rank Membership Profile. Similar to CMP but with ranks instead of classes.

ScoreFreq

Frequency polygon of score distribution with rank thresholds.

ScoreRank

Heatmap of score membership probabilities for each rank.

ICRP

Visualizes ranks (x-axis) versus category response probabilities (y-axis).

ICBR

Visualizes ranks (x-axis) versus cumulative category probabilities (y-axis).

FRP

Field Reference Profile. Shows correspondence between fields and latent classes/ranks.

Array

Array plot for Biclustering/Ranklustering. Colored matrix cells where darker cells indicate larger values.

FieldPIRP

Shows correct response rates by number of correct answers in parent fields. Only available for LDB model.

LDPSR

Latent Dependence Passing Student Rate. Compares passing rates of parent and child classes.

items

Numeric vector specifying which items to plot. If NULL, all items are included. When type is "IIF"/"IIC", specifying 0 will produce a TIF/TIC for the entire test.

students

Numeric vector specifying which students to plot. If NULL, all students are included.

nc

Integer specifying the number of columns for multiple plots. Default is 1.

nr

Integer specifying the number of rows for multiple plots. Default is 1.

overlay

Logical. If TRUE, elements such as IRFs will be overlaid on a single plot. Default is FALSE.

colors

Character vector specifying custom color palette. If NULL, default colorblind-friendly palette is used. For array plots, the first color should be for missing values, followed by response category colors.

cell_width

Numeric value specifying the width of each cell in array plots. Default is 3.

cell_height

Numeric value specifying the height of each cell in array plots. Default is 1.

filename

Character string specifying output filename. If NULL, plot is displayed on screen. Supported formats: png, pdf, jpeg, jpg. Format determined by file extension.

width

Numeric value specifying plot width in pixels (for png/jpeg) or inches (for pdf). Default is 800.

height

Numeric value specifying plot height in pixels (for png/jpeg) or inches (for pdf). Default is 600.

dpi

Numeric value specifying resolution in dots per inch for raster formats (png/jpeg). Default is 300.

...

Additional arguments passed to plotting functions.

Details

Each model class supports specific plot types:

IRT

Supports "IRF"/"ICC", "TRF", "IIF"/"IIC", "TIF"/"TIC"

GRM

Supports "IRF"/"ICC", "IIF"/"IIC", "TIF"/"TIC"

LCA

Supports "IRP", "FRP", "TRP", "LCD", "CMP"

LRA

Supports "IRP", "FRP", "TRP", "LRD", "RMP"

LRAordinal

Supports "ScoreFreq", "ScoreRank", "ICRP", "ICBR", "RMP"

LRArated

Supports "ScoreFreq", "ScoreRank", "ICRP", "RMP"

Biclustering

Supports "FRP", "TRP", "LCD", "LRD", "CMP", "RMP", "CRV", "RRV", "Array"

IRM

Supports "FRP", "TRP", "Array"

LDLRA

Supports "IRP", "TRP", "LRD", "RMP"

LDB

Supports "FRP", "TRP", "LRD", "RMP", "Array", "FieldPIRP"

BINET

Supports "FRP", "TRP", "LRD", "RMP", "Array", "LDPSR"

Examples

Run this code
if (FALSE) {
# IRT model example
irt_result <- exametrika::IRT(J15S500)
plot(irt_result, type = "IRF", items = 1:5)
plot(irt_result, type = "TIF")

# LCA model example
lca_result <- exametrika::LCA(U)
plot(lca_result, type = "IRP")
plot(lca_result, type = "LCD")
}

# Array plot with custom output
biclustering_result <- exametrika::Biclustering(J35S515)
# Custom colors and file output
my_colors <- c("#404040", "#E69F00", "#56B4E9", "#009E73", "#F0E442")
plot(biclustering_result, type = "Array", colors = my_colors)

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