Visualization of 'exametrika' Output Using 'ggplot2'.

ggExametrika

ggExametrika provides ggplot2-based visualization for the exametrika package. It supports a wide range of psychometric models:

Reference

Shojima, Kojiro (2022) Test Data Engineering: Latent Rank Analysis, Biclustering, and Bayesian Network (Behaviormetrics: Quantitative Approaches to Human Behavior, 13), Springer, ISBN 978-981-16-9985-6

Installation

# install.packages("devtools")
devtools::install_github("kosugitti/ggExametrika")

Examples

All plot functions take exametrika output directly and return ggplot objects. Functions are named plotXXX_gg().

IRT: Item Characteristic Curve (plotICC_gg)

library(exametrika)
library(ggExametrika)

result_irt <- IRT(J15S500, model = 3)
plots <- plotICC_gg(result_irt)
plots[[5]]

combinePlots_gg(plots)

IRT: Overlay Plots (plotICC_overlay_gg, plotIIC_overlay_gg)

# All ICCs on a single plot
plotICC_overlay_gg(result_irt, show_legend = TRUE)

# All IICs on a single plot (also works with GRM)
plotIIC_overlay_gg(result_irt, items = c(1, 3, 5), show_legend = TRUE)

IRT: Item Information Curve (plotIIC_gg)

plots <- plotIIC_gg(result_irt)
combinePlots_gg(plots, selectPlots = 8:11)

IRT: Test Information Curve / Test Response Function (plotTIC_gg, plotTRF_gg)

plotTIC_gg(result_irt)
plotTRF_gg(result_irt)

GRM: Item Category Response Function (plotICRF_gg)

result_grm <- GRM(J5S1000)
plots <- plotICRF_gg(result_grm)
plots[[1]]
combinePlots_gg(plots, selectPlots = 1:5)

# GRM also supports IIC and TIC
plotIIC_gg(result_grm)
plotTIC_gg(result_grm)

LCA: Latent Class Analysis

result_lca <- LCA(J15S500, ncls = 3)

plotIRP_gg(result_lca)         # Item Reference Profile
plotFRP_gg(result_lca)         # Field Reference Profile
plotTRP_gg(result_lca)         # Test Reference Profile
plotLCD_gg(result_lca)         # Latent Class Distribution
plotCMP_gg(result_lca)         # Class Membership Profile

LRA: Latent Rank Analysis

result_lra <- LRA(J15S500, nrank = 4)

plotIRP_gg(result_lra)         # Item Reference Profile
plotFRP_gg(result_lra)         # Field Reference Profile
plotTRP_gg(result_lra)         # Test Reference Profile
plotLRD_gg(result_lra)         # Latent Rank Distribution
plotRMP_gg(result_lra)         # Rank Membership Profile

LRAordinal / LRArated

result_lra_ord <- LRA(J5S1000, nrank = 4)  # ordinal data

plotScoreFreq_gg(result_lra_ord)   # Score Frequency Distribution
plotScoreRank_gg(result_lra_ord)   # Score-Rank Heatmap
plotICRP_gg(result_lra_ord)        # Item Category Reference Profile
plotICBR_gg(result_lra_ord)        # Item Category Boundary Response (ordinal only)
plotRMP_gg(result_lra_ord)         # Rank Membership Profile

Biclustering (binary)

result_bic <- Biclustering(J35S515, nfld = 5, nrank = 6)

plotFRP_gg(result_bic)         # Field Reference Profile
plotTRP_gg(result_bic)         # Test Reference Profile
plotLCD_gg(result_bic)         # Latent Class Distribution
plotLRD_gg(result_bic)         # Latent Rank Distribution
plotCMP_gg(result_bic)         # Class Membership Profile
plotRMP_gg(result_bic)         # Rank Membership Profile
plotCRV_gg(result_bic)         # Class Reference Vector
plotRRV_gg(result_bic)         # Rank Reference Vector
plotArray_gg(result_bic)       # Array Plot (heatmap)

Biclustering (nominal / ordinal)

# Nominal Biclustering
result_nom <- Biclustering(data, ncls = 3, nfld = 4)

plotFRP_gg(result_nom, stat = "mean")   # stat: "mean", "median", or "mode"
plotFCRP_gg(result_nom, style = "line") # Field Category Response Profile (style: "line" or "bar")
plotScoreField_gg(result_nom)           # Expected Score Heatmap (field x class/rank)
plotCRV_gg(result_nom, stat = "mean")   # Class Reference Vector
plotRRV_gg(result_nom, stat = "mean")   # Rank Reference Vector
plotArray_gg(result_nom)                # Array Plot

# Ordinal Biclustering (additional)
plotFCBR_gg(result_ord)                 # Field Cumulative Boundary Reference (ordinal only)

LDB: Locally Dependent Biclustering

result_ldb <- LDB(J35S515, ncls = 6, nfld = 5)

plotFRP_gg(result_ldb)         # Field Reference Profile
plotTRP_gg(result_ldb)         # Test Reference Profile
plotLRD_gg(result_ldb)         # Latent Rank Distribution
plotRMP_gg(result_ldb)         # Rank Membership Profile
plotArray_gg(result_ldb)       # Array Plot
plotFieldPIRP_gg(result_ldb)   # Field Parent Item Reference Profile
plotGraph_gg(result_ldb)       # DAG per rank

BINET: Bayesian Network and Test

result_binet <- BINET(J35S515, ncls = 6, nfld = 5)

plotFRP_gg(result_binet)       # Field Reference Profile
plotTRP_gg(result_binet)       # Test Reference Profile
plotLRD_gg(result_binet)       # Latent Rank Distribution
plotRMP_gg(result_binet)       # Rank Membership Profile
plotArray_gg(result_binet)     # Array Plot
plotGraph_gg(result_binet, show_edge_label = TRUE)  # DAG with edge labels

BNM / LDLRA: DAG Visualization (plotGraph_gg)

result_bnm <- BNM(J15S500)
plotGraph_gg(result_bnm)

result_ldlra <- LDLRA(J15S500, ncls = 5)
plotGraph_gg(result_ldlra)     # One DAG per rank

Function-Model Compatibility

IRT / GRM

LCA / LRA / LRAordinal / LRArated

Biclustering / IRM

Network Models (LDLRA / LDB / BINET / BNM)

Utility

Common Plot Options

All plot functions support these customization options:

Some functions have additional parameters:

Documentation

• Getting Started (English)
• Function Reference.