README

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GMLTM

The GMLTM package provides Bayesian estimation of Item Response Theory models that decompose item difficulty into cognitive operations or rules. It implements the Linear Logistic Test Model (LLTM; Fischer, 1973), the Multicomponent Latent Trait Model for Diagnosis (MLTM-D; Embretson & Yang, 2013), and the Generalized Multicomponent Latent Trait Model for Diagnosis (GMLTM-D; Ramírez et al., 2024). All models are estimated via Hamiltonian Monte Carlo using Stan through the rstan interface.

Installation

From CRAN (recommended)

install.packages("GMLTM")

From GitHub (development version)

# Install devtools if needed
install.packages("devtools")

# Install from GitHub
devtools::install_github("Eduar-Ramirez/GMLTM-D", force = TRUE)

Requirements

The package requires rstan as the Stan backend. Install it from CRAN before using GMLTM:

install.packages("rstan")

# Recommended configuration
rstan::rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())

Basic Usage

library(GMLTM)

# Load example data
data(analogy)

# Define Q-matrix (items x cognitive rules)
Q <- matrix(...)  # your Q-matrix here

# Define component structure
components <- list(
  transformation = c(1, 2, 3),
  relational     = c(4, 5)
)

# Fit GMLTM-D model
fit <- GMLTM(analogy, Q, components,
             iters = 2000, iter_warmup = 1000,
             chains = 2, cores = 2)

# Extract EAP estimates
fit$EAP$eta      # rule difficulty
fit$EAP$beta     # item difficulty
fit$EAP$alpha    # discrimination
fit$EAP$guessing # guessing parameters

# Marginal reliability
reliability(fit)

# Model fit
compute_model_validation(fit)

Custom Prior Distributions

A key feature of GMLTM is support for user-defined prior distributions via the priors argument. This enables prior sensitivity analysis — refitting models with different priors to verify that conclusions are robust.

# Conservative priors (default) — Beta(3,20) for guessing (mean ~0.13)
fit_conservative <- GMLTM(analogy, Q, components,
  iters = 2000, iter_warmup = 1000, chains = 2,
  priors = list(
    theta = list(mu = 0, sigma = 1),
    eta   = list(mu = 0, sigma = 1),
    alpha = list(mu = 0, sigma = 1),
    c     = list(shape1 = 3, shape2 = 20)
  ))

# Moderate priors — Beta(2,5) for guessing (mean ~0.29)
fit_moderate <- GMLTM(analogy, Q, components,
  iters = 2000, iter_warmup = 1000, chains = 2,
  priors = list(
    theta = list(mu = 0, sigma = 2),
    eta   = list(mu = 0, sigma = 2),
    c     = list(shape1 = 2, shape2 = 5)
  ))

# Diffuse priors — Beta(1,1) uniform for guessing
fit_diffuse <- GMLTM(analogy, Q, components,
  iters = 2000, iter_warmup = 1000, chains = 2,
  priors = list(
    theta = list(mu = 0, sigma = 5),
    eta   = list(mu = 0, sigma = 5),
    c     = list(shape1 = 1, shape2 = 1)
  ))

# Compare models using LOO-CV
loo::loo_compare(
  loo::loo(as.matrix(fit_conservative$fit, pars = "log_lik")),
  loo::loo(as.matrix(fit_moderate$fit,    pars = "log_lik")),
  loo::loo(as.matrix(fit_diffuse$fit,     pars = "log_lik"))
)

Prior parameters by model

Main Functions

References

Parameter	Distribution	Default	Models
`theta` (ability)	Normal(`mu`, `sigma`)	N(0, 1)	LLTM, MLTM, GMLTM
`eta` (rule difficulty)	Normal(`mu`, `sigma`)	N(0, 1)	LLTM, MLTM, GMLTM
`alpha` (discrimination)	Half-Normal(`sigma`)	HN(1)	MLTM, GMLTM
`c` (guessing)	Beta(`shape1`, `shape2`)	Beta(3, 20)	GMLTM only

Function	Description
`GMLTM()`	Fit the GMLTM-D model
`MLTM()`	Fit the MLTM-D model
`LLTM()`	Fit the LLTM model
`reliability()`	Marginal reliability estimation
`ppchecks()`	Posterior predictive checks (histogram)
`marginal_Pchecks()`	Marginal proportion checks with credible intervals
`compute_model_validation()`	LOO-CV and WAIC model fit indices
`plot_ICC_grouped()`	Item characteristic curves (grouped, 3×3 layout)
`plot_ICC_individual()`	Item characteristic curves (individual)
`conditional_reliability_tif()`	Conditional reliability via Test Information Function
`generate_Q_with_interactions()`	Extend Q-matrix with rule interactions

Fischer, G. H. (1973). The linear logistic test model as an instrument in educational research. Acta Psychologica, 37(6), 359–374.

Embretson, S. E., & Yang, X. (2013). A multicomponent latent trait model for diagnosis. Psychometrika, 78, 14–36.

Ramírez, E. S., Jiménez, M., Franco, V. R., & Alvarado, J. M. (2024). Delving into the complexity of analogical reasoning: A detailed exploration with the Generalized Multicomponent Latent Trait Model for Diagnosis. Journal of Intelligence, 12, 67. https://doi.org/10.3390/jintelligence12070067

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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