Overview

The edstan package for R provides convenience functions and pre-programmed Stan models related to item response theory (IRT). Its purpose is to make fitting common IRT models using Stan easy.

The following table lists the models packaged with edstan. Each of these may optionally included a latent regression of ability. The Stan code for these models is documented in a series of case studies, linked in the table.

Model	Stan file
Rasch	rasch_latent_reg.stan
Partical credit	pcm_latent_reg.stan
Rating scale	rsm_latent_reg.stan
Two-parameter logistic	2pl_latent_reg.stan
Generalized partial credit	gpcm_latent_reg.stan
Generalized rating scale	grsm_latent_reg.stan

The next table lists the functions packaged with edstan.

Function	Purpose
irt_data()	Prepares data for fitting
labelled_integer()	Create vector of consecutive integers
irt_stan()	Wrapper for Running MCMC
print_irt_stan()	Show table of output
rhat_columns()	Create plot of convergence statistics

Installation

edstan relies on the rstan package, which should be installed first. See here for instructions on installing rstan.

edstan is available on CRAN and may be installed with install.packages("edstan"). Alternatively, the development version may be installed directly from Github as follows.

# Install edstan development version of edstan
install.packages("devtools")
devtools::install_github("danielcfurr/edstan")

Example

The R code below is an example how prepare data, fit the Rasch model, and then view results. It uses an example dataset packaged with edstan.

# Load packages
library(rstan)
library(edstan)

# Make the data list
data_dich <- irt_data(y = aggression$dich, 
                      ii = labelled_integer(aggression$description), 
                      jj = aggression$person)

# Fit the Rasch model
fit_rasch <- irt_stan(data_dich, model = "rasch_latent_reg.stan",
                      iter = 200, chains = 4)

# View convergence statistics
rhat_columns(fit_rasch)

# View summary of parameter posteriors					  
print_irt_stan(fit_rasch, data_dich)

# Add a latent regression to the previous model
data_lr <- irt_data(y = aggression$dich, 
                    ii = labelled_integer(aggression$description), 
                    jj = aggression$person,
                    covariates = aggression[, c("male", "anger")],
                    formula = ~ 1 + male*aggression)
fit_lr <- irt_stan(data_lr, model = "rasch_latent_reg.stan",
                   iter = 200, chains = 4)