The goal of
rrum is to provide an implementation of Gibbs sampling
algorithm for Bayesian Estimation of Reduced Reparameterized Unified
Model (rrum), described by Culpepper and Hudson (2017) \<doi:
You can install
rrum from CRAN using:
Or, you can be on the cutting-edge development version on GitHub using:
if(!requireNamespace("devtools")) install.packages("devtools") devtools::install_github("tmsalab/rrum")
rrum, load the package using:
library("rrum") #> Loading required package: simcdm
From here, the rRUM model can be estimated using:
rrum_model = rrum(<data>, <q>)
Additional parameters can be accessed with:
rrum_model = rrum(<data>, <q>, chain_length = 10000L, as = 1, bs = 1, ag = 1, bg = 1, delta0 = rep(1, 2^ncol(Q)))
rRUM item data can be simulated using:
# Set a seed for reproducibility set.seed(888) # Setup Parameters N = 15 # Number of Examinees / Subjects J = 10 # Number of Items K = 2 # Number of Skills / Attributes # Simulate identifiable Q matrix Q = sim_q_matrix(J, K) # Penalties for failing to have each of the required attributes rstar = .5 * Q # The probabilities of answering each item correctly for individuals # who do not lack any required attribute pistar = rep(.9, J) # Latent Class Probabilities pis = c(.1, .2, .3, .4) # Generate latent attribute profile with custom probability (N subjects by K skills) subject_alphas = sim_subject_attributes(N, K, prob = pis) # Simulate rrum items rrum_items = simcdm::sim_rrum_items(Q, rstar, pistar, subject_alphas)
Steven Andrew Culpepper, Aaron Hudson, and James Joseph Balamuta
To ensure future development of the package, please cite
if used during an analysis or simulation study. Citation information for
the package may be acquired by using in R:
GPL (>= 2)
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