| Title: | Exploratory Reduced Reparameterized Unified Model Estimation |
| Version: | 0.0.3 |
| Description: | Perform a Bayesian estimation of the exploratory reduced reparameterized unified model (ErRUM) described by Culpepper and Chen (2018) <doi:10.3102/1076998618791306>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/tmsalab/errum |
| BugReports: | https://github.com/tmsalab/errum/issues |
| Depends: | R (≥ 3.5.0) |
| Imports: | Rcpp (≥ 1.0.0) |
| LinkingTo: | Rcpp, RcppArmadillo (≥ 0.9.200) |
| Suggests: | simcdm |
| LazyData: | true |
| RoxygenNote: | 7.1.0 |
| Encoding: | UTF-8 |
| Language: | en-US |
| NeedsCompilation: | yes |
| Packaged: | 2020-03-20 03:16:08 UTC; ronin |
| Author: | James Joseph Balamuta
 [aut, cre,
cph],
Steven Andrew Culpepper
[aut, cph],
Jeffrey A. Douglas [aut] |
| Maintainer: | James Joseph Balamuta <balamut2@illinois.edu> |
| Repository: | CRAN |
| Date/Publication: | 2020-03-20 09:50:05 UTC |
errum: Exploratory Reduced Reparameterized Unified Model Estimation
Description
Perform a Bayesian estimation of the exploratory reduced reparameterized unified model (ErRUM) described by Culpepper and Chen (2018) <doi:10.3102/1076998618791306>.
Author(s)
Maintainer: James Joseph Balamuta balamut2@illinois.edu (ORCID) [copyright holder]
Authors:
Steven Andrew Culpepper sculpepp@illinois.edu (ORCID) [copyright holder]
Jeffrey A. Douglas jeffdoug@illinois.edu
See Also
Useful links:
Exploratory reduced Reparameterized Unified Model (ErRUM)
Description
Obtains samples from posterior distribution for the Exploratory reduced Reparameterized Unified Model (ErRUM).
Usage
errum(
y,
k = 3,
burnin = 1000,
chain_length = 10000,
verbose = FALSE,
X = matrix(1, nrow = ncol(y)),
v0 = 4,
v1 = 2,
cv0 = 0.1,
cv1 = 10,
bnu = 16
)
Arguments
y |
Binary responses to assessments in |
k |
Number of Attribute Levels as a positive |
burnin |
Number of Observations to discard on the chain. |
chain_length |
Length of the MCMC chain |
verbose |
Display estimation progress updates. |
X, v0, v1, cv0, cv1, bnu |
Additional tuning parameters |
Value
An errum object that has:
-
PISTAR -
RSTAR -
PIs -
QS -
m_Delta -
Delta_biject -
M2 -
M1 -
NUS
See Also
simcdm::attribute_bijection(),
simcdm::sim_rrum_items()
Examples
# Setup Simulation Parameters
N = 5
K = 3
J = 30
# Note:
# Sample size has been reduced to create a minimally
# viable example that can be run during CRAN's automatic check.
# Please make sure to have a larger sample size of around 3,000.
# Sample true attribute profiles
Z = matrix(rnorm(N * K), N, K)
Sig = matrix(.5, K, K)
diag(Sig) = 1
theta = Z %*% chol(Sig)
thvals = matrix(qnorm((1:K) / (K + 1)),
N, K, byrow = TRUE)
Alphas = 1 * (theta > thvals)
# Defining matrix of possible attribute profiles
As = as.matrix(expand.grid(c(0, 1), c(0, 1), c(0, 1)))
Q = rbind(As[rep(c(2, 3, 5), 4),],
As[rep(c(4, 6, 7), 4),],
As[rep(8, 6),])
# Use simulation functions available in simcdm
if (requireNamespace("simcdm", quietly = TRUE)) {
a = As %*% simcdm::attribute_bijection(K)
As = As[a + 1,]
# Setting item parameters
pistar = rep(.9, J)
rstar = matrix(.6, J, K) * Q
# Simulate data under rRUM model
Y = simcdm::sim_rrum_items(Q, rstar, pistar, Alphas)
# Estimation Settings
chainLength = 10000 # Run with 20000
burnin = chainLength / 2
# Gibbs Estimation
model = errum(Y, K, burnin, chainLength)
}