Estimate the Deterministic Input, Noisy "And" Gate (DINA)
cognitive diagnostic model parameters using the Gibbs sampler described
by Culpepper (2015)
Estimate the Deterministic Input, Noisy And Gate (DINA) cognitive diagnostic model parameters using the Gibbs sampler described by Culpepper (2015) <doi: 10.3102/1076998615595403>.
You can install dina
from CRAN using:
install.packages("dina")
Or, you can be on the cutting-edge development version on GitHub using:
if(!requireNamespace("devtools")) install.packages("devtools")devtools::install_github("tmsalab/dina")
To use the dina
package, load it into R using:
library("dina")
From there, the DINA CDM can be estimated using:
dina_model = dina(<data>, <q>, chain_length = 10000)
To simulate item data under DINA, use:
# Set a seed for reproducibilityset.seed(888)# Setup ParametersN = 15 # Number of Examinees / SubjectsJ = 10 # Number of ItemsK = 2 # Number of Skills / Attributes# Assign slipping and guessing values for each itemss = gs = rep(.2, J)# Simulate identifiable Q matrixQ = sim_q_matrix(J, K)# Simulate subject attributessubject_alphas = sim_subject_attributes(N, K)# Item dataitems_dina = sim_dina_items(subject_alphas, Q, ss, gs)
Steven Andrew Culpepper and James Joseph Balamuta
dina
packageTo ensure future development of the package, please cite dina
package
if used during an analysis or simulations. Citation information for the
package may be acquired by using in R:
citation("dina")
GPL (>= 2)
DINA_Gibbs()
in favor of dina()
, which generates the correct
alpha matrix (Amat
) inside of the function instead of relying on the user
to set it up.dina(Y, Q, chain_length)
simcdm
C++ routines
and imported R level-routines.src/init.c
to autogeneration via Rcpp 0.12.15dina
package.