Description Usage Arguments Value Author(s) References See Also Examples
View source: R/rrumestimation.R
Obtains samples from posterior distributon for the reduced Reparametrized Unified Model (rRUM).
1 2 
Y 
A 
Q 
A 
chain_length 
A 
as 
A 
bs 
A 
ag 
A 
bg 
A 
delta0 
A 
A list
that contains
PISTAR
: A matrix
where each column represents one draw from the
posterior distribution of pistar.
RSTAR
: A J x K x chain_length array
where J
reperesents the
number of items, and K
represents the number of attributes.
Each slice represents one draw from the posterior distribution
of rstar
.
PI
: A matrix
where each column reperesents one draw from the posterior
distribution of pi
.
ALPHA
: An N x K x chain_length array
where N
reperesents the
number of individuals, and K
represents the number of
attributes. Each slice represents one draw from the posterior
distribution of alpha
.
Steven Andrew Culpepper, Aaron Hudson, and James Joseph Balamuta
Culpepper, S. A. & Hudson, A. (In Press). An improved strategy for Bayesian estimation of the reduced reparameterized unified model. Applied Psychological Measurement.
Hudson, A., Culpepper, S. A., & Douglas, J. (2016, July). Bayesian estimation of the generalized NIDA model with Gibbs sampling. Paper presented at the annual International Meeting of the Psychometric Society, Asheville, North Carolina.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52  # Set seed for reproducibility
set.seed(217)
## Define Simulation Parameters
N = 1000 # Number of Individuals
J = 6 # Number of Items
K = 2 # Number of Attributes
# Matrix where rows represent attribute classes
As = attribute_classes(K)
# Latent Class probabilities
pis = c(.1, .2, .3, .4)
# Q Matrix
Q = rbind(c(1, 0),
c(0, 1),
c(1, 0),
c(0, 1),
c(1, 1),
c(1, 1)
)
# The probabiliies of answering each item correctly for individuals
# who do not lack any required attribute
pistar = rep(.9, J)
# Penalties for failing to have each of the required attributes
rstar = .5 * Q
# Randomized alpha profiles
alpha = As[sample(1:(K ^ 2), N, replace = TRUE, pis),]
# Simulate data
rrum_items = simcdm::sim_rrum_items(Q, rstar, pistar, alpha)
## Not run:
# Note: This portion of the code is computationally intensive.
# Recover simulation parameters with Gibbs Sampler
Gibbs.out = rrum(rrum_items, Q)
# Iterations to be discarded from chain as burnin
burnin = 1:5000
# Calculate summarizes of posterior distributions
rstar.mean = with(Gibbs.out, apply(RSTAR[,,burnin], c(1, 2), mean))
pistar.mean = with(Gibbs.out, apply(PISTAR[,burnin], 1, mean))
pis.mean = with(Gibbs.out, apply(PI[,burnin], 1 ,mean))
## End(Not run)

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