Description Usage Arguments Value Author(s) References Examples
Using MCMC methods to fit the MVNOS model. Please install JAGS 3.X (http://mcmc-jags.sourceforge.net) and rjags (https://cran.r-project.org/package=rjags) at first.
1 2 | mvnos.model(y, p, Z, beta0 = NULL, A0 = NULL, alpha = NULL, P = NULL,
BURN_IN_ITERATIONS = 1000, MAX_ITERATIONS = 10000, DRAW_CYCLE = 20)
|
y |
:an n*k matrix, observed data, each row is an individual's rank of items |
p |
:number of parameters in MVNOS model |
Z |
:a n*k*p array of covariates associated with all judges |
beta0 |
:a 1*p matrix, prior normal distribution mean parameters |
A0 |
:a p*p matrix, prior normal distribution variance-covariance matrix |
alpha |
:scalar, prior Wishart distribution degree of freedom |
P |
:a (k-1)*(k-1) matrix, prior Wishart distribution scale matrix |
BURN_IN_ITERATIONS |
:number of iterations to burn-in at first |
MAX_ITERATIONS |
:full sample iterations |
DRAW_CYCLE |
:reduce the full sample by draw-cycle(e.g. draw every 20th draw from the full sample) |
A list of Gibbs sampling traces
Li Qinglong <liqinglong0830@163.com>
Yu, P. L. H. (2000). Bayesian analysis of order-statistics models for ranking data. Psychometrika, 65(3):281-299.
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 | # APA data application
# It will take about 10 minutes to run the demo.
data(APA)
y = freq2case(APA, freq.col = 1)
y = 6 - y
# number of observed judges
n = dim(y)[1]
# number of items
k = dim(y)[2]
# number of parameteros of beta
p = k
beta0 = rep(0, p)
alpha = k + 1
A0 = diag(100, ncol = p, nrow = p)
P = diag(k + 1, ncol = k - 1, nrow = k - 1)
# Construct Z
Z = array(0, dim = c(n, k, p))
for (j in 1:n)
{
Z[j, , ] = diag(1, nrow= k, ncol = p)
}
# Total iterations of Gibbs sampling
MAX_ITERATIONS = 10000
# Number of iterations to be reduced(burnt in)
BURN_IN_ITERATIONS = 1000
# Run the model, time consuming
# output_list = mvnos.model(y, p, Z, beta0, A0, alpha, P,
# MAX_ITERATIONS = MAX_ITERATIONS, BURN_IN_ITERATIONS = BURN_IN_ITERATIONS)
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