Description Usage Arguments Details Note Author(s) References See Also
This function implements the collapsed allocation sampler of Nobile and Fearnside (2007) at the context of mixtures of multivariate Bernoulli distributions.
1 2 3  allocationSamplerBinMix(Kmax, alpha, beta, gamma, m, burn, data,
thinning, z.true, ClusterPrior, ejectionAlpha, Kstart, outputDir,
metropolisMoves, reorderModels, heat, zStart, LS, rsX, originalX, printProgress)

Kmax 
Maximum number of clusters (integer, at least equal to two). 
alpha 
First shape parameter of the Beta prior distribution (strictly positive). Defaults to 1. 
beta 
Second shape parameter of the Beta prior distribution (strictly positive). Defaults to 1. 
gamma 

m 
Number of MCMC iterations. 
burn 
The number of initial MCMC iterations that will be discarded as burnin period. 
data 
Binary data array (NAs not allowed here). 
thinning 
Integer that defines a thinning of the reported MCMC sample. Under the default setting, every 5th MCMC iteration is saved. 
z.true 
An optional vector of cluster assignments considered as the groundtruth clustering of the observations. Useful for simulations. 
ClusterPrior 
Character string specifying the prior distribution of the number of clusters on the set \{1,…,K_{max}\}. Available options: 
ejectionAlpha 
Probability of ejecting an empty component. Defaults to 0.2. 
Kstart 
Initial value for the number of clusters. Defaults to 1. 
outputDir 
The name of the produced output folder. 
metropolisMoves 
A vector of character strings with possible values 
reorderModels 
Character string specifying whether to postprocess the MCMC sample of each distinct generated value of 
heat 
The temperature of the simulated chain, that is, a scalar in the set (0,1]. 
zStart 
ndimensional integer vector of latent allocations to initialize the sampler. 
LS 
Boolean indicating whether to postprocess the MCMC sample using the label switching algorithms. 
rsX 
Optional vector containing the rowsums of the observed data (NAs are allowed). It is required only in the case of missing values. 
originalX 
Optional array containing the observed data (containing NAs). It is required only in the case of missing values. 
printProgress 
Logical, indicating whether to print the progress of the sampler or not. Default: FALSE. 
The output is reordered according to the following labelswitching solving algorithms: ECR, ECRITERATIVE1 and STEPHENS. In most cases the results of these different algorithms are identical.
This function is recursively called inside the coupledMetropolis
function. There is no need to call it separately.
Panagiotis Papastamoulis
Nobile A and Fearnside A (2007): Bayesian finite mixtures with an unknown number of components: The allocation sampler. Statistics and Computing, 17(2): 147162.
Papastamoulis P. and Iliopoulos G. (2010). An artificial allocations based solution to the label switching problem in Bayesian analysis of mixtures of distributions. Journal of Computational and Graphical Statistics, 19: 313331.
Papastamoulis P. and Iliopoulos G. (2013). On the convergence rate of Random Permutation Sampler and ECR algorithm in missing data models. Methodology and Computing in Applied Probability, 15(2): 293304.
Papastamoulis P. (2014). Handling the label switching problem in latent class models via the ECR algorithm. Communications in Statistics, Simulation and Computation, 43(4): 913927.
Papastamoulis P (2016): label.switching: An R package for dealing with the label switching problem in MCMC outputs. Journal of Statistical Software, 69(1): 124.
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