View source: R/sampling_Omega_ij.R
sampling_Omega_ij | R Documentation |
Generates a sample from the the posterior distribution of the (i,j) element of the Ω matrix in the mixdpcluster model for bayesian clustering. The simulation is done via Metropolis-Hastings method.
sampling_Omega_ij( n = 1, Omega.ini, i, j, delta = 4, Z, mu_Z, Lambda, sampling_prob, n.burn = 0, n.thin = 0, max.time = Inf, verbose = F, USING_CPP = USING_CPP )
n |
number of simulations to be generated |
Omega.ini |
matrix Ω with an initialization value for Ω_{i,j}. |
i |
indicates the row for Ω_{i,j} |
j |
indicates the column for Ω_{i,j} |
delta |
defines the maximum jump on each iteration of the MCMC as 1/delta of the feasible interval for Ω_{i,j} |
Z |
Latent continuous variables |
mu_Z |
Mean of the continuous latent variables |
Lambda |
Standard deviations in the decomposition of the covariance matrix |
sampling_prob |
sampling probabilities for each observation, for complex surveys. |
n.burn |
number of iterations in the simulation considered in the burn-in period. |
n.thin |
number of iterations discarded between two simulated values (for thinning of the MCMC chain). |
max.time |
maximum allowed time for the simulation process. The function returns |
verbose |
if |
A list with two elements:
A numeric vector with the simulated values from the posterior distribution of Ω_{i,j} .
A logical vector indicating whether or not omega_ij.prop
was accepted.
Carmona C., Nieto-Barajas L., Canale A. (2017). Model based approach for household clustering with mixed scale variables.
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