CRPClustering (version 1.2)

crp_gibbs: Markov chain Monte Carlo methods for CRP clustering

Description

Markov chain Monte Carlo methods for CRP clustering

Usage

crp_gibbs(data, mu = c(0, 0), sigma_table = 14, alpha = 0.3, ro_0 = 0.1,
  burn_in = 40, iteration = 200)

Arguments

data

: a matrix of data for clustering. Row is each data_i and column is dimensions of each data_i.

mu

: a vector of center points of data. If data is 3 dimensions, a vector of 3 elements like c(2,4,2).

sigma_table

: a numeric of CRP variance.

alpha

: a numeric of a CRP concentrate rate.

ro_0

: a numeric of a CRP mu change rate.

burn_in

: an iteration integer of burn in.

iteration

: an iteration integer.

Value

z_result : an array expresses cluster numbers for each data_i.

Examples

# NOT RUN {
data <- matrix(c(1.8,1.9,2.1,2.5,5.6,5.2,6,6.1), 4, 2)
z_result <- crp_gibbs(
                      data,
                      mu=c(0,0),
                      sigma_table=14,
                      alpha=0.3,
                      ro_0=0.1,
                      burn_in=10,
                      iteration=100
                     )
# }