parafun {SC.MEB}

parafun.

Description

The function parafun implements the model SC-MEB for fixed number of clusters and a sequence of beta with initial value from Gaussian mixture model

Usage

parafun(
  y,
  Adj,
  G,
  beta_grid = seq(0, 4, 0.2),
  PX = TRUE,
  maxIter_ICM = 10,
  maxIter = 50
)

Arguments

y

is n-by-d PCs.

Adj

is a sparse matrix of neighborhood.

G

is an integer specifying the numbers of clusters.

beta_grid

is a numeric vector specifying the smoothness parameter of Random Markov Field. The default is seq(0,4,0.2).

PX

is a logical value specifying the parameter expansion in EM algorithm.

maxIter_ICM

is the maximum iteration of ICM algorithm. The default is 10.

maxIter

is the maximum iteration of EM algorithm. The default is 50.

Details

The function parafun implements the model SC-MEB for fixed number of clusters and a sequence of beta with initial value from Gaussian mixture model

Value

a list, We briefly explain the output of the SC.MEB.

The item 'x' storing clustering results.

The item 'gam' is the posterior probability matrix.

The item 'ell' is the opposite log-likelihood.

The item 'mu' is the mean of each component.

The item 'sigma' is the variance of each component.

Examples

y = matrix(rnorm(50, 0, 1), 25,2)
pos = cbind(rep(1:5, each=5), rep(1:5, 5))
Adj_sp = getneighborhood_fast(pos, 1.2)
beta_grid = c(0.5,1)
G = 2
out = parafun(y, Adj_sp, G, beta_grid)