parafun.

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

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.
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.
Adj_sp	is a sparse matrix of neighborhood.

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.

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