SC.MEB.

SC.MEB implements the model SC-MEB, spatial clustering with hidden Markov random field using empirical Bayes.

SC.MEB(
  y,
  Adj_sp,
  beta_grid = seq(0, 4, 0.2),
  K_set = 2:10,
  parallel = TRUE,
  num_core = 5,
  PX = TRUE,
  maxIter_ICM = 10,
  maxIter = 50,
  ...
)

Arguments

y	is n-by-d PCs.
Adj_sp	is a sparse matrix of neighborhood. It is often generated from function find_neighbors2 or getneighborhood_fast.
beta_grid	is a numeric vector specifying the smoothness parameter of Random Markov Field. The default is seq(0,4,0.2).
K_set	is an integer vector specifying the numbers of mixture components (clusters) for which the BIC is to be calculated. The default is K = 2:10.
parallel	is a logical value to decide whether the function SC.MEB run in parallel. The default is TRUE.
num_core	is an integer value to decide how many cores are used to run SC.MEB in parallel.
PX	is a logical value to decide whether to use 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.

Value

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

The item 'x' contains 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

SC.MEB can implements the model SC-MEB in parallel which can improve the speed of the computation.

References

Yang Y, Shi X, Zhou Q, et al. SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes[J]. bioRxiv, 2021.