SC.MEB: SC.MEB.
In SC.MEB: Spatial Clustering with Hidden Markov Random Field using Empirical Bayes

Description Usage Arguments Details Value References Examples

View source: R/SC-MEB.R

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
)

`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.

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

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.

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

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)
K_set = 2:3
out = SC.MEB(y, Adj_sp, beta_grid, K_set, TRUE, 2)