SC.MEB (version 1.1)

ICMEM: ICMEM.

Description

The function ICMEM was used to conduct spatial clustering with hidden Markov random field for a sequence of beta and fixed number of clusters

Usage

ICMEM(
  y,
  x_int,
  Adj,
  mu_int,
  sigma_int,
  alpha,
  beta_grid,
  PX,
  maxIter_ICM,
  maxIter
)

Arguments

y

is a matrix of PCs containing gene expression.

x_int

is a vector of initial cluster label.

Adj

is a matrix containing neighborhood information generated by find_neighbors2.

mu_int

is a initial mean vector. we often generated it by Gaussian mixture model.

sigma_int

is a initial co-variance matrix. we often generated it by Gaussian mixture model.

alpha

is a intercept.

beta_grid

is a sequence of smoothing parameter that can be specified by user.

PX

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

maxIter_ICM

is the maximum iteration of ICM algorithm.

maxIter

is the maximum iteration of EM algorithm.

Value

a list.

The item 'x' is the clustering result.

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 ICMEM was used to conduct spatial clustering with hidden Markov random field for fixed beta and fixed number of clusters

Examples

# NOT RUN {
y = matrix(rnorm(50, 0, 1), 25,2)
pos = cbind(rep(1:5, each=5), rep(1:5, 5))
Adj = getneighborhood_fast(pos, 1.2)
beta_grid = c(0.5,1)
G = 2
fit_int = Mclust(y, G = G)
x_gmm <- fit_int$classification
mu_int <- unname(fit_int$parameter$mean)
sigma_int <- unname(fit_int$parameter$variance$sigma) 
alpha <- -log(fit_int$parameter$pro)*0
reslist <- ICMEM(y = y, x_int = x_gmm, Adj = Adj, mu_int = mu_int, sigma_int = sigma_int,
alpha = alpha, beta_grid = beta_grid, 
PX = TRUE, maxIter_ICM = 10, maxIter = 50) 
# }