Mico_bi_jensens {lnmCluster}

run main microbiome bicluster algorithm.

Description

run main microbiome bicluster algorithm.

Usage

Mico_bi_jensens(
  W_count,
  G,
  Q_g,
  pi_g,
  mu_g,
  sig_g,
  V,
  m,
  B_g,
  T_g,
  D_g,
  cov_str,
  iter,
  const,
  beta_g,
  X
)

Arguments

W_count

The microbiome count matrix that you want to analyze.

G

The number of component

Q_g

The number of biclusters for each component, a vector.

pi_g

A vector of initial guesses of component proportion

mu_g

A list of initial guess of mean vector

sig_g

A list of initial guess of covariance matrix for each component

V

A list of initial guess of variational varaince

m

A list of initial guess of variational mean

B_g

A list of initial guess of bicluster membership

T_g

A list of initial guess of covariance of latent variable: u

D_g

A list of initial guess of error matrix

cov_str

The covaraince structure you choose, there are 16 different models belongs to this family:UUU, UUG, UUD, UUC, UGU, UGG, UGD, UGC, GUU, GUG, GUD, GUC, GGU, GGG, GGD, GGC.

iter

Max iterations, default is 150.

const

the permutation constant in multinomial distribution. Calculated before the main algorithm in order to save computation time.

beta_g

initial guess of covariates coefficients.

X

The regression covariates matrix, which generates by model.matrix.

Value

z_ig Estimated latent variable z

cluster Component labels

mu_g Estimated component mean

pi_g Estimated component proportion

B_g Estimated bicluster membership

T_g Estimated covariance of latent variable u

D_g Estimated error covariance

COV Estimated sparsity component covariance

beta_g Estimated covariates coefficients.

sigma Estimated original component covariance

overall_loglik Complete log likelihood value for each iteration

ICL ICL value

BIC BIC value

AIC AIC value