initial_variational_lasso {lnmCluster}R Documentation

Gives default initial guesses for penalized logistic-normal multinomial Factor analyzer algorithm.

Description

Gives default initial guesses for penalized logistic-normal multinomial Factor analyzer algorithm.

Usage

initial_variational_lasso(W_count, G, Q_g, cov_str, X)

Arguments

W_count

The microbiome count matrix that you want to analyze.

G

The number of component

Q_g

A specific number of latent dimension.

cov_str

The covaraince structure you choose, there are 2 different models belongs to this family:UUU, GUU.

X

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

Value

new_pi_g Initial guess of proportion

new_mu_g Initial guess of mean vector

new_sig_g Initial guess of covariance matrix for each component

new_B_g Initial guess of loading matrix.

new_T_g The identity matrix of latent variable: u

new_D_g Initial guess of error matrix

new_m Initial guess of variational mean

new_V Initial guess of variational varaince

new_beta_g Initial guess of covariates coefficients.


[Package lnmCluster version 0.3.1 Index]