Mico_bi_lasso {lnmCluster} | R Documentation |
Penalized Logistic Normal Multinomial factor analyzer main estimation process
Description
Main function will perform PLNM factor analyzer and return parameters
Usage
Mico_bi_lasso(
W_count,
G,
Q_g,
pi_g,
mu_g,
sig_g,
V,
m,
B_K,
T_K,
D_K,
cov_str,
tuning,
iter,
const,
beta_g,
X
)
Arguments
W_count |
The microbiome count matrix |
G |
All possible number of components. A vector. |
Q_g |
A specific number of latent dimension. |
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_K |
A list of initial guess of loading matrix. |
T_K |
A list of identity matrix with dimension q. |
D_K |
A list of initial guess of error matrix |
cov_str |
The covaraince structure you choose, there are 2 different models belongs to this family:UUU and GUU. You can choose more than 1 covarance structure to do model selection. |
tuning |
length G vector with range 0-1, define the tuning parameter for each component |
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 sparsity loading matrix
D_g Estimated error covariance
COV Estimated component covariance
beta_g Estimated covariates coefficients.
overall_loglik Complete log likelihood value for each iteration
ICL ICL value
BIC BIC value
AIC AIC value
tuning display the tuning parameter you specified.