This function deprecates. Please use est_lucid instead.
est.lucid(
G,
Z,
Y,
CoG = NULL,
CoY = NULL,
K = 2,
family = c("normal", "binary"),
useY = TRUE,
tol = 0.001,
max_itr = 1000,
max_tot.itr = 10000,
Rho_G = 0,
Rho_Z_Mu = 0,
Rho_Z_Cov = 0,
modelName = "VVV",
seed = 123,
init_impute = c("mclust", "lod"),
init_par = c("mclust", "random"),
verbose = FALSE
)Exposures, a numeric vector, matrix, or data frame. Categorical variable should be transformed into dummy variables. If a matrix or data frame, rows represent observations and columns correspond to variables.
Omics data, a numeric matrix or data frame. Rows correspond to observations and columns correspond to variables.
Outcome, a numeric vector. Categorical variable is not allowed. Binary outcome should be coded as 0 and 1.
Optional, covariates to be adjusted for estimating the latent cluster. A numeric vector, matrix or data frame. Categorical variable should be transformed into dummy variables.
Optional, covariates to be adjusted for estimating the association between latent cluster and the outcome. A numeric vector, matrix or data frame. Categorical variable should be transformed into dummy variables.
Number of latent clusters. An integer greater or equal to 2. User
can use lucid to determine the optimal number of latent clusters.
Distribution of outcome. For continuous outcome, use "normal"; for binary outcome, use "binary". Default is "normal".
Flag to include information of outcome when estimating the latent cluster. Default is TRUE.
Tolerance for convergence of EM algorithm. Default is 1e-3.
Max number of iterations for EM algorithm.
Max number of total iterations for est_lucid function.
est_lucid may conduct EM algorithm for multiple times if the algorithm
fails to converge.
A scalar. This parameter is the LASSO penalty to regularize
exposures. If user wants to tune the penalty, use the wrapper
function lucid
A scalar. This parameter is the LASSO penalty to
regularize cluster-specific means for omics data (Z). If user wants to tune the
penalty, use the wrapper function lucid
A scalar. This parameter is the graphical LASSO
penalty to estimate sparse cluster-specific variance-covariance matrices for omics
data (Z). If user wants to tune the penalty, use the wrapper function lucid
The variance-covariance structure for omics data.
See mclust::mclustModelNames for details.
An integer to initialize the EM algorithm or imputing missing values. Default is 123.
Method to initialize the imputation of missing values in
LUCID. "mclust" will use mclust:imputeData to implement EM Algorithm
for Unrestricted General Location Model to impute the missing values in omics
data; lod will initialize the imputation via relacing missing values by
LOD / sqrt(2). LOD is determined by the minimum of each variable in omics data.
Method to initialize the EM algorithm. "mclust" will use mclust model to initialize parameters; "random" initialize parameters from uniform distribution.
A flag indicates whether detailed information for each iteration of EM algorithm is printed in console. Default is FALSE.