est.lucid: Deprecated function est.lucid
In USCbiostats/LUCIDus: Latent Unknown Clustering Integrating Multi-View Data

est.lucid

R Documentation

Deprecated function est.lucid

Description

This function deprecates. Please use est_lucid instead.

Usage

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
)

Arguments

`G`	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.
`Z`	Omics data, a numeric matrix or data frame. Rows correspond to observations and columns correspond to variables.
`Y`	Outcome, a numeric vector. Categorical variable is not allowed. Binary outcome should be coded as 0 and 1.
`CoG`	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.
`CoY`	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.
`K`	Number of latent clusters. An integer greater or equal to 2. User can use `lucid` to determine the optimal number of latent clusters.
`family`	Distribution of outcome. For continuous outcome, use "normal"; for binary outcome, use "binary". Default is "normal".
`useY`	Flag to include information of outcome when estimating the latent cluster. Default is TRUE.
`tol`	Tolerance for convergence of EM algorithm. Default is 1e-3.
`max_itr`	Max number of iterations for EM algorithm.
`max_tot.itr`	Max number of total iterations for `est_lucid` function. `est_lucid` may conduct EM algorithm for multiple times if the algorithm fails to converge.
`Rho_G`	A scalar. This parameter is the LASSO penalty to regularize exposures. If user wants to tune the penalty, use the wrapper function `lucid`
`Rho_Z_Mu`	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`
`Rho_Z_Cov`	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`
`modelName`	The variance-covariance structure for omics data. See `mclust::mclustModelNames` for details.
`seed`	An integer to initialize the EM algorithm or imputing missing values. Default is 123.
`init_impute`	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.
`init_par`	Method to initialize the EM algorithm. "mclust" will use mclust model to initialize parameters; "random" initialize parameters from uniform distribution.
`verbose`	A flag indicates whether detailed information for each iteration of EM algorithm is printed in console. Default is FALSE.