Fit a lucid model for integrated analysis on exposure, outcome and multi-omics data

Fit a lucid model for integrated analysis on exposure, outcome and multi-omics data

Usage

lucid(
  G,
  Z,
  Y,
  CoG = NULL,
  CoY = NULL,
  family = "normal",
  K = 2,
  Rho_G = 0,
  Rho_Z_Mu = 0,
  Rho_Z_Cov = 0,
  verbose_tune = 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.

family

Distribution of outcome. For continuous outcome, use "normal"; for binary outcome, use "binary". Default is "normal".

K

Number of latent clusters (should be greater or equal than 2). Either an integer or a vector of integer. If K is a vector, model selection on K is performed.

Rho_G

A scalar or a vector. This parameter is the LASSO penalty to regularize exposures. If it is a vector, lucid will call tune_lucid to conduct model selection and variable selection. User can try penalties from 0 to 1.

Rho_Z_Mu

A scalar or a vector. This parameter is the LASSO penalty to regularize cluster-specific means for omics data (Z). If it is a vector, lucid will call tune_lucid to conduct model selection and variable selection. User can try penalties from 1 to 100.

Rho_Z_Cov

A scalar or a vector. This parameter is the graphical LASSO penalty to estimate sparse cluster-specific variance-covariance matrices for omics data (Z). If it is a vector, lucid will call tune_lucid to conduct model selection and variable selection. User can try penalties from 0 to 1.

verbose_tune

A flag to print details of tuning process.

...

Other parameters passed to est_lucid

Value

An optimal lucid model

Examples

if (FALSE) {
G <- sim_data$G
Z <- sim_data$Z
Y_normal <- sim_data$Y_normal
Y_binary <- sim_data$Y_binary
cov <- sim_data$Covariate

# fit lucid model
fit1 <- lucid(G = G, Z = Z, Y = Y_normal, family = "normal")
fit2 <- lucid(G = G, Z = Z, Y = Y_binary, family = "binary", useY = FALSE)

# including covariates
fit3 <- lucid(G = G, Z = Z, Y = Y_binary, family = "binary", CoG = cov)
fit4 <- lucid(G = G, Z = Z, Y = Y_binary, family = "binary", CoY = cov)

# tune K
fit5 <- lucid(G = G, Z = Z, Y = Y_binary, family = "binary", K = 2:5)

# variable selection
fit6 <- lucid(G = G, Z = Z, Y = Y_binary, family = "binary", Rho_G = seq(0.01, 0.1, by = 0.01))
fit7 <- lucid(G = G, Z = Z, Y = Y_binary, family = "binary", 
Rho_Z_Mu = seq(10, 100, by = 10), Rho_Z_Cov = 0.5,
init_par = "random", verbose_tune = TRUE)
}