boot_lucid: Inference of LUCID model based on bootstrap resampling

In LUCIDus: LUCID with Multiple Omics Data

Inference of LUCID model based on bootstrap resampling

Description

Generate R bootstrap replicates of LUCID parameters and derive confidence interval (CI) based on bootstrap. Bootstrap replicates are generated by nonparametric resampling, implemented with the ordinary method of boot::boot. Supports lucid_model = "early", lucid_model = "parallel", and lucid_model = "serial".

Usage

boot_lucid(
  G,
  Z,
  Y,
  lucid_model = c("early", "parallel", "serial"),
  CoG = NULL,
  CoY = NULL,
  model,
  conf = 0.95,
  R = 100,
  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: for LUCID early integration, a numeric matrix/data frame; for LUCID in parallel, a list of numeric matrices/data frames. 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.
lucid_model	Specifying LUCID model, "early" for early integration, "parallel" for LUCID in parallel, "serial" for LUCID in serial. Bootstrap inference is implemented for all three model types.
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.
model	A LUCID model fitted by estimate_lucid. If the fitted model uses nonzero penalties, boot_lucid will automatically refit a zero-penalty model as fallback because bootstrap inference is only supported for Rho_G = Rho_Z_Mu = Rho_Z_Cov = 0.
conf	A numeric scalar between 0 and 1 to specify confidence level(s) of the required interval(s).
R	An integer to specify number of bootstrap replicates for LUCID model. If feasible, it is recommended to set R >= 1000.
verbose	A flag indicates whether detailed information is printed in console. Default is FALSE.

Value

A list containing:

beta	Bootstrap CI table(s) for G-to-X effects. For lucid_model = "parallel", this is a list by omics layer and includes the multinomial intercept plus exposures in G (not CoG).
mu	Bootstrap CI table(s) for cluster-specific means of omics features. For lucid_model = "parallel", this is a list by omics layer.
gamma	Bootstrap CI table for X-to-Y parameters.
stage	For lucid_model = "serial", a list of stage-wise CI tables (each stage contains beta, mu, and gamma for the final stage only).
bootstrap	The boot object returned by boot::boot.

Examples

# use simulated data
G <- sim_data$G[1:300, , drop = FALSE]
Z <- sim_data$Z[1:300, , drop = FALSE]
Y_normal <- sim_data$Y_normal[1:300]

# fit lucid model
fit1 <- estimate_lucid(G = G, Z = Z, Y = Y_normal, lucid_model = "early", 
family = "normal", K = 2,
seed = 1008)

# conduct bootstrap resampling
boot1 <- suppressWarnings(
  boot_lucid(G = G, Z = Z, Y = Y_normal, 
             lucid_model = "early", model = fit1, R = 5)
)

# Use 90% CI
boot2 <- suppressWarnings(
  boot_lucid(G = G, Z = Z, Y = Y_normal, lucid_model = "early", 
             model = fit1, R = 5, conf = 0.9)
)

LUCIDus documentation built on March 11, 2026, 9:06 a.m.