boot_lucid {LUCIDus}

Inference of LUCID model based on bootstrap resampling

Description

Generate R bootstrap replicates of LUCID parameters and derive confidence interval (CI) base on bootstrap. Bootstrap replicates are generated based on nonparameteric resampling, implemented by ordinary method of boot::boot function. Now only achieved for LUCID early integration.

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 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.

lucid_model

Specifying LUCID model, "early" for early integration, "parallel" for lucid in parallel, "serial" for LUCID in serial.Now only work for LUCID early. If "parallel" or "serial", the function will do nothing.

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.

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 the following components:

beta

effect estimate for each exposure

mu

cluster-specific mean for each omics feature

gamma

effect estiamte for the association btween latent cluster and outcome

bootstrap

The boot object returned by boot:boot

Examples

# use simulated data
G <- sim_data$G
Z <- sim_data$Z
Y_normal <- sim_data$Y_normal

# 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 <- boot_lucid(G = G, Z = Z, Y = Y_normal, 
lucid_model = "early",model = fit1, R = 100)

# use 90% CI
boot2 <- boot_lucid(G = G, Z = Z, Y = Y_normal, lucid_model = "early", 
model = fit1, R = 100, conf = 0.9)