Inference of LUCID model based on bootstrap resampling

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 codeboot::boot function.

Usage

boot_lucid(G, Z, Y, CoG = NULL, CoY = NULL, model, conf = 0.95, R = 100)

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.

model

A LUCID model fitted by est.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.

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

if (FALSE) {
# use simulated data
G <- sim_data$G
Z <- sim_data$Z
Y_normal <- sim_data$Y_normal

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

# conduct bootstrap resampling
boot1 <- boot_lucid(G = G, Z = Z, Y = Y_normal, model = fit1, R = 100)

# check distribution for bootstrap replicates of the variable of interest
plot(boot1$bootstrap, 1)

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