LUCIDus (version 2.0.0)

boot.lucid: Bootstrap method of inference for LUCID

Description

This function provides SEs of parameter estimates from a LUCID model through bootstrap method.

Usage

boot.lucid(G, Z, Y, CoG = NULL, CoY = NULL, model, R = 100, n = detectCores())

Arguments

G

Genetic features/environmental exposures, a matrix.

Z

Biomarkers/other omics data, a matrix.

Y

Disease outcome, it is suggested to transform it into a n by 1 matrix.

CoG

Optional, matrix. Covariates to be adjusted for estimating the latent cluster.

CoY

Optional, matrix. Covariates to be adjusted for estimating the outcome.

model

A LUCID model fitted by est.lucid.

R

Number of bootstrap iterations.

n

Number of CPU cores to be used in the bootstrap

Value

A list of estimates with their 95 percent CI.

References

Cheng Peng, Jun Wang, Isaac Asante, Stan Louie, Ran Jin, Lida Chatzi, Graham Casey, Duncan C Thomas, David V Conti, A Latent Unknown Clustering Integrating Multi-Omics Data (LUCID) with Phenotypic Traits, Bioinformatics, , btz667, https://doi.org/10.1093/bioinformatics/btz667.

Examples

# NOT RUN {
fit1 <- est.lucid(G = G1, Z = Z1, Y = Y1, CoY = CovY, K = 2, family = "binary")
chk <- Sys.getenv("_R_CHECK_LIMIT_CORES_", "")
if (nzchar(chk) && chk == "TRUE") {
 # use 2 cores in CRAN/Travis/AppVeyor
 num_workers <- 2L
} else {
 num_workers <- parallel::detectCores()
}
boot1 <- boot.lucid(G = G1, Z = Z1, Y = Y1, CoY = CovY, model = fit1, R = 100, n = num_workers)
# }