DGP_OC: Data Generating Process (Omics Data versus Compositional...

In MicrobiomeStat: Statistical Methods for Microbiome Compositional Data

Data Generating Process (Omics Data versus Compositional data)

Description

Data Generating Process (Omics Data versus Compositional data)

Usage

DGP_OC(seed = 10, n, p, q, sigma.nu, sigma.eps, omega_X, omega_Y)

Arguments

seed	an integer for the initial seed.
n	an integer representing the sample size.
p	an integer representing the feature size of the omics dataset.
q	an integer representing the feature size of the compositional dataset.
sigma.nu	a numerial value representing the strength of correlation.
sigma.eps	a numerical value representing the strength of noise.
omega_X	a p vector representing the coefficient for the omics data.
omega_Y	a q vector representing the coefficient for the compositional data.

Value

A list containing the following elements: (a) Y: a n*(2p) matrix representing the full observations; (b) View.ind: a 2p integer vector indicating the classes of features. The features with the same View.ind is in the same class; (c) omega a 2p vector representing the true coefficients.

Examples

library(dplyr)
n <- 200
p <- q <- 100
sigma.nu <- 5
sigma.eps <- 1
omega_X <- 0.85*c(rep(1/10,9),-9/10,rep(0,p-10))
omega_Y <- 0.85*c(seq(0.08,0.12,length = 10),rep(0,q-10))
Data1 <- DGP_OC(seed=10,n,p,q,sigma.nu,sigma.eps,omega_X,omega_Y)

MicrobiomeStat documentation built on Jan. 9, 2026, 1:07 a.m.