R/sim.pheno.bin.E.R

In agaye/ESPRESSO.E: Power Analysis and Sample Size Calculation for a model with a main effect environmental exposure

#' 
#' @title Generates phenotype statuses
#' @description Generates affected and non-affected subjects.
#' @param num.obs number of observations to generate per iteration.
#' @param disease.prev prevalence of the binary outcome.
#' @param environment environmental exposure data.
#' @param subject.effect.data subject effect data, reflects the heterogenity in baseline disease risk.
#' @param env.OR odds ratios related to the exposure.
#' @return a vector binary vector, the phenotype data.
#' @keywords internal
#' @author Gaye A.
#'
sim.pheno.bin.E <- function (num.obs = 10000, disease.prev = 0.1, environment = NULL, 
    subject.effect.data = NULL, env.OR = 1.5) 
{
    if (is.null(environment)) {
        cat("\n\n ALERT!\n")
        cat(" No environment data found.\n")
        cat(" Check the argument 'environment'\n")
        stop(" End of process!\n\n", call. = FALSE)
    }
    if (is.null(subject.effect.data)) {
        cat("\n\n ALERT!\n")
        cat(" No baseline effect data found.\n")
        cat(" Check the argument 'subject.effect.data'\n")
        stop(" End of process!\n\n", call. = FALSE)
    }
    numobs <- num.obs
    pheno.prev <- disease.prev
    env.data <- environment
    s.efkt.data <- subject.effect.data
    e.OR <- env.OR
    alpha <- log(pheno.prev/(1 - pheno.prev))
    beta <- log(e.OR)
    lp <- alpha + beta * env.data + s.efkt.data
    mu <- exp(lp)/(1 + exp(lp))
    phenotype <- rbinom(numobs, 1, mu)
    return(phenotype)
}