R/get.obs.env.R

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

#'
#' @title Generates the observed environmental exposure data
#' @description Adds a set level of error to binary or quantitative data (the true data)
#' to obtain data with a larger variance (the observed data). The level of error is determined by
#' the misclassification rates in binary exposure and by the set level of variance in the quantitative
#' exposure.
#' @param env.data a vector of environmental measures that represents the true data.
#' @param env.model distribution of the exposure: binary=0 , normal=1 or uniform=2.
#' @param env.prev prevalence of the environmental exposure.
#' @param env.error misclassification rates: 1-sensitivity and 1-specificity.
#' @param env.reliability reliability of the assessment of quantitative exposure.
#' @return a dataframe with two coloumns/variables:
#' \code{true.environment} the error free exposure data (true data).
#' \code{observed.environment} the true esposure data with some added error (observed data).
#' @keywords internal
#' @author Amadou Gaye
#' 
get.obs.env <- function (env.data = NULL, env.model = 0, env.sd = 1, env.prev = 0.2, 
    env.error = c(0.1, 0.1), env.reliability = 0.9) 
{
    if (env.model == 0) {
        obs.env <- misclassify(binary.vector = env.data, error.1.0 = env.error[1], 
            error.0.1 = env.error[2])
    }
    else {
        var.error <- (env.sd^2/env.reliability) - env.sd^2
        numsubs <- length(env.data)
        if (env.model == 1) {
            obs.env <- rnorm(numsubs, env.data, sqrt(var.error))
        }
        else {
            env.model.error <- rnorm(numsubs, 0, sqrt(var.error))
            obs.env <- env.data + env.model.error
        }
    }
    return(obs.env)
}