R/power.calc.R

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

#' 
#' @title Calculates the empirical and theoretical power
#' @description The function determines the empirical and theoretical power. 
#' The empirical power is the proportion of simulations in which 
#' the z-statistic for the parameter of interest exceeds the z-statistic 
#' for the desured level if statistical significance. 
#' The theoretical power is the power of the study.
#' @param pval cut-off p-value defining statistical significance.
#' @param z.values z-statistic of the determinant.
#' @param mean.model.z mean z-statistic of the environmental determinant.
#' @return a list that contains the computed empirical power and theoretical power.
#' @keywords internal
#' @author Gaye A.
#'
power.calc <- function (pval = 1e-04, z.values = NULL, mean.model.z = NULL) 
{
    if (is.null(z.values)) {
        cat("\n\n ALERT!\n")
        cat(" No z-statistics found\n")
        cat(" Check the argument 'z.values'.\n")
        stop(" End of process!\n\n", call. = FALSE)
    }
    if (is.null(mean.model.z)) {
        cat("\n\n ALERT!\n")
        cat(" The argument 'mean.model.z' is set to NULL.\n")
        cat(" This argument should be the ratio 'mean.beta/mean.se'.\n")
        stop(" End of process!\n\n", call. = FALSE)
    }
    z.pval <- qnorm(1 - pval/2)
    empirical.power <- round(mean((z.values > z.pval), na.rm = TRUE), 
        3)
    modelled.power <- pnorm(mean.model.z - z.pval)
    return(list(empirical = empirical.power, modelled = modelled.power))
}