R/power.calc.R

In kwesterman/ESPRESSO.GxE.RV: Power analysis and sample size calculation for a multi-variant gene-environment interaction model

#' 
#' @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.thresh cut-off p-value defining statistical significance.
#' @param p.values Vector of p-values (one per genetic variant).
#' @return a list that contains the computed empirical power and theoretical power.
#' @keywords internal
#' @author Gaye A.; Westerman K.
#'
power.calc <- function(pval.thresh=NULL, p.values=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)
  #}
  
  ## CALCULATE Z STATISTIC THRESHOLD FOR DESIRED P-VALUE 
  #z.pval <- qnorm(1-pval.thresh/2)
  
  # GET EMPIRICAL POWER: THE PROPORTION OF SIMULATIONS IN WHICH THE 
  # Z STATISTIC FOR THE PARAMETER OF INTEREST EXCEEDS THE Z STATISTIC 
  # FOR THE DESIRED LEVEL OF STATISTICAL SIGNIFICANCE
  #empirical.power <- round(mean((z.values > z.pval), na.rm=TRUE),3)
  empirical.power <- round(mean((p.values < pval.thresh), na.rm=TRUE), 3)
  
  # # GET THE MODELLED POWER
  # modelled.power <- 1 #pnorm(mean.model.z-z.pval)
  
  return(empirical.power)  
  # return(list(empirical=empirical.power, modelled=modelled.power))
}