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#' @title Summarizing Initial Sample Size Estimates
#' @description \code{summary} method for class "\code{ssest}".
#'
#' @param object an object of class "\code{ssest}".
#' @param ... Arguments to be passed to or from other methods.
#'
#' @details
#' \code{summary.ssest} gives back initial sample size estimates required. Furthermore, inputs are displayed for double checking.
#'
#' @seealso \code{\link{n.nb.inar1}} for initial sample size estimates within the NB-INAR(1) model.
#'
#' @examples
#' #Calculate required sample size to find significant difference with
#' #80% probability when testing the Nullhypothesis H_0: mu_T/mu_C >= 1
#' #assuming the true effect delta is 0.8 and rate, size and correlation
#' #parameter in the control group are 2, 1 and 0.5, respectively.
#'
#' estimate<-n.nb.inar1(alpha=0.025, power=0.8, delta=0.8, muC=2, size=1, rho=0.5, tp=7, k=1)
#' summary(estimate)
#' @export
summary.ssest<-function(object, ...){
if(object$model=="NB-INAR(1)"){
cat("Initial Sample Size Calculation")
cat("\n")
cat("---------------------------------")
cat("\n")
cat(paste("alpha level: ", object$alpha))
cat("\n")
cat(paste("testing power: ", round(object$power,2)))
cat("\n")
cat(paste("rate ratio: ", object$delta))
cat("\n")
cat(paste("rate control group ", object$muC))
cat("\n")
cat(paste("dispersion parameter: ", object$size))
cat("\n")
cat(paste("correlation parameter:", object$rho))
cat("\n")
cat(paste("time points: ", object$tp))
cat("\n")
cat(paste("allocation factor: ", object$k))
cat("\n\n")
cat("Sample Size")
cat("\n")
cat("---------------------------------")
cat("\n")
cat(paste("control group: ", round(object$n[1],2)))
cat("\n")
cat(paste("treatment group:"), round(object$n[2],2))
}
if(object$model=="GF"){
cat("Initial Sample Size Calculation")
cat("\n")
cat("---------------------------------")
cat("\n")
cat(paste("alpha level: ", object$alpha))
cat("\n")
cat(paste("testing power: ", round(object$power,2)))
cat("\n")
cat(paste("trend type: ", object$trend))
cat("\n")
cat(paste(c("trend parameters: ", object$lambda)))
cat("\n")
cat(paste("dispersion parameter: ", object$size))
cat("\n")
cat(paste("correlation parameter:", object$rho))
cat("\n")
cat(paste("time points: ", object$tp))
cat("\n")
cat(paste("allocation factor: ", object$k))
cat("\n\n")
cat("Sample Size")
cat("\n")
cat("---------------------------------")
cat("\n")
cat(paste("control group: ", round(object$n[1],2)))
cat("\n")
cat(paste("treatment group:"), round(object$n[2],2))
}
if(object$model=="normal1subgroup"){
cat("Initial Sample Size Calculation")
cat("\n")
cat("Model: One Subgroup within a Full Population")
cat("\n")
cat("---------------------------------------------")
cat("\n")
cat(paste("alpha level: ", object$alpha))
cat("\n")
cat(paste("1-(testing power): ", object$beta))
cat("\n")
cat(paste("prevalence of subgroup: ", object$tau))
cat("\n")
cat(paste("effect in subgroup: ", object$delta[2]))
cat("\n")
cat(paste("effect outside subgroup:", object$delta[1]))
cat("\n")
cat(paste("sd in subgroup: ", object$sigma[2]))
cat("\n")
cat(paste("sd outside subgroup: ", object$sigma[1]))
cat("\n")
cat(paste("power precision: ", object$eps))
cat("\n")
cat(paste("approximation method: ", object$approx))
cat("\n")
cat(paste("allocation factor: ", object$k))
cat("\n\n")
cat("Sample Size")
cat("\n")
cat("---------------------------------------------")
cat("\n")
cat(paste("control group: ", round(object$n[1],2)))
cat("\n")
cat(paste("treatment group:"), round(object$n[2],2))
}
}
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