R/Sampling.R

In danielmarcelino/SciencesPo: A Tool Set for Analyzing Political Behavior Data

#' @title  Calculate the Minimum Required Sample Size for a Sample
#'
#' @description Calculate the sample size for a population for a given confidence level. The function sample.size calculates the minimum required sample size for a sample.
#' @param c.lev a numeric value for the required confidence level
#' @param margin a numeric value for margin of error
#' @param c.interval a numeric value for confidence interval
#' @param population an integer value for population size of the base population
#' @export
#' @examples
#' sampleSize(c.lev = 95, margin=.5, population = 3e+06)
`sampleSize` <- function(c.lev, margin=.5, c.interval=.05, population) {
  z.val = qnorm(.5+c.lev/200)
  ss = (z.val^2 * margin * (1-margin))/c.interval^2
  p.ss = round((ss/(1 + ((ss-1)/population))), digits=0)
  METHOD = paste("Recommended sample size for a population of ", population,
                 " at a ", c.lev, "% confidence level", sep = "")
  structure(list(Population = population, "Confidence level" = paste((c.lev), "%", sep=""), "Margin of error" = paste((c.interval*100), "%", sep=""), "Response distribution" = paste((margin*100), "%", sep=""), "Recommended sample size" = p.ss, method = METHOD),class = "power.htest")
}
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#' @title Calculates a Table of Sample Sizes
#' @description Calculates a table of sample sizes for the confidence levels: 80%,90%,95%,98%,99%,99.5%,99.8%,99.9% and 99.99% for a given base population.
#' @param margin a numeric value for margin of error
#' @param c.interval a numeric value for confidence interval
#' @param population an integer value for population size of the base population
#' @export
#' @examples
#' sampleSizeTable(population = 3000000)
`sampleSizeTable` <- function(margin=.5, c.interval=.05, population) {
  z.val=c(1.281551565545, 1.644853626951, 1.959963984540, 2.326347874041,
          2.575829303549, 2.807033768344, 3.090232306168, 3.290526731492,
          3.890591886413)
  ss = (z.val^2 * margin * (1-margin))/(c.interval^2)
  p.ss = ss/(1 + ((ss-1)/population))
  c.level = c("80%","90%","95%","98%","99%","99.5%","99.8%","99.9%","99.99%")
  results = data.frame(c.level, round(p.ss, digits = 0))
  names(results) = c("Confidence Level", "Sample Size")
  METHOD = c("Suggested sample sizes at different confidence levels")
  moe = paste((c.interval*100), "%", sep="")
  resp.dist = paste((margin*100),"%", sep="")
  pre = structure(list(Population=population, "Margin of error" = moe,
                       "Response distribution" = resp.dist, method = METHOD),
                  class = "power.htest")
  print(pre)
  print(results)
}
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