R sample size and confidence interval calculation

SampleSize.R

###############################################################################
#                           Sample Size  and Confidence Interval Calculation  #
#                           v 1.3 by "Ananda Mahto"/mrdwab/[email protected]  #
#  2011 May 17                                                                #
# --------------------------------------------------------------------------- #
#                                                                             #
#  Example usage:                                                             #
#    * sample.size.table(c.lev = c(90, 95, 98, 99), population = 378)         #
#    * sample.size(c.lev = 98, population = 200)                              #
#    * confidence.interval(c.lev = 95, p.ss = 80, population = 100)           #
#                                                                             #
#  Advanced usage:                                                            #
#    * sample.size.table (c.lev = rep(c(95:99), each = 5),                    #
#                         c.int = c(1:5), pop = 100)                          #
#    * sample.size.table (c.lev = rep(c(95:99)), c.int = 5,                   #
#                         pop = rep(c(101:105), each=5))                      #
# --------------------------------------------------------------------------- #
#  Notes:                                                                     #
#         c.lev = confidence level, usually 90 or 95                          #
#        margin = usually set at 50%, which gives the largest sample size     #
#    c.interval = confidence interval, usually +/- 5%                         #
#    population = the entire population you want to draw a sample from        #
#          p.ss = your population sample size                                 #
###############################################################################

sample.size.table = function(c.lev, margin=50, c.interval=5, population) {
  z      = qnorm(.5+c.lev/200)
  m      = margin/100
  int    = c.interval/100
  ss     = (z^2 * m * (1-m))/(int^2)
  p.ss   = ss/(1 + ((ss-1)/population))
  CL     = c(paste(c.lev, "%", sep = ""))
  CI     = c(paste(c.interval, "%", sep = ""))
  M      = c(paste(margin, "%", sep = ""))
  METHOD = c("Sample size calculations")
  RES    = data.frame(population, CL, CI, M, round(p.ss, digits = 0))
  pre    = structure(list(POP = "Population", CL = "Confidence level",
           CI = "Confidence interval (+/-)", MOE = "Margin of error",
		   SS = "Sample size", method = METHOD), class = "power.htest")
  names(RES) = c("POP","CL", "CI", "MOE", "SS")
  print(pre)
  print(RES)
}

sample.size = 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 = "")
  moe    = paste((c.interval*100), "%", sep="")
  resp.dist = paste((margin*100),"%", sep="")
  structure(list(Population = population, "Confidence level" = c.lev, 
  "Margin of error" = moe, "Response distribution" = resp.dist,
  "Recommended sample size" = p.ss, method = METHOD), class = "power.htest")
}

confidence.interval = function(c.lev, margin=.5, p.ss, population) {
  z.val      = qnorm(.5+c.lev/200)
  ss         = ((population*p.ss-p.ss)/(population-p.ss))
  c.interval = sqrt((z.val^2 * margin * (1-margin))/ss)
  r.cint     = round(c.interval*100, digits = 2)
  METHOD     = paste("The confidence interval is ", r.cint, 
               "% with the following assumptions:", sep = "")
  resp.dist  = paste((margin*100),"%", sep="")
  structure(list(Population = population, "Sample size" = p.ss,
  "Confidence level" = c.lev, "Response distribution" = resp.dist, 
  method = METHOD), class = "power.htest")
}