# NOT RUN {
# Look at how the required sample size of a one-sample
# confidence interval increases with decreasing
# required half-width:
ciBinomN(half.width = c(0.1, 0.05, 0.03))
#$n
#[1] 92 374 1030
#
#$p.hat
#[1] 0.5 0.5 0.5
#
#$half.width
#[1] 0.10010168 0.05041541 0.03047833
#
#$method
#[1] "Score normal approximation, with continuity correction"
#----------
# Note that the required sample size decreases if we are less
# stringent about how much the confidence interval width can
# deviate from the supplied value of the 'half.width' argument:
ciBinomN(half.width = c(0.1, 0.05, 0.03), tol.half.width = 0.005)
#$n
#[1] 84 314 782
#
#$p.hat
#[1] 0.5 0.5 0.5
#
#$half.width
#[1] 0.10456066 0.05496837 0.03495833
#
#$method
#[1] "Score normal approximation, with continuity correction"
#--------------------------------------------------------------------
# Look at how the required sample size for a one-sample
# confidence interval tends to decrease as the estimated
# value of p decreases below 0.5 or increases above 0.5:
seq(0.2, 0.8, by = 0.1)
#[1] 0.2 0.3 0.4 0.5 0.6 0.7 0.8
ciBinomN(half.width = 0.1, p.hat = seq(0.2, 0.8, by = 0.1))
#$n
#[1] 70 90 100 92 100 90 70
#
#$p.hat
#[1] 0.2 0.3 0.4 0.5 0.6 0.7 0.8
#
#$half.width
#[1] 0.09931015 0.09839843 0.09910818 0.10010168 0.09910818 0.09839843
#[7] 0.09931015
#
#$method
#[1] "Score normal approximation, with continuity correction"
#----------------------------------------------------------------
# Look at how the required sample size for a one-sample
# confidence interval increases with increasing confidence level:
ciBinomN(half.width = 0.05, conf.level = c(0.8, 0.9, 0.95, 0.99))
#$n
#[1] 160 264 374 644
#
#$p.hat
#[1] 0.5 0.5 0.5 0.5
#
#$half.width
#[1] 0.05039976 0.05035948 0.05041541 0.05049152
#
#$method
#[1] "Score normal approximation, with continuity correction"
#----------------------------------------------------------------
# Compare required sample size for a one-sample
# confidence interval based on the different methods:
ciBinomN(half.width = 0.05, ci.method = "score")
#$n
#[1] 374
#
#$p.hat
#[1] 0.5
#
#$half.width
#[1] 0.05041541
#
#$method
#[1] "Score normal approximation, with continuity correction"
ciBinomN(half.width = 0.05, ci.method = "exact")
#$n
#[1] 394
#
#$p.hat
#[1] 0.5
#
#$half.width
#[1] 0.05047916
#
#$method
#[1] "Exact"
ciBinomN(half.width = 0.05, ci.method = "adjusted Wald")
#$n
#[1] 374
#
#$p.hat
#[1] 0.5
#
#$half.width
#[1] 0.05041541
#
#$method
#[1] "Adjusted Wald normal approximation"
ciBinomN(half.width = 0.05, ci.method = "Wald")
#$n
#[1] 398
#
#$p.hat
#[1] 0.5
#
#$half.width
#[1] 0.05037834
#
#$method
#[1] "Wald normal approximation, with continuity correction"
#----------------------------------------------------------------
# }
# NOT RUN {
# Look at how the required sample size of a two-sample
# confidence interval increases with decreasing
# required half-width:
ciBinomN(half.width = c(0.1, 0.05, 0.03), sample.type = "two")
#$n1
#[1] 210 778 2089
#
#$n2
#[1] 210 778 2089
#
#$p1.hat
#[1] 0.5000000 0.5000000 0.4997607
#
#$p2.hat
#[1] 0.4000000 0.3997429 0.4001915
#
#$half.width
#[1] 0.09943716 0.05047044 0.03049753
#
#$method
#[1] "Score normal approximation, with continuity correction"
# }
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