# NOT RUN {
# Look at the relationship between half-width and sample size
# for a one-sample confidence interval for a binomial proportion,
# assuming an estimated proportion of 0.5 and a confidence level of
# 95%. The jigsaw appearance of the plot is the result of using the
# score method:
dev.new()
plotCiBinomDesign()
#----------
# Redo the example above, but use the traditional (and inaccurate)
# Wald method.
dev.new()
plotCiBinomDesign(ci.method = "Wald")
#--------------------------------------------------------------------
# Plot sample size vs. the estimated proportion for various half-widths,
# using a 95% confidence level and the adjusted Wald method:
# NOTE: This example takes several seconds to run so it has been
# commented out. Simply remove the pound signs (#) from in front
# of the R commands to run it.
#dev.new()
#plotCiBinomDesign(x.var = "p.hat", y.var = "n",
# half.width = 0.04, ylim = c(0, 600), main = "",
# xlab = expression(hat(p)))
#
#plotCiBinomDesign(x.var = "p.hat", y.var = "n",
# half.width = 0.05, add = TRUE, plot.col = 2)
#
#plotCiBinomDesign(x.var = "p.hat", y.var = "n",
# half.width = 0.06, add = TRUE, plot.col = 3)
#
#legend(0.5, 150, paste("Half-Width =", c(0.04, 0.05, 0.06)),
# lty = rep(1, 3), lwd = rep(2, 3), col=1:3, bty = "n")
#
#mtext(expression(paste("Sample Size vs. ", hat(p),
# " for Confidence Interval for p")), line = 2.5, cex = 1.25)
#mtext("with Confidence=95% and Various Values of Half-Width",
# line = 1.5, cex = 1.25)
#mtext(paste("CI Method = Score Normal Approximation",
# "with Continuity Correction"), line = 0.5)
#--------------------------------------------------------------------
# Modifying the example on pages 8-5 to 8-7 of USEPA (1989b),
# look at the relationship between half-width and sample size
# for a 95% confidence interval for the difference between the
# proportion of detects at the background and compliance wells.
# Use the estimated proportion of detects from the original data.
# (The data are stored in EPA.89b.cadmium.df.)
# Assume equal sample sizes at each well.
EPA.89b.cadmium.df
# Cadmium.orig Cadmium Censored Well.type
#1 0.1 0.100 FALSE Background
#2 0.12 0.120 FALSE Background
#3 BDL 0.000 TRUE Background
# ..........................................
#86 BDL 0.000 TRUE Compliance
#87 BDL 0.000 TRUE Compliance
#88 BDL 0.000 TRUE Compliance
p.hat.back <- with(EPA.89b.cadmium.df,
mean(!Censored[Well.type=="Background"]))
p.hat.back
#[1] 0.3333333
p.hat.comp <- with(EPA.89b.cadmium.df,
mean(!Censored[Well.type=="Compliance"]))
p.hat.comp
#[1] 0.375
dev.new()
plotCiBinomDesign(p.hat.or.p1.hat = p.hat.back,
p2.hat = p.hat.comp, digits=3)
#==========
# Clean up
#---------
rm(p.hat.back, p.hat.comp)
graphics.off()
# }
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