# NOT RUN {
# USEPA (2009) contains an example on page 19-23 that involves monitoring
# nw=100 compliance wells at a large facility with minimal natural spatial
# variation every 6 months for nc=20 separate chemicals.
# There are n=25 background measurements for each chemical to use to create
# simultaneous prediction intervals. We would like to determine which kind of
# resampling plan based on normal distribution simultaneous prediction intervals to
# use (1-of-m, 1-of-m based on means, or Modified California) in order to have
# adequate power of detecting an increase in chemical concentration at any of the
# 100 wells while at the same time maintaining a site-wide false positive rate
# (SWFPR) of 10% per year over all 4,000 comparisons
# (100 wells x 20 chemicals x semi-annual sampling).
# The function predIntNormSimultaneousTestPower includes the argument "r"
# that is the number of future sampling occasions (r=2 in this case because
# we are performing semi-annual sampling), so to compute the individual test
# Type I error level alpha.test (and thus the individual test confidence level),
# we only need to worry about the number of wells (100) and the number of
# constituents (20): alpha.test = 1-(1-alpha)^(1/(nw x nc)). The individual
# confidence level is simply 1-alpha.test. Plugging in 0.1 for alpha,
# 100 for nw, and 20 for nc yields an individual test confidence level of
# 1-alpha.test = 0.9999473.
nc <- 20
nw <- 100
conf.level <- (1 - 0.1)^(1 / (nc * nw))
conf.level
#[1] 0.9999473
# The help file for predIntNormSimultaneousTestPower shows how to
# create the results below for various sampling plans:
# Rule k m N.Mean K Power Total.Samples
#1 k.of.m 1 2 1 3.16 0.39 2
#2 k.of.m 1 3 1 2.33 0.65 3
#3 k.of.m 1 4 1 1.83 0.81 4
#4 Modified.CA 1 4 1 2.57 0.71 4
#5 k.of.m 1 1 2 3.62 0.41 2
#6 k.of.m 1 2 2 2.33 0.85 4
#7 k.of.m 1 1 3 2.99 0.71 3
# The above table shows the K-multipliers for each prediction interval, along with
# the power of detecting a change in concentration of three standard deviations at
# any of the 100 wells during the course of a year, for each of the sampling
# strategies considered. The last three rows of the table correspond to sampling
# strategies that involve using the mean of two or three observations.
# Here we will create a variation of this example based on
# using a lognormal distribution and plotting power versus ratio of the
# means assuming cv=1.
# Here is the power curve for the 1-of-4 sampling strategy:
dev.new()
plotPredIntLnormAltSimultaneousTestPowerCurve(n = 25, k = 1, m = 4, r = 2,
rule="k.of.m", range.ratio.of.means = c(1, 10), pi.type = "upper",
conf.level = conf.level, ylim = c(0, 1), main = "")
title(main = paste("Power Curves for 1-of-4 Sampling Strategy Based on 25 Background",
"Samples, SWFPR=10%, and 2 Future Sampling Periods", sep = "\n"))
mtext("Assuming Lognormal Data with CV=1", line = 0)
#----------
# Here are the power curves for the first four sampling strategies.
# Because this takes several seconds to run, here we have commented out
# the R commands. To run this example, just remove the pound signs (#)
# from in front of the R commands.
#dev.new()
#plotPredIntLnormAltSimultaneousTestPowerCurve(n = 25, k = 1, m = 4, r = 2,
# rule="k.of.m", range.ratio.of.means = c(1, 10), pi.type = "upper",
# conf.level = conf.level, ylim = c(0, 1), main = "")
#plotPredIntLnormAltSimultaneousTestPowerCurve(n = 25, k = 1, m = 3, r = 2,
# rule="k.of.m", range.ratio.of.means = c(1, 10), pi.type = "upper",
# conf.level = conf.level, add = TRUE, plot.col = "red", plot.lty = 2)
#plotPredIntLnormAltSimultaneousTestPowerCurve(n = 25, k = 1, m = 2, r = 2,
# rule="k.of.m", range.ratio.of.means = c(1, 10), pi.type = "upper",
# conf.level = conf.level, add = TRUE, plot.col = "blue", plot.lty = 3)
#plotPredIntLnormAltSimultaneousTestPowerCurve(n = 25, r = 2, rule="Modified.CA",
# range.ratio.of.means = c(1, 10), pi.type = "upper", conf.level = conf.level,
# add = TRUE, plot.col = "green3", plot.lty = 4)
#legend("topleft", c("1-of-4", "Modified CA", "1-of-3", "1-of-2"),
# col = c("black", "green3", "red", "blue"), lty = c(1, 4, 2, 3),
# lwd = 3 * par("cex"), bty = "n")
#title(main = paste("Power Curves for 4 Sampling Strategies Based on 25 Background",
# "Samples, SWFPR=10%, and 2 Future Sampling Periods", sep = "\n"))
#mtext("Assuming Lognormal Data with CV=1", line = 0)
#----------
# Here are the power curves for the last 3 sampling strategies:
# Because this takes several seconds to run, here we have commented out
# the R commands. To run this example, just remove the pound signs (#)
# from in front of the R commands.
#dev.new()
#plotPredIntLnormAltSimultaneousTestPowerCurve(n = 25, k = 1, m = 2, n.geomean = 2,
# r = 2, rule="k.of.m", range.ratio.of.means = c(1, 10), pi.type = "upper",
# conf.level = conf.level, ylim = c(0, 1), main = "")
#plotPredIntLnormAltSimultaneousTestPowerCurve(n = 25, k = 1, m = 1, n.geomean = 2,
# r = 2, rule="k.of.m", range.ratio.of.means = c(1, 10), pi.type = "upper",
# conf.level = conf.level, add = TRUE, plot.col = "red", plot.lty = 2)
#plotPredIntLnormAltSimultaneousTestPowerCurve(n = 25, k = 1, m = 1, n.geomean = 3,
# r = 2, rule="k.of.m", range.ratio.of.means = c(1, 10), pi.type = "upper",
# conf.level = conf.level, add = TRUE, plot.col = "blue", plot.lty = 3)
#legend("topleft", c("1-of-2, Order 2", "1-of-1, Order 3", "1-of-1, Order 2"),
# col = c("black", "blue", "red"), lty = c(1, 3, 2), lwd = 3 * par("cex"),
# bty="n")
#title(main = paste("Power Curves for 3 Sampling Strategies Based on 25 Background",
# "Samples, SWFPR=10%, and 2 Future Sampling Periods", sep = "\n"))
#mtext("Assuming Lognormal Data with CV=1", line = 0)
#==========
# Clean up
#---------
rm(nc, nw, conf.level)
graphics.off()
# }
Run the code above in your browser using DataLab