plotPropTestDesign {EnvStats} R Documentation

## Plots for Sampling Design Based on One- or Two-Sample Proportion Test

### Description

Create plots involving sample size, power, difference, and significance level for a one- or two-sample proportion test.

### Usage

  plotPropTestDesign(x.var = "n", y.var = "power",
range.x.var = NULL, n.or.n1 = 25, n2 = n.or.n1, ratio = 1,
p.or.p1 = switch(alternative, greater = 0.6, less = 0.4,
two.sided = ifelse(two.sided.direction == "greater", 0.6, 0.4)),
p0.or.p2 = 0.5, alpha = 0.05, power = 0.95,
sample.type = ifelse(!missing(n2) || !missing(ratio), "two.sample", "one.sample"),
alternative = "two.sided", two.sided.direction = "greater",
approx = TRUE, correct = sample.type == "two.sample", round.up = FALSE,
warn = TRUE, n.min = 2, n.max = 10000, tol.alpha = 0.1 * alpha,
tol = 1e-07, maxiter = 1000, plot.it = TRUE, add = FALSE, n.points = 50,
plot.col = "black", plot.lwd = 3 * par("cex"), plot.lty = 1,

### Details

See the help files for propTestPower, propTestN, and propTestMdd for information on how to compute the power, sample size, or minimal detectable difference for a one- or two-sample proportion test.

### Value

plotPropTestDesign invisibly returns a list with components x.var and y.var, giving coordinates of the points that have been or would have been plotted.

### Note

See the help files for propTestPower, propTestN, and propTestMdd.

### Author(s)

Steven P. Millard (EnvStats@ProbStatInfo.com)

### References

See the help files for propTestPower, propTestN, and propTestMdd.

propTestPower, propTestN, propTestMdd, Binomial, binom.test, prop.test.

### Examples

  # Look at the relationship between power and sample size for a
# one-sample proportion test, assuming the true proportion is 0.6, the
# hypothesized proportion is 0.5, and a 5% significance level.
# Compute the power based on the normal approximation to the binomial
# distribution.

dev.new()
plotPropTestDesign()

#----------

# For a two-sample proportion test, plot sample size vs. the minimal detectable
# difference for various levels of power, using a 5% significance level and a
# two-sided alternative:

dev.new()
plotPropTestDesign(x.var = "delta", y.var = "n", sample.type = "two",
ylim = c(0, 2800), main="")

plotPropTestDesign(x.var = "delta", y.var = "n", sample.type = "two",
power = 0.9, add = TRUE, plot.col = "red")

plotPropTestDesign(x.var = "delta", y.var = "n", sample.type = "two",
power = 0.8, add = TRUE, plot.col = "blue")

legend("topright", c("95%", "90%", "80%"), lty = 1,
lwd = 3 * par("cex"), col = c("black", "red", "blue"), bty = "n")

title(main = paste("Sample Size vs. Minimal Detectable Difference for Two-Sample",
"Proportion Test with p2=0.5, Alpha=0.05 and Various Powers", sep = "\n"))

#==========

# Example 22-3 on page 22-20 of USEPA (2009) involves determining whether more than
# 10% of chlorine gas containers are stored at pressures above a compliance limit.
# We want to test the one-sided null hypothesis that 10% or fewer of the containers
# are stored at pressures greater than the compliance limit versus the alternative
# that more than 10% are stored at pressures greater than the compliance limit.
# We want to have at least 90% power of detecting a true proportion of 30% or
# greater, using a 5% Type I error level.

# Here we will modify this example and create a plot of power versus
# sample size for various assumed minimal detactable differences,
# using a 5% Type I error level.

dev.new()
plotPropTestDesign(x.var = "n", y.var = "power",
sample.type = "one", alternative = "greater",
p0.or.p2 = 0.1, p.or.p1 = 0.25,
range.x.var = c(20, 50), ylim = c(0.6, 1), main = "")

plotPropTestDesign(x.var = "n", y.var = "power",
sample.type = "one", alternative = "greater",
p0.or.p2 = 0.1, p.or.p1 = 0.3,
range.x.var = c(20, 50), add = TRUE, plot.col = "red")

plotPropTestDesign(x.var = "n", y.var = "power",
sample.type = "one", alternative = "greater",
p0.or.p2 = 0.1, p.or.p1 = 0.35,
range.x.var = c(20, 50), add = TRUE, plot.col = "blue")

legend("bottomright", c("p=0.35", "p=0.3", "p=0.25"), lty = 1,
lwd = 3 * par("cex"), col = c("blue", "red", "black"), bty = "n")

title(main = paste("Power vs. Sample Size for One-Sided One-Sample Proportion",
"Test with p0=0.1, Alpha=0.05 and Various Detectable Differences",
sep = "\n"))

#==========

# Clean up
#---------
graphics.off()


[Package EnvStats version 2.8.1 Index]