plotTTestDesign {EnvStats} R Documentation

Plots for a Sampling Design Based on a One- or Two-Sample t-Test

Description

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

Usage

  plotTTestDesign(x.var = "n", y.var = "power", range.x.var = NULL,
n.or.n1 = 25, n2 = n.or.n1,
delta.over.sigma = switch(alternative, greater = 0.5, less = -0.5,
two.sided = ifelse(two.sided.direction == "greater", 0.5, -0.5)),
alpha = 0.05, power = 0.95,
sample.type = ifelse(!missing(n2), "two.sample", "one.sample"),
alternative = "two.sided", two.sided.direction = "greater", approx = FALSE,
round.up = FALSE, n.max = 5000, tol = 1e-07, maxiter = 1000, plot.it = TRUE,
add = FALSE, n.points = 50, plot.col = "black", plot.lwd = 3 * par("cex"),

Details

See the help files for tTestPower, tTestN, and tTestScaledMdd for information on how to compute the power, sample size, or scaled minimal detectable difference for a one- or two-sample t-test.

Value

plotTTestDesign 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 tTestPower, tTestN, and tTestScaledMdd.

Author(s)

Steven P. Millard (EnvStats@ProbStatInfo.com)

References

See the help files for tTestPower, tTestN, and tTestScaledMdd.

tTestPower, tTestN, tTestScaledMdd, t.test.

Examples

  # Look at the relationship between power and sample size for a two-sample t-test,
# assuming a scaled difference of 0.5 and a 5% significance level:

dev.new()
plotTTestDesign(sample.type = "two")

#----------

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

dev.new()
plotTTestDesign(x.var = "delta.over.sigma", y.var = "n", sample.type = "two",
ylim = c(0, 110), main="")

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

plotTTestDesign(x.var = "delta.over.sigma", 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. Scaled Difference for",
"Two-Sample t-Test, with Alpha=0.05 and Various Powers",
sep="\n"))

#==========

# Modifying the example on pages 21-4 to 21-5 of USEPA (2009), look at
# power versus scaled minimal detectable difference for various sample
# sizes in the context of the problem of using a one-sample t-test to
# compare the mean for the well with the MCL of 7 ppb.  Use alpha = 0.01,
# assume an upper one-sided alternative (i.e., compliance well mean larger
# than 7 ppb).

dev.new()
plotTTestDesign(x.var = "delta.over.sigma", y.var = "power",
range.x.var = c(0.5, 2), n.or.n1 = 8, alpha = 0.01,
alternative = "greater", ylim = c(0, 1), main = "")

plotTTestDesign(x.var = "delta.over.sigma", y.var = "power",
range.x.var = c(0.5, 2), n.or.n1 = 6, alpha = 0.01,
alternative = "greater", add = TRUE, plot.col = "red")

plotTTestDesign(x.var = "delta.over.sigma", y.var = "power",
range.x.var = c(0.5, 2), n.or.n1 = 4, alpha = 0.01,
alternative = "greater", add = TRUE, plot.col = "blue")

legend("topleft", paste("N =", c(8, 6, 4)), lty = 1, lwd = 3 * par("cex"),
col = c("black", "red", "blue"), bty = "n")

title(main = paste("Power vs. Scaled Difference for One-Sample t-Test",
"with Alpha=0.01 and Various Sample Sizes", sep="\n"))

#==========

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


[Package EnvStats version 2.8.1 Index]