plotLinearTrendTestDesign {EnvStats} R Documentation

## Plots for a Sampling Design Based on a t-Test for Linear Trend

### Description

Create plots involving sample size, power, scaled difference, and significance level for a t-test for linear trend.

### Usage

  plotLinearTrendTestDesign(x.var = "n", y.var = "power",
range.x.var = NULL, n = 12,
slope.over.sigma = switch(alternative, greater = 0.1, less = -0.1,
two.sided = ifelse(two.sided.direction == "greater", 0.1, -0.1)),
alpha = 0.05, power = 0.95, 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 = ifelse(x.var == "n", diff(range.x.var) + 1, 50),
plot.col = "black", plot.lwd = 3 * par("cex"), plot.lty = 1,

### Details

See the help files for linearTrendTestPower, linearTrendTestN, and linearTrendTestScaledMds for information on how to compute the power, sample size, or scaled minimal detectable slope for a t-test for linear trend.

### Value

plotlinearTrendTestDesign 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 linearTrendTestPower.

### Author(s)

Steven P. Millard (EnvStats@ProbStatInfo.com)

### References

See the help file for linearTrendTestPower.

linearTrendTestPower, linearTrendTestN, linearTrendTestScaledMds.

### Examples

  # Look at the relationship between power and sample size for the t-test for
# liner trend, assuming a scaled slope of 0.1 and a 5% significance level:

dev.new()
plotLinearTrendTestDesign()

#==========

# Plot sample size vs. the scaled minimal detectable slope for various
# levels of power, using a 5% significance level:

dev.new()
plotLinearTrendTestDesign(x.var = "slope.over.sigma", y.var = "n",
ylim = c(0, 30), main = "")

plotLinearTrendTestDesign(x.var = "slope.over.sigma", y.var = "n",
power = 0.9, add = TRUE, plot.col = "red")

plotLinearTrendTestDesign(x.var = "slope.over.sigma", y.var = "n",
power = 0.8, add = TRUE, plot.col = "blue")

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

title(main = paste("Sample Size vs. Scaled Slope for t-Test for Linear Trend",
"with Alpha=0.05 and Various Powers", sep="\n"))

#==========

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


[Package EnvStats version 2.8.1 Index]