EnvStats (version 2.3.1)

plotLinearTrendTestDesign: 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, 
    digits = .Options$digits, ..., main = NULL, xlab = NULL, ylab = NULL, 
    type = "l")

Arguments

x.var

character string indicating what variable to use for the x-axis. Possible values are "n" (sample size; the default), "slope.over.sigma" (scaled minimal detectable slope), "power" (power of the test), and "alpha" (significance level of the test).

y.var

character string indicating what variable to use for the y-axis. Possible values are "power" (power of the test; the default), "slope.over.sigma" (scaled minimal detectable slope), and "n" (sample size).

range.x.var

numeric vector of length 2 indicating the range of the x-variable to use for the plot. The default value depends on the value of x.var. When x.var="n" the default value is c(3,25). When x.var="slope.over.sigma" and alternative="greater" or alternative="two.sided" and two.sided.direction="greater", the default value is c(0.1, 1). When x.var="slope.over.sigma" and alternative="less" or alternative="two.sided" and two.sided.direction="less", the default value is -c(1, 0.1). When x.var="power" the default value is c(alpha + .Machine$double.eps, 0.95). When x.var="alpha", the default value is c(0.01, 0.2).

n

numeric scalar indicating the sample size. The default value is n=12. Missing (NA), undefined (NaN), and infinite (Inf, -Inf) values are not allowed. This argument is ignored if either x.var="n" or y.var="n".

slope.over.sigma

numeric scalar specifying the ratio of the true slope (\(\beta_1\)) to the population standard deviation of the error terms (\(\sigma\)). This is also called the "scaled slope". When alternative="greater" or alternative="two.sided" and two.sided.direction="greater", the default value is slope.over.sigma=0.1. When alternative="less" or alternative="two.sided" and two.sided.direction="less", the default value is delta.over.sigma=-0.1. This argument is ignored when x.var="slope.over.sigma" or y.var="slope.over.sigma".

alpha

numeric scalar between 0 and 1 indicating the Type I error level associated with the hypothesis test. The default value is alpha=0.05. This argument is ignored when x.var="alpha".

power

numeric scalar between 0 and 1 indicating the power associated with the hypothesis test. The default value is power=0.95. This argument is ignored when x.var="power" or y.var="power".

alternative

character string indicating the kind of alternative hypothesis. The possible values are "two.sided" (the default), "greater", and "less".

two.sided.direction

character string indicating the direction (positive or negative) for the scaled minimal detectable slope when alternative="two.sided". When two.sided.direction="greater" (the default), the scaled minimal detectable slope is positive. When two.sided.direction="less", the scaled minimal detectable slope is negative. This argument is ignored if alternative="less" or alternative="greater".

approx

logical scalar indicating whether to compute the power based on an approximation to the non-central t-distribution. The default value is approx=FALSE.

round.up

logical scalar indicating whether to round up the values of the computed sample size(s) to the next smallest integer. The default value is FALSE.

n.max

for the case when y.var="n", a positive integer greater than 2 indicating the maximum sample size. The default value is n.max=5000.

tol

numeric scalar indicating the toloerance to use in the uniroot search algorithm. The default value is tol=1e-7.

maxiter

positive integer indicating the maximum number of iterations argument to pass to the uniroot function. The default value is maxiter=1000.

plot.it

a logical scalar indicating whether to create a new plot or add to the existing plot (see add) on the current graphics device. If plot.it=FALSE, no plot is produced, but a list of (x,y) values is returned (see VALUE). The default value is plot.it=TRUE.

add

a logical scalar indicating whether to add the design plot to the existing plot (add=TRUE), or to create a plot from scratch (add=FALSE). The default value is add=FALSE. This argument is ignored if plot.it=FALSE.

n.points

a numeric scalar specifying how many (x,y) pairs to use to produce the plot. There are n.points x-values evenly spaced between range.x.var[1] and range.x.var[2]. The default value is n.points=50 unless x.var="n", in which case n.points=diff(range.x.var)+1.

plot.col

a numeric scalar or character string determining the color of the plotted line or points. The default value is plot.col="black". See the entry for col in the help file for par for more information.

plot.lwd

a numeric scalar determining the width of the plotted line. The default value is 3*par("cex"). See the entry for lwd in the help file for par for more information.

plot.lty

a numeric scalar determining the line type of the plotted line. The default value is plot.lty=1. See the entry for lty in the help file for par for more information.

digits

a scalar indicating how many significant digits to print out on the plot. The default value is the current setting of options("digits").

main, xlab, ylab, type, …

additional graphical parameters (see par).

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.

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.

References

See the help file for linearTrendTestPower.

See Also

linearTrendTestPower, linearTrendTestN, linearTrendTestScaledMds.

Examples

# NOT RUN {
  # 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()
# }