Plots for Sampling Design Based on Nonparametric Confidence Interval for a Quantile
Create plots involving sample size, quantile, and confidence level for a nonparametric confidence interval for a quantile.
plotCiNparDesign(x.var = "n", y.var = "conf.level", range.x.var = NULL,
n = 25, p = 0.5, conf.level = 0.95, ci.type = "two.sided",
lcl.rank = ifelse(ci.type == "upper", 0, 1),
n.plus.one.minus.ucl.rank = ifelse(ci.type == "lower", 0, 1),
plot.it = TRUE, add = FALSE, n.points = 100, plot.col = "black",
plot.lwd = 3 * par("cex"), plot.lty = 1, digits = .Options$digits,
cex.main = par("cex"), ..., main = NULL, xlab = NULL, ylab = NULL,
type = "l")x.var |
character string indicating what variable to use for the x-axis.
Possible values are |
y.var |
character string indicating what variable to use for the y-axis.
Possible values are |
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 |
n |
numeric scalar indicating the sample size. The default value is
|
p |
numeric scalar specifying the quantile. The value of this argument must be
between 0 and 1. The default value is |
conf.level |
a scalar between 0 and 1 indicating the confidence level associated with the confidence interval.
The default value is |
ci.type |
character string indicating what kind of confidence interval to compute. The
possible values are |
lcl.rank, n.plus.one.minus.ucl.rank |
numeric vectors of non-negative integers indicating the ranks of the
order statistics that are used for the lower and upper bounds of the
confidence interval for the specified quantile(s). When |
plot.it |
a logical scalar indicating whether to create a plot or add to the
existing plot (see |
add |
a logical scalar indicating whether to add the design plot to the
existing plot ( |
n.points |
a numeric scalar specifying how many (x,y) pairs to use to produce the plot.
There are |
plot.col |
a numeric scalar or character string determining the color of the plotted
line or points. The default value is |
plot.lwd |
a numeric scalar determining the width of the plotted line. The default value is
|
plot.lty |
a numeric scalar determining the line type of the plotted line. The default value is
|
digits |
a scalar indicating how many significant digits to print out on the plot. The default
value is the current setting of |
cex.main, main, xlab, ylab, type, ... |
additional graphical parameters (see |
See the help files for eqnpar, ciNparConfLevel,
and ciNparN for information on how to compute a
nonparametric confidence interval for a quantile, how the confidence level
is computed when other quantities are fixed, and how the sample size is
computed when other quantities are fixed.
plotCiNparDesign invisibly returns a list with components
x.var and y.var, giving coordinates of the points that
have been or would have been plotted.
See the help file for eqnpar.
Steven P. Millard (EnvStats@ProbStatInfo.com)
See the help file for eqnpar.
# Look at the relationship between confidence level and sample size for
# a two-sided nonparametric confidence interval for the 90'th percentile.
dev.new()
plotCiNparDesign(p = 0.9)
#----------
# Plot sample size vs. quantile for various levels of confidence:
dev.new()
plotCiNparDesign(x.var = "p", y.var = "n", range.x.var = c(0.8, 0.95),
ylim = c(0, 60), main = "")
plotCiNparDesign(x.var = "p", y.var = "n", conf.level = 0.9, add = TRUE,
plot.col = 2, plot.lty = 2)
plotCiNparDesign(x.var = "p", y.var = "n", conf.level = 0.8, add = TRUE,
plot.col = 3, plot.lty = 3)
legend("topleft", c("95%", "90%", "80%"), lty = 1:3, col = 1:3,
lwd = 3 * par('cex'), bty = 'n')
title(main = paste("Sample Size vs. Quantile for ",
"Nonparametric CI for \nQuantile, with ",
"Various Confidence Levels", sep=""))
#==========
# Clean up
#---------
graphics.off()Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.