ple.plot: Plot PLE With Confidence Limits
In STAND: Statistical Analysis of Non-Detects

Description Usage Arguments Value Note Author(s) See Also Examples

Plot the product limit estimate (PLE) of F(x) and 100(2γ -1)\% two-sided confidence limits (CLs) for left censored data. A horizontal line corresponding to the Xp = 100pth percentile is added to the plot and the nonparametric confidence limits for Xp are displayed in the title.

1	ple.plot(dd, gam = 0.95, p = 0.95, xlow = 0, xh = NA, ylow = 0, yh = 1,...)

`dd`	An n by 2 matrix or data frame with x (exposure) variable in column 1, and det = 0 for non-detect or 1 for detect in column 2
`gam`	one-sided confidence level γ. Default is 0.95
`p`	probability for Xp the 100pth percentile. Default is 0.95
`xlow`	minimum value on x axis. Default = 0
`xh`	maximum value on the x axis. Default = maximum value of x
`ylow`	minimum value on y axis. Default = 0
`yh`	maximum value on the y axis. Default = 1
`...`	Additional parameters to plot

Data frame with columns

`a`	value of jth detect (ordered)
`ple`	PLE of F(x) at a
`stder`	standard error of F(x) at a
`lower`	lower CL for PLE at a
`upper`	upper CL for PLE at a
`n`	number of detects or non-detects ≥ a
`r`	number of detects equal to a

If the solid horizontal line does not intersect the lower CL for the PLE, then the upper CL for Xp UX(p,γ) is not defined.

E. L. Frome

See Also plekm

data(beTWA)
par( mfrow=c(1,2) )
ple.plot(beTWA)  #  plot the PLE of F(x) for the beTWA data
ple.plot(beTWA,ylow=0.8) #  plot the upper right tail 
# Lognormal ML estimates of 95th percentile and CLs
unlist(percentile.ml(beTWA))
# PLE   estimates of 95th percentile and CLs
unlist(percentile.ple(beTWA))
#