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

## Description

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.

## Usage

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

## Arguments

 `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

## Value

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

## Note

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.

## Author(s)

E. L. Frome

See Also `plekm`
 ```1 2 3 4 5 6 7 8 9``` ```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)) # ```