NADA2 (version 1.0.1)

ROSci: Computes confidence intervals on regression on order statistics (ROS) mean

Description

Uses ROS model output from the NADA package and computes the Zhou and Gao 1997 modified Cox<U+2019>s method two-sided confidence interval around the mean for a lognormal distribution. Computes a t-interval for a gaussian ROS model output.

Usage

ROSci(cenros.out, conf = 0.95, printstat = TRUE)

Arguments

cenros.out

an ROS model output object (see details)

conf

Confidence coefficient of the interval (Default is 0.95)

printstat

Logical TRUE/FALSE option of whether to print the resulting statistics in the console window, or not. Default is TRUE.

Value

Prints a lower (LCL) and upper (UCL) confidence interval based on the conf provided (Default is 95%)

Details

This function uses an ROS model output based on the ros function in the NADA package. The lognormal distribution is the default for the NADA package but a gaussian distribution is optional here. For more detail on ROS modeling see the ros help file (?NADA::ros).

For implementation of ROSci(...) see the examples below.

References

Helsel, D.R., 2011. Statistics for censored environmental data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Lee, L., Helsel, D., 2005. Statistical analysis of water-quality data containing multiple detection limits: S-language software for regression on order statistics. Computers & Geosciences 31, 1241<U+2013>1248. https://doi.org/10.1016/j.cageo.2005.03.012

Zhou, X.-H., Gao, S., 1997. Confidence Intervals for the Log-Normal Mean. Statistics in Medicine 16, 783<U+2013>790. https://doi.org/10.1002/(SICI)1097-0258(19970415)16:7<783::AID-SIM488>3.0.CO;2-2

See Also

NADA::ros

Examples

# NOT RUN {
data(Brumbaugh)
myros <- NADA::ros(Brumbaugh$Hg,Brumbaugh$HgCen)

summary(myros)

# ROS Mean
mean(myros)

# 95% CI around the ROS mean
ROSci(myros)
# }