predict.ros {NADA}R Documentation

Query and Prediction with ROS models

Description

Functions that perform query and/or prediction with Regression on Order Statistics (ros) objects.

mean returns the modeled mean of a ROS model. median returns the modeled median of a ROS model. sd returns the modeled standard deviation of a ROS model.

quantile produces sample quantiles corresponding to the given probabilities. The smallest observation corresponds to a probability of 0 and the largest to a probability of 1.

predict Predicted values based on a ROS model object.

Usage

    ## S3 method for class 'ros':
    mean(x, ...)
    ## S3 method for class 'ros':
    median(x, na.rm=FALSE)
    ## S3 method for class 'ros':
    sd(x, na.rm=FALSE)
    ## S3 method for class 'ros':
    quantile(x, probs=c(0.05, 0.10, 0.25, 0.50, 0.75, 0.90, 0.95), ...)
    ## S3 method for class 'ros':
    predict(object, newdata, ...)

Arguments

x, object A ROS model constructed using ros
na.rm logical. Should missing values be removed?
probs Numeric vector with values in [0,1] – the quantiles to predict.
newdata Numeric vector of normalized quantiles of plotting positions.
... Additional arguments passed to the generic method.

Details

Value

All functions return a numeric vector of results.

Note

Some of these fuctions mask the original functions in the base package. For the documentation on the original functions use: help("foo", package="base")

Author(s)

Lopaka(Rob) Lee <rclee@usgs.gov>

References

Lee and Helsel (in press) Statistical analysis of environmental data containing multiple detection limits: S-language software for regression on order statistics, Computers in Geoscience vol. X, pp. X-X

Lee and Helsel (in press) Baseline models of trace elements in major aquifers of the United States. Applied Geochemistry vol. X, pp. X-X.

Dennis R. Helsel (2004), Nondetects And Data Analasis: John Wiley and Sons, New York.

Dennis R. Helsel (1990), Less Than Obvious: Statistical Methods for, Environmental Science and Technology, vol.24, no. 12, pp. 1767-1774

Dennis R. Helsel and Timothy A. Cohn (1988), Estimation of descriptive statistics for multiply censored water quality data, Water Resources Research vol. 24, no. 12, pp.1997-2004

See Also

ros hc.ppoints

Examples

    obs      = c(0.5,    0.5,   1.0,  1.5,   5.0,    10,   100)
    censored = c(TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE)

    myros = ros(obs, censored) 

    mean(myros)
    median(myros) 
    sd(myros)
 
    quantile(myros, probs=c(0.90, 0.95))
    predict(myros, 1.5)

[Package NADA version 1.1-2 Index]