predict.ros {NADA} | R Documentation |
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.
## 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, ...)
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. |
All functions return a numeric vector of results.
Some of these fuctions mask the original functions in the base
package. For the documentation on the original functions use:
help("foo", package="base")
Lopaka(Rob) Lee <rclee@usgs.gov>
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
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)