print.estimateCensored: Print Objects of Class "estimateCensored"

Description

Formats and prints the results of EnvStats functions that estimate the parameters or quantiles of a probability distribution and optionally construct confidence, prediction, or tolerance intervals based on a sample of Tyep I censored data assumed to come from that distribution. This method is automatically called by print when given an object of class "estimateCensored". See the subsections Estimating Distribution Parameters and Estimating Distribution Quantiles in the help file Censored Data for lists of functions that estimate distribution parameters and quantiles based on Type I censored data. See the subsection Prediction and Tolerance Intervals in the help file Censored Data for lists of functions that create prediction and tolerance intervals.

Usage

## S3 method for class 'estimateCensored':
print(x, show.cen.levels = TRUE, 
  pct.censored.digits = .Options$digits, 
  conf.cov.sig.digits = .Options$digits, limits.sig.digits = .Options$digits, 
  ...)

Arguments

an object of class "estimateCensored". See estimateCensored.object for details.

show.cen.levels

logical scalar indicating whether to print the censoring levels. The default is show.cen.levels=TRUE.

pct.censored.digits

numeric scalar indicating the number of significant digits to print for the percent of censored observations.

conf.cov.sig.digits

numeric scalar indicating the number of significant digits to print for the confidence level or coverage of a confidence, prediction, or tolerance interval.

limits.sig.digits

numeric scalar indicating the number of significant digits to print for the upper and lower limits of a confidence, prediction, or tolerance interval.

...

arguments that can be supplied to the format function.

Value

Invisibly returns the input x.

Details

This is the "estimateCensored" method for the generic function print. Prints estimated parameters and, if present in the object, information regarding confidence, prediction, or tolerance intervals.

References

Chambers, J. M. and Hastie, T. J. (1992). Statistical Models in S. Wadsworth & Brooks/Cole.