htestCensored.object {EnvStats}  R Documentation 
This class of objects is returned by EnvStats functions that perform
hypothesis tests based on censored data.
Objects of class "htestCensored"
are lists that contain information about
the null and alternative hypotheses, the censoring side, the censoring levels,
the percentage of observations that are censored,
the estimated distribution parameters (if applicable), the test statistic,
the pvalue, and (optionally, if applicable)
confidence intervals for distribution parameters.
Objects of S3 class "htestCensored"
are returned by
the functions listed in the section Hypothesis Tests
in the help file
EnvStats Functions for Censored Data.
Currently, the only function listed is
twoSampleLinearRankTestCensored
.
Required Components
The following components must be included in a legitimate list of
class "htestCensored"
.
statistic 
numeric scalar containing the value of the test statistic, with a

parameters 
numeric vector containing the parameter(s) associated with the null distribution of
the test statistic. This vector has a 
p.value 
numeric scalar containing the pvalue for the test under the null hypothesis. 
null.value 
numeric vector containing the value(s) of the population parameter(s) specified by
the null hypothesis. This vector has a 
alternative 
character string indicating the alternative hypothesis (the value of the input
argument 
method 
character string giving the name of the test used. 
sample.size 
numeric scalar containing the number of nonmissing observations in the sample used for the hypothesis test. 
data.name 
character string containing the actual name(s) of the input data. 
bad.obs 
the number of missing ( 
censoring.side 
character string indicating whether the data are left or rightcensored. 
censoring.name 
character string indicating the name of the data object used to identify which values are censored. 
censoring.levels 
numeric scalar or vector indicating the censoring level(s). 
percent.censored 
numeric scalar indicating the percent of nonmissing observations that are censored. 
Optional Components
The following component may optionally be included in an object of
of class "htestCensored"
:
estimate 
numeric vector containing the value(s) of the estimated population parameter(s)
involved in the null hypothesis. This vector has a 
estimation.method 
character string containing the method used to compute the estimated distribution
parameter(s). The value of this component will depend on the available estimation
methods (see 
interval 
a list containing information about a confidence, prediction, or tolerance interval. 
Generic functions that have methods for objects of class
"htestCensored"
include:
print
.
Since objects of class "htestCensored"
are lists, you may extract
their components with the $
and [[
operators.
Steven P. Millard (EnvStats@ProbStatInfo.com)
print.htestCensored
, Censored Data.
# Create an object of class "htestCensored", then print it out.
#
htestCensored.obj < with(EPA.09.Ex.16.5.PCE.df,
twoSampleLinearRankTestCensored(
x = PCE.ppb[Well.type == "Compliance"],
x.censored = Censored[Well.type == "Compliance"],
y = PCE.ppb[Well.type == "Background"],
y.censored = Censored[Well.type == "Background"],
test = "taroneware", alternative = "greater"))
mode(htestCensored.obj)
#[1] "list"
class(htestCensored.obj)
#[1] "htest"
names(htestCensored.obj)
# [1] "statistic" "parameters" "p.value"
# [4] "estimate" "null.value" "alternative"
# [7] "method" "estimation.method" "sample.size"
#[10] "data.name" "bad.obs" "censoring.side"
#[13] "censoring.name" "censoring.levels" "percent.censored"
htestCensored.obj
#Results of Hypothesis Test
#Based on Censored Data
#
#
#Null Hypothesis: Fy(t) = Fx(t)
#
#Alternative Hypothesis: Fy(t) > Fx(t) for at least one t
#
#Test Name: TwoSample Linear Rank Test:
# TaroneWare Test
# with Hypergeometric Variance
#
#Censoring Side: left
#
#Data: x = PCE.ppb[Well.type == "Compliance"]
# y = PCE.ppb[Well.type == "Background"]
#
#Censoring Variable: x = Censored[Well.type == "Compliance"]
# y = Censored[Well.type == "Background"]
#
#Sample Sizes: nx = 8
# ny = 6
#
#Percent Censored: x = 12.5%
# y = 50.0%
#
#Test Statistics: nu = 8.458912
# var.nu = 20.912407
# z = 1.849748
#
#Pvalue: 0.03217495
#==========
# Extract the test statistics
#
htestCensored.obj$statistic
# nu var.nu z
# 8.458912 20.912407 1.849748
#==========
# Clean up
#
rm(htestCensored.obj)