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 p-value, and (optionally, if applicable)
confidence intervals for distribution parameters.
Details
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.
Value
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
names attribute indicating the null distribution.
parameters
numeric vector containing the parameter(s) associated with the null distribution of
the test statistic. This vector has a names attribute describing its
element(s).
p.value
numeric scalar containing the p-value 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 names attribute describing its
elements.
alternative
character string indicating the alternative hypothesis (the value of the input
argument alternative). Possible values are "greater", "less",
or "two-sided".
method
character string giving the name of the test used.
sample.size
numeric scalar containing the number of non-missing 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 (NA), undefined (NaN) and/or infinite
(Inf, -Inf) values that were removed from the data object prior to
performing the hypothesis test.
censoring.side
character string indicating whether the data are
left- or right-censored.
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 non-missing
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 names attribute
describing its element(s).
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 Distribution.df).
interval
a list containing information about a confidence, prediction, or tolerance interval.
Methods
Generic functions that have methods for objects of class
"htestCensored" include: print.
Note
Since objects of class "htestCensored" are lists, you may extract
their components with the $ and [[ operators.
# 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 = "tarone-ware", 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: Two-Sample Linear Rank Test:
# Tarone-Ware 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
#
#P-value: 0.03217495
#==========
# Extract the test statistics
#----------------------------
htestCensored.obj$statistic
# nu var.nu z
# 8.458912 20.912407 1.849748
#==========
# Clean up
#---------
rm(htestCensored.obj)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(EnvStats)
Attaching package: 'EnvStats'
The following objects are masked from 'package:stats':
predict, predict.lm
The following object is masked from 'package:base':
print.default
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/EnvStats/htestCensored.object.Rd_%03d_medium.png", width=480, height=480)
> ### Name: htestCensored.object
> ### Title: S3 Class "htestCensored"
> ### Aliases: htestCensored.object
> ### Keywords: classes
>
> ### ** Examples
>
> # 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 = "tarone-ware", alternative = "greater"))
>
> mode(htestCensored.obj)
[1] "list"
> #[1] "list"
>
> class(htestCensored.obj)
[1] "htestCensored"
> #[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"
> # [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: Two-Sample Linear Rank Test:
Tarone-Ware Test
with Hypergeometric Variance
Censoring Side: left
Censoring Level(s): x = 5
y = 2 4 5
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
P-value: 0.03217495
>
> #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: Two-Sample Linear Rank Test:
> # Tarone-Ware 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
> #
> #P-value: 0.03217495
>
> #==========
>
> # Extract the test statistics
> #----------------------------
>
> htestCensored.obj$statistic
nu var.nu z
8.458912 20.912407 1.849748
> # nu var.nu z
> # 8.458912 20.912407 1.849748
>
> #==========
>
> # Clean up
> #---------
> rm(htestCensored.obj)
>
>
>
>
>
> dev.off()
null device
1
>