boxcoxCensored.object.RdObjects of S3 class "boxcoxCensored" are returned by the EnvStats
function boxcoxCensored, which computes objective values for
user-specified powers, or computes the optimal power for the specified
objective, based on Type I censored data.
Objects of class "boxcoxCensored" are lists that contain
information about the powers that were used, the objective that was used,
the values of the objective for the given powers, and whether an
optimization was specified.
Generic functions that have methods for objects of class
"boxcoxCensored" include: plot, print.
Required Components
The following components must be included in a legitimate list of
class "boxcoxCensored".
Numeric vector containing the powers used in the Box-Cox transformations.
If the value of the optimize component is FALSE, then
lambda contains the values of all of the powers at which the objective
was evaluated. If the value of the optimize component is TRUE,
then lambda is a scalar containing the value of the power that
maximizes the objective.
Numeric vector containing the value(s) of the objective for the given value(s)
of \(\lambda\) that are stored in the component lambda.
Character string indicating the objective that was used. The possible values are
"PPCC" (probability plot correlation coefficient; the default),
"Shapiro-Wilk" (the Shapiro-Wilk goodness-of-fit statistic), and
"Log-Likelihood" (the log-likelihood function).
Logical scalar indicating whether the objective was simply evaluted at the
given values of lambda (optimize=FALSE), or instead
the optimal power transformation was computed within the bounds specified by
lambda (optimize=TRUE).
Numeric vector of length 2 with a names attribute indicating the bounds within
which the optimization took place. When optimize=FALSE, this contains
missing values.
Finite, positive numeric scalar indicating what value of eps was used.
When the absolute value of lambda is less
than eps, lambda is assumed to be 0 for the Box-Cox transformation.
Numeric scalar indicating the number of finite, non-missing observations.
Character string indicating the censoring side. Possible values are
"left" and "right".
Numeric vector containing the censoring levels.
Numeric scalar indicating the percent of observations that are censored.
The name of the data object used for the Box-Cox computations.
The name of the data object indicating which observations are censored.
The number of missing (NA), undefined (NaN) and/or infinite
(Inf, -Inf) values that were removed from the data object
prior to performing the Box-Cox computations.
Optional Component
The following components may optionally be included in a legitimate
list of class "boxcoxCensored". They must be included if you want to
call the function plot.boxcoxCensored and specify Q-Q plots or
Tukey Mean-Difference Q-Q plots.
Numeric vector containing the data actually used for the Box-Cox computations (i.e., the original data without any missing or infinite values).
Logical vector indicating which of the vales in the component data
are censored.
Since objects of class "boxcoxCensored" are lists, you may extract
their components with the $ and [[ operators.
# Create an object of class "boxcoxCensored", then print it out.
# (Note: the call to set.seed simply allows you to reproduce this example.)
set.seed(250)
x.1 <- rlnormAlt(15, mean = 10, cv = 2)
censored.1 <- x.1 < 2
x.1[censored.1] <- 2
x.2 <- rlnormAlt(15, mean = 10, cv = 2)
censored.2 <- x.2 < 4
x.2[censored.2] <- 4
x <- c(x.1, x.2)
censored <- c(censored.1, censored.2)
boxcox.list <- boxcoxCensored(x, censored)
data.class(boxcox.list)
#> [1] "boxcoxCensored"
#[1] "boxcoxCensored"
names(boxcox.list)
#> [1] "lambda" "objective" "objective.name" "optimize"
#> [5] "optimize.bounds" "eps" "data" "censored"
#> [9] "sample.size" "censoring.side" "censoring.levels" "percent.censored"
#> [13] "data.name" "censoring.name" "bad.obs"
# [1] "lambda" "objective" "objective.name"
# [4] "optimize" "optimize.bounds" "eps"
# [7] "data" "censored" "sample.size"
#[10] "censoring.side" "censoring.levels" "percent.censored"
#[13] "data.name" "censoring.name" "bad.obs"
boxcox.list
#>
#> Results of Box-Cox Transformation
#> Based on Type I Censored Data
#> ---------------------------------
#>
#> Objective Name: PPCC
#>
#> Data: x
#>
#> Censoring Variable: censored
#>
#> Censoring Side: left
#>
#> Censoring Level(s): 2 4
#>
#> Sample Size: 30
#>
#> Percent Censored: 26.7%
#>
#> lambda PPCC
#> -2.0 0.8954683
#> -1.5 0.9338467
#> -1.0 0.9643680
#> -0.5 0.9812969
#> 0.0 0.9776834
#> 0.5 0.9471025
#> 1.0 0.8901990
#> 1.5 0.8187488
#> 2.0 0.7480494
#Results of Box-Cox Transformation
#Based on Type I Censored Data
#---------------------------------
#
#Objective Name: PPCC
#
#Data: x
#
#Censoring Variable: censored
#
#Censoring Side: left
#
#Censoring Level(s): 2 4
#
#Sample Size: 30
#
#Percent Censored: 26.7%
#
# lambda PPCC
# -2.0 0.8954683
# -1.5 0.9338467
# -1.0 0.9643680
# -0.5 0.9812969
# 0.0 0.9776834
# 0.5 0.9471025
# 1.0 0.8901990
# 1.5 0.8187488
# 2.0 0.7480494
boxcox.list2 <- boxcox(x, optimize = TRUE)
names(boxcox.list2)
#> [1] "lambda" "objective" "objective.name" "optimize"
#> [5] "optimize.bounds" "eps" "data" "sample.size"
#> [9] "data.name" "bad.obs"
# [1] "lambda" "objective" "objective.name"
# [4] "optimize" "optimize.bounds" "eps"
# [7] "data" "sample.size" "data.name"
#[10] "bad.obs"
boxcox.list2
#>
#> Results of Box-Cox Transformation
#> ---------------------------------
#>
#> Objective Name: PPCC
#>
#> Data: x
#>
#> Sample Size: 30
#>
#> Bounds for Optimization: lower = -2
#> upper = 2
#>
#> Optimal Value: lambda = -0.5826431
#>
#> Value of Objective: PPCC = 0.9755402
#>
#Results of Box-Cox Transformation
#---------------------------------
#
#Objective Name: PPCC
#
#Data: x
#
#Sample Size: 30
#
#Bounds for Optimization: lower = -2
# upper = 2
#
#Optimal Value: lambda = -0.5826431
#
#Value of Objective: PPCC = 0.9755402
#==========
# Clean up
#---------
rm(x.1, censored.1, x.2, censored.2, x, censored, boxcox.list, boxcox.list2)