EnvStats: boxcoxCensored.object – R documentation

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boxcoxCensored.object

S3 Class "boxcoxCensored"

Description

Objects 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.

Details

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.

Value

Required Components
The following components must be included in a legitimate list of class "boxcoxCensored".

`lambda`	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.
`objective`	Numeric vector containing the value(s) of the objective for the given value(s) of λ that are stored in the component `lambda`.
`objective.name`	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).
`optimize`	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`).
`optimize.bounds`	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.
`eps`	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.
`sample.size`	Numeric scalar indicating the number of finite, non-missing observations.
`censoring.side`	Character string indicating the censoring side. Possible values are `"left"` and `"right"`.
`censoring.levels`	Numeric vector containing the censoring levels.
`percent.censored`	Numeric scalar indicating the percent of observations that are censored.
`data.name`	The name of the data object used for the Box-Cox computations.
`censoring.name`	The name of the data object indicating which observations are censored.
`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 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.

`data`	Numeric vector containing the data actually used for the Box-Cox computations (i.e., the original data without any missing or infinite values).
`censored`	Logical vector indicating which of the vales in the component `data` are censored.

Methods

Generic functions that have methods for objects of class "boxcoxCensored" include:
plot, print.

Note

Since objects of class "boxcoxCensored" are lists, you may extract their components with the $ and [[ operators.

Author(s)

Steven P. Millard (EnvStats@ProbStatInfo.com)

Examples

# 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"
  
  names(boxcox.list)
  # [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

  boxcox.list2 <- boxcox(x, optimize = TRUE) 
  names(boxcox.list2)
  # [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

  #==========

  # Clean up
  #---------
  rm(x.1, censored.1, x.2, censored.2, x, censored, boxcox.list, boxcox.list2)

EnvStats

Package for Environmental Statistics, Including US EPA Guidance

v2.4.0

GPL (>= 3)

Authors

Steven P. Millard [aut], Alexander Kowarik [ctb, cre] (<https://orcid.org/0000-0001-8598-4130>)

Initial release

2020-10-20