% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CoefVarCI.R
\name{CoefVarCI}
\alias{CoefVarCI}
\title{R6 Confidence Intervals for the Coefficient of Variation (cv)}
\arguments{
\item{x}{An \code{R} object. Currently there are methods for numeric vectors}

\item{na.rm}{a logical value indicating whether \code{NA} values should be
stripped before the computation proceeds.}

\item{digits}{integer indicating the number of decimal places to be used.}

\item{method}{a scalar representing the type of confidence intervals
required. The value should be any of the values "kelley_ci", "mckay_ci",
"miller_ci", "vangel_ci", "mahmoudvand_hassani_ci", "equal_tailed_ci",
"shortest_length_ci", "normal_approximation_ci", "norm_ci","basic_ci", or
"all_ci".}

\item{alpha}{The allowed type I error probability}

\item{R}{integer indicating the number of bootstrap replicates.}

\item{correction}{returns the unbiased estimate of the coefficient of
variation if TRUE is determined.}
}
\value{
An object of type "list" which contains the estimate, the intervals,
  and the computation method. It has two main components:

\describe{ \item{$method}{ A description of statistical method used
  for the computations. } \item{$statistics}{ A data frame representing three
  vectors: est/, lower and upper limits of confidence interval \code{(CI)};
  additional description vector is provided when "all" is selected: \cr \cr
  \strong{est:}{ \code{cv*100} } \cr \cr \strong{Kelley Confidence
  Interval:}{ Thanks to package \pkg{MBESS} \code{[2]} for the
  computation of confidence limits for the noncentrality parameter from a
  \emph{t} distribution \link[MBESS]{conf.limits.nct} \code{[3]}. } \cr \cr
  \strong{McKay Confidence Interval:}{ The intervals calculated by the method
  introduced by McKay \code{[4]}, using chi-square distribution. } \cr \cr
  \strong{Miller Confidence Interval:}{ The intervals calculated by the
  method introduced by Miller \code{[5]}, using the standard normal
  distribution. } \cr \cr \strong{Vangel Confidence Interval:}{ Vangel
  \code{[6]} proposed a method for the calculation of CI for \emph{cv}; which
  is a modification on McKay’s CI. } \cr \cr \strong{Mahmoudvand-Hassani
  Confidence Interval:}{ Mahmoudvand and Hassani \code{[7]} proposed a new CI
  for \emph{cv}; which is obtained using ranked set sampling \emph{(RSS)} }
  \cr \cr \strong{Normal Approximation Confidence Interval:}{ Wararit
  Panichkitkosolkul \code{[8]} proposed another CI for \emph{cv}; which is a
  normal approximation. } \cr \cr \strong{Shortest-Length Confidence
  Interval:}{ Wararit Panichkitkosolkul \code{[8]} proposed another CI for
  \emph{cv}; which is obtained through minimizing the length of CI. } \cr \cr
  \strong{Equal-Tailed Confidence Interval:}{ Wararit Panichkitkosolkul
  \code{[8]} proposed another CI for \emph{cv}; which is obtained using
  chi-square distribution. } \cr \cr \strong{Bootstrap Confidence
  Intervals:}{ Thanks to package \pkg{boot} by Canty & Ripley \code{[9]} we
  can obtain bootstrap CI around \emph{cv} using \link[boot]{boot.ci}. } \cr
  \cr } }
}
\description{
The R6 class \code{CoefVarCI} for the confidence intervals of
  coefficient of variation (cv)
}
\details{
\describe{ \item{\strong{Coefficient of Variation}}{ The \emph{cv}
  is a measure of relative dispersion representing the degree of variability
  relative to the mean \code{[1]}. Since \eqn{cv} is unitless, it is useful
  for comparison of variables with different units. It is also a measure of
  homogeneity \code{[1]}. } }
}
\examples{
y <- c(
    0.2, 0.5, 1.1, 1.4, 1.8, 2.3, 2.5, 2.7, 3.5, 4.4,
    4.6, 5.4, 5.4, 5.7, 5.8, 5.9, 6.0, 6.6, 7.1, 7.9
)
CoefVarCI$new(x = y)$kelley_ci()
cv_y <- CoefVarCI$new(
   x = y,
   alpha = 0.05,
   R = 1000,
   digits = 2,
   correction = TRUE
)
cv_y$kelley_ci()
cv_y$mckay_ci()
R6::is.R6(cv_y)
}
\references{
\code{[1]} Albatineh, AN., Kibria, BM., Wilcox, ML., & Zogheib,
  B, 2014, Confidence interval estimation for the population coefficient of
  variation using ranked set sampling: A simulation study, Journal of Applied
  Statistics, 41(4), 733–751, DOI:
  \href{http://doi.org/10.1080/02664763.2013.847405}{http://doi.org/10.1080/02664763.2013.847405}

\code{[2]} Kelley, K., 2018, MBESS: The MBESS R Package. R
  package version 4.4. 3.

\code{[3]} Kelley, K., 2007, Sample size planning for the
  coefficient of variation from the accuracy in parameter estimation
  approach, Behavior Research Methods, 39(4), 755–766, DOI:
  \href{http://doi.org/10.3758/BF03192966}{http://doi.org/10.3758/BF03192966}

\code{[4]} McKay, AT., 1932, Distribution of the Coefficient of
  Variation and the Extended“ t” Distribution, Journal of the Royal
  Statistical Society, 95(4), 695–698

\code{[5]} Miller, E., 1991, Asymptotic test statistics for
  coefficients of variation, Communications in Statistics-Theory and Methods,
  20(10), 3351–3363

\code{[6]} Vangel, MG., 1996, Confidence intervals for a normal
  coefficient of variation, The American Statistician, 50(1), 21–26

\code{[7]} Mahmoudvand, R., & Hassani, H., 2009, Two new
  confidence intervals for the coefficient of variation in a normal
  distribution, Journal of Applied Statistics, 36(4), 429–442

\code{[8]} Panichkitkosolkul, W., 2013, Confidence Intervals for
  the Coefficient of Variation in a Normal Distribution with a Known
  Population Mean, Journal of Probability and Statistics, 2013, 1–11,
  \href{http://doi.org/10.1155/2013/324940}{http://doi.org/10.1155/2013/324940}

\code{[9]} Canty, A., & Ripley, B., 2017, boot: Bootstrap R
  (S-Plus) Functions, R package version 1.3-20
}
