cvcqv (version 1.0.0)

CoefQuartVarCI: R6 Confidence Intervals for the Coefficient of Quartile Variation (cqv)

Description

The R6 class CoefQuartVarCI for the confidence intervals of coefficient of quartile variation (cqv)

Arguments

x

An R object. Currently there are methods for numeric vectors

na.rm

a logical value indicating whether NA values should be stripped before the computation proceeds.

digits

integer indicating the number of decimal places to be used.

methods

the available computation methods of confidence intervals are: "bonett_ci", "norm_ci", "basic_ci", "perc_ci", "bca_ci" or "all_ci".

R

integer indicating the number of bootstrap replicates.

Value

An object of type "list" which contains the estimate, the intervals, and the computation method. It has two components:

$method

A description of statistical method used for the computations.

$statistics

A data frame representing three vectors: est, lower and upper limits of 95% confidence interval (CI): est: cqv*100 Bonett 95% CI: It uses a centering adjustment which helps to equalize the tail error probabilities [1, 2]. Normal approximation 95% CI: The intervals calculated by the normal approximation [3, 4], using boot.ci. Basic bootstrap 95% CI: The intervals calculated by the basic bootstrap method [3, 4], using boot.ci. Bootstrap percentile 95% CI: The intervals calculated by the bootstrap percentile method [3, 4], using boot.ci. Adjusted bootstrap percentile (BCa) 95% CI: The intervals calculated by the adjusted bootstrap percentile (BCa) method [3, 4], using boot.ci.

Details

Coefficient of Quartile Variation

The cqv is a measure of relative dispersion that is based on interquartile range (iqr). Since \(cqv\) is unitless, it is useful for comparison of variables with different units. It is also a measure of homogeneity [1, 2].

References

[1] Bonett, DG., 2006, Confidence interval for a coefficient of quartile variation, Computational Statistics & Data Analysis, 50(11), 2953-7, DOI: http://doi.org/10.1016/j.csda.2005.05.007

[2] Altunkaynak, B., Gamgam, H., 2018, Bootstrap confidence intervals for the coefficient of quartile variation, Simulation and Computation, 1-9, DOI: http://doi.org/10.1080/03610918.2018.1435800

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

[4] Davison, AC., & Hinkley, DV., 1997, Bootstrap Methods and Their Applications. Cambridge University Press, Cambridge. ISBN 0-521-57391-2

Examples

# NOT RUN {
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
)
CoefQuartVarCI$new(x = y)$bonett_ci()
cqv_y <- CoefQuartVarCI$new(
    x = y,
    alpha = 0.05,
    R = 1000,
    digits = 2
)
cqv_y$bonett_ci()
R6::is.R6(cqv_y)
# }