CoefQuartVarCI: R6 Confidence Intervals for the Coefficient of Quartile...

In MaaniBeigy/DescObs: Versatile Exploration of Data

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.

Details

Coefficient of Quartile Variation

cqv = ((q3-q1)/(q3 + q1))*100 ,

where q3 and q1 are third quartile (i.e., 75th percentile) and first quartile (i.e., 25th percentile), respectively. 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].

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:

((q3-q1)/(q3 + q1))*100

Bonett 95% CI:

exp{ln(D/S)C +/- (z(1 - alpha/2) * sqrt(v))},

where C = n/(n - 1) is a centering adjustment which helps to equalize the tail error probabilities. For this confidence interval, D = q3 - q1 and S = q3 + q1; z(1 - alpha/2) is the 1 - alpha/2 quantile of the standard normal distribution [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.

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

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()
cqv_y$norm_ci()
cqv_y$basic_ci()
cqv_y$perc_ci()
cqv_y$bca_ci()
cqv_y$all_ci()
R6::is.R6(cqv_y)

MaaniBeigy/DescObs documentation built on May 23, 2019, 9:37 a.m.