Description Arguments Details Value References Examples
Versatile function for the coefficient of quartile variation (cqv)
x 
An 
na.rm 
a logical value indicating whether 
digits 
integer indicating the number of decimal places to be used. 
method 
a scalar representing the type of confidence intervals required. The value should be any of the values "bonett", "norm", "basic", "perc", "bca" or "all". 
R 
integer indicating the number of bootstrap replicates. 
cqv = ((q3q1)/(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].
An object of type "list" which contains the estimate, the intervals, and the computation method. It has two components:
A description of statistical method used for the computations.
A data frame representing three vectors: est, lower and upper limits
of 95% confidence interval (CI)
:
est:
((q3q1)/(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.
[1] Bonett, DG., 2006, Confidence interval for a coefficient of quartile variation, Computational Statistics & Data Analysis, 50(11), 29537, 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, 19, DOI: http://doi.org/10.1080/03610918.2018.1435800
[3] Canty, A., & Ripley, B, 2017, boot: Bootstrap R (SPlus) Functions. R package version 1.320.
[4] Davison, AC., & Hinkley, DV., 1997, Bootstrap Methods and Their Applications. Cambridge University Press, Cambridge. ISBN 0521573912
1 2 3 4 5 6 7 8 9 10 11 12  x < 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
)
cqv_versatile(x)
cqv_versatile(x, na.rm = TRUE, digits = 2)
cqv_versatile(x, na.rm = TRUE, digits = 2, method = "bonett")
cqv_versatile(x, na.rm = TRUE, digits = 2, method = "norm")
cqv_versatile(x, na.rm = TRUE, digits = 2, method = "basic")
cqv_versatile(x, na.rm = TRUE, digits = 2, method = "perc")
cqv_versatile(x, na.rm = TRUE, digits = 2, method = "bca")
cqv_versatile(x, na.rm = TRUE, digits = 2, method = "all")

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