Description Arguments Details Value References Examples
The R6 class CoefVarCI
for the confidence intervals of
coefficient of variation (cv)
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 "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". |
alpha |
The allowed type I error probability |
R |
integer indicating the number of bootstrap replicates. |
correction |
returns the unbiased estimate of the coefficient of variation if TRUE is determined. |
CV = σ/μ
where σ and μ are standard deviation and mean, respectively. The cv is a measure of relative dispersion representing the degree of variability relative to the mean [1]. Since cv is unitless, it is useful for comparison of variables with different units. It is also a measure of homogeneity [1].
An object of type "list" which contains the estimate, the intervals, and the computation method. It has two main components:
A description of statistical method used for the computations.
A data frame representing three vectors: est, lower and upper limits
of (1-α)% confidence interval (CI)
;
additional description vector is provided when "all" is selected:
est:
(sd/mean)*100
Kelley Confidence Interval:
Thanks to package MBESS [2] for the
computation of confidence limits for the noncentrality parameter from
a t distribution conf.limits.nct [3].
McKay Confidence Interval:
The intervals calculated by the method introduced by McKay [4],
using χ^2 distribution.
Miller Confidence Interval:
The intervals calculated by the method introduced by Miller [5],
using the standard normal distribution.
Vangel Confidence Interval:
Vangel [6] proposed a method for the calculation of CI for cv;
which is a modification on McKay’s CI.
Mahmoudvand-Hassani Confidence Interval:
Mahmoudvand and Hassani [7] proposed a new CI for cv; which
is obtained using ranked set sampling (RSS)
Normal Approximation Confidence Interval:
Wararit Panichkitkosolkul [8] proposed another CI for cv;
which is a normal approximation.
Shortest-Length Confidence Interval:
Wararit Panichkitkosolkul [8] proposed another CI for cv;
which is obtained through minimizing the length of CI.
Equal-Tailed Confidence Interval:
Wararit Panichkitkosolkul [8] proposed another CI for cv;
which is obtained using χ^2 distribution.
Bootstrap Confidence Intervals:
Thanks to package boot by Canty & Ripley [9] we can obtain
bootstrap CI around cv using boot.ci.
[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: http://doi.org/10.1080/02664763.2013.847405
[2] Kelley, K., 2018, MBESS: The MBESS R Package. R package version 4.4. 3., Retrieved from http://cran.r-project.org/package=MBESS
[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: http://doi.org/10.3758/BF03192966
[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
[5] Miller, E., 1991, Asymptotic test statistics for coefficients of variation, Communications in Statistics-Theory and Methods, 20(10), 3351–3363
[6] Vangel, MG., 1996, Confidence intervals for a normal coefficient of variation, The American Statistician, 50(1), 21–26
[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
[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, http://doi.org/10.1155/2013/324940
[9] Canty, A., & Ripley, B., 2017, boot: Bootstrap R (S-Plus) Functions, R package version 1.3-20
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | 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$mckay_ci()
cv_y$miller_ci()
cv_y$vangel_ci()
cv_y$mh_ci()
cv_y$equal_ci()
cv_y$shortest_ci()
cv_y$normaapprox_ci()
cv_y$norm_ci()
cv_y$basic_ci()
cv_y$perc_ci()
cv_y$bca_ci()
cv_y$all_ci()
R6::is.R6(cv_y)
|
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