# cqv_versatile: Coefficient of Quartile Variation (cqv) In cvcqv: Coefficient of Variation (CV) with Confidence Intervals (CI)

## Description

Versatile function for the 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. `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.

## 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]`.

## 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.

## 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

 ```1 2 3 4 5 6 7``` ```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") ```

### Example output

```Loading required package: dplyr

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

filter, lag

The following objects are masked from ‘package:base’:

intersect, setdiff, setequal, union

\$method
[1] "cqv = (q3-q1)/(q3+q1)"

\$statistics
est
45.6

\$method
[1] "cqv = (q3-q1)/(q3+q1)"

\$statistics
est
45.62

\$method
[1] "cqv with Bonett 95% CI"

\$statistics
est lower upper
45.62 24.78 77.33
```

cvcqv documentation built on Aug. 6, 2019, 5:10 p.m.