cv {goeveg}

Coefficient of variation (CV)

Description

Compute the coefficient of variation (CV). The CV, also known as relative standard deviation (RSD), is a standardized measure of dispersion of a probability distribution or frequency distribution. It is defined as the ratio of the standard deviation to the mean and is often expressed as a percentage. In contrast to the standard deviation, it enables comparison between datasets as the CV is independent of the unit in which the measurement has been taken. If na.rm is TRUE then missing values are removed before computation proceeds.

Usage

cv(x, na.rm = FALSE)

Arguments

x

a numeric vector

na.rm

logical. Should missing values be removed?

Value

A numeric scalar – the sample coefficient of variation.

Details

The coefficient of variation (CV) should be computed only for data measured on a ratio scale (i.e. data with an absolute zero). The CV may not have any meaning for data on an interval scale.

According to Dormann 2017 CV-values below 0.05 (5%) indicate very high precision of the data, values above 0.2 (20%) low precision. However, this is considered as a rule of thumb. In studies of highly variable systems (e.g. some ecological studies) CV values above 1 may occur.

The CV of a zero-length vector (after removal of NAs if na.rm = TRUE) is not defined and gives an error. If there is only a single value, sd is NA and cv returns NA.

References

Dormann, C. (2017). Parametrische Statistik. Verteilungen, maximum likelihood und GLM in R. Springer. doi:10.1007/978-3-662-54684-0

Frost, J. (2023). Coefficient of variation in statistics. Statistics by Jim. https://statisticsbyjim.com/basics/coefficient-variation/.

See Also

sd

Examples

## Calculate CV for variable soil depth
cv(schedenenv$soil_depth)