metan (version 1.19.0)

acv: Adjusted Coefficient of Variation

Description

Computes the scale-adjusted coefficient of variation, acv, (Doring and Reckling, 2018) to account for the systematic dependence of ^2 from . The acv is computed as follows: acv = 10^ v_i_i 100 where v_i is the adjusted logarithm of the variance computed as: v_i = a + (b - 2)1n m_i + 2m_i + e_i being a and b the coefficients of the linear regression for log_10 of the variance over the log_10 of the mean; m_i is the log_10 of the mean, and e_i is the Power Law Residuals (POLAR), i.e., the residuals for the previously described regression.

Usage

acv(mean, var, na.rm = FALSE)

Value

A tibble with the following columns

• mean The mean values.

• var The variance values;

• log10_mean The base 10 logarithm of mean;

• log10_var The base 10 logarithm of variance;

• POLAR The Power Law Residuals;

• cv The standard coefficient of variation;

• acv Adjusted coefficient of variation.

Arguments

mean

A numeric vector with mean values.

var

A numeric vector with variance values.

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

Author

Tiago Olivoto

References

Doring, T.F., and M. Reckling. 2018. Detecting global trends of cereal yield stability by adjusting the coefficient of variation. Eur. J. Agron. 99: 30-36. tools:::Rd_expr_doi("10.1016/j.eja.2018.06.007")

Examples

# \donttest{
################# Table 1 from Doring and Reckling (2018)  ###########

# Mean values
u <- c(0.5891, 0.6169, 0.7944, 1.0310, 1.5032, 3.8610, 4.6969, 6.1148,
       7.1526, 7.5348, 1.2229, 1.6321, 2.4293, 2.5011, 3.0161)

# Variances
v <- c(0.0064, 0.0141, 0.0218, 0.0318, 0.0314, 0.0766, 0.0620, 0.0822,
       0.1605, 0.1986, 0.0157, 0.0593, 0.0565, 0.1997, 0.2715)

library(metan)
acv(u, v)
# }