 Computes the scale-adjusted coefficient of variation, acv, (Doring and Reckling, 2018) to account for the systematic dependence of $$\sigma^2$$ from $$\mu$$. The acv is computed as follows: $acv = \frac{\sqrt{10^{\tilde v_i}}}{\mu_i}\times 100$ where $$\tilde v_i$$ is the adjusted logarithm of the variance computed as: $\tilde v_i = a + (b - 2)\frac{1}{n}\sum m_i + 2m_i + e_i$ 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)

## 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.

## 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.

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. doi:10.1016/j.eja.2018.06.007