Description Usage Arguments Value Author(s) References Examples
Computes the scaleadjusted coefficient of variation, acv, (Doring and Reckling, 2018) to account for the systematic dependence of \mjseqn\sigma^2 from \mjseqn\mu. The acv is computed as follows: \mjsdeqnacv = \frac\sqrt10^\tilde v_i\mu_i\times 100 where \mjseqn\tilde v_i is the adjusted logarithm of the variance computed as: \mjsdeqn\tilde v_i = a + (b  2)\frac1n\sum m_i + 2m_i + e_i being \mjseqna and \mjseqnb the coefficients of the linear regression for \mjseqnlog_10 of the variance over the \mjseqnlog_10 of the mean; \mjseqn m_i is the \mjseqnlog_10 of the mean, and \mjseqn e_i is the Power Law Residuals (POLAR), i.e., the residuals for the previously described regression.
1 
mean 
A numeric vector with mean values. 
var 
A numeric vector with variance values. 
na.rm 
If 
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.
Tiago Olivoto tiagoolivoto@gmail.com
Doring, T.F., and M. Reckling. 2018. Detecting global trends of cereal yield stability by adjusting the coefficient of variation. Eur. J. Agron. 99: 3036. doi: 10.1016/j.eja.2018.06.007
1 2 3 4 5 6 7 8 9 10 11 12  ################# 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)

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