Compute the Synthetic Variation Coefficient (\(CV\%\)) (Dong 1998; Dong et al. 2001) to compare quantitative traits of the entire collection (EC) and core set (CS).

`scv.evaluate.core(data, names, quantitative, selected)`

- data
The data as a data frame object. The data frame should possess one row per individual and columns with the individual names and multiple trait/character data.

- names
Name of column with the individual names as a character string

- quantitative
Name of columns with the quantitative traits as a character vector.

- selected
Character vector with the names of individuals selected in core collection and present in the

`names`

column.

The Synthetic Variation Coefficient values for EC and CS

Synthetic Variation Coefficient (\(CV\%\)) (Dong 1998; Dong et al. 2001) is computed as follows for the core set (CS).

\[CV(\%) = \left ( \frac{1}{n} \sum_{i=1}^{n} \frac{SE_{j}}{\mu_{i}} \right ) \times 100\]

Where, \(SE_{i}\) is the standard error of the \(i\)th trait, \(\mu_{i}\) is the mean of the \(i\)th trait and \(n\) is the total number of traits.

Dong YS (1998).
“Exploration on genetic diversity center for cultivated soybean in China.”
*Chinese Crops Journal*, **1**, 18--19.

Dong YS, Zhuang BC, Zhao LM, Sun H, He MY (2001).
“The genetic diversity of annual wild soybeans grown in China.”
*Theoretical and Applied Genetics*, **103**(1), 98--103.

```
data("cassava_CC")
data("cassava_EC")
ec <- cbind(genotypes = rownames(cassava_EC), cassava_EC)
ec$genotypes <- as.character(ec$genotypes)
rownames(ec) <- NULL
core <- rownames(cassava_CC)
quant <- c("NMSR", "TTRN", "TFWSR", "TTRW", "TFWSS", "TTSW", "TTPW", "AVPW",
"ARSR", "SRDM")
qual <- c("CUAL", "LNGS", "PTLC", "DSTA", "LFRT", "LBTEF", "CBTR", "NMLB",
"ANGB", "CUAL9M", "LVC9M", "TNPR9M", "PL9M", "STRP", "STRC",
"PSTR")
ec[, qual] <- lapply(ec[, qual],
function(x) factor(as.factor(x)))
scv.evaluate.core(data = ec, names = "genotypes",
quantitative = quant, selected = core)
#> EC_SCV CS_SCV
#> 10.75148 41.67945
```