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)

## Arguments

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.

## Value

The Synthetic Variation Coefficient values for EC and CS

## Details

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.

## Examples


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