Compute the Variable Rate of Coefficient of Variation (\(VR\)) (Hu et al. 2000) to compare quantitative traits of the entire collection (EC) and core set (CS).
vr.evaluate.core(data, names, quantitative, selected)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.
Name of column with the individual names as a character string.
Name of columns with the quantitative traits as a character vector.
Character vector with the names of individuals selected in
core collection and present in the names column.
The \(VR\) value.
The Variable Rate of Coefficient of Variation (\(VR\)) is computed as follows.
\[VR = \left ( \frac{1}{n} \sum_{i=1}^{n} \frac{CV_{CS_{i}}}{CV_{EC_{i}}} \right ) \times 100\]
Where, \(CV_{CS_{i}}\) is the coefficients of variation for the \(i\)th trait in the CS, \(CV_{EC_{i}}\) is the coefficients of variation for the \(i\)th trait in the EC and \(n\) is the total number of traits
Hu J, Zhu J, Xu HM (2000). “Methods of constructing core collections by stepwise clustering with three sampling strategies based on the genotypic values of crops.” Theoretical and Applied Genetics, 101(1), 264–268.
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)))
vr.evaluate.core(data = ec, names = "genotypes",
quantitative = quant, selected = core)
#> [1] 111.6594