Compute the Class Coverage (Kim et al. 2007) to compare the distribution frequencies of qualitative traits between entire collection (EC) and core set (CS).
coverage.evaluate.core(data, names, qualitative, 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 qualitative traits as a character vector.
Character vector with the names of individuals selected in
core collection and present in the names column.
The Class Coverage value.
Class Coverage (Kim et al. 2007) is computed as follows.
\[Class\, Coverage = \left ( \frac{1}{n} \sum_{i=1}^{n} \frac{k_{CS_{i}}}{k_{EC_{i}}} \right ) \times 100\]
Where, \(k_{CS_{i}}\) is the number of phenotypic classes in CS for the \(i\)th trait, \(k_{EC_{i}}\) is the number of phenotypic classes in EC for the \(i\)th trait and \(n\) is the total number of traits.
Kim K, Chung H, Cho G, Ma K, Chandrabalan D, Gwag J, Kim T, Cho E, Park Y (2007). “PowerCore: A program applying the advanced M strategy with a heuristic search for establishing core sets.” Bioinformatics, 23(16), 2155–2162.
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)))
coverage.evaluate.core(data = ec, names = "genotypes",
qualitative = qual, selected = core)
#> [1] 92.70833