Compute the Class Coverage (Kim et al. 2007) to compare the distribution frequencies of qualitative traits between entire collection (EC) and core set (CS).
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.
- qualitative
Name of columns with the qualitative traits as a character vector.
- selected
Character vector with the names of individuals selected in core collection and present in the
namescolumn.
Details
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.
References
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.
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)))
coverage.evaluate.core(data = ec, names = "genotypes",
qualitative = qual, selected = core)
#> [1] 92.70833