coverage.evaluate.core {EvaluateCore}

Class Coverage

Description

Compute the Class Coverage (Kim et al. 2007) to compare the distribution frequencies of qualitative traits between entire collection (EC) and core set (CS).

Usage

coverage.evaluate.core(data, names, qualitative, 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

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 names column.

Details

Class Coverage (Kim et al. 2007) is computed as follows.

\[Class\, Coverage = \left ( \frac{1}{n} \sum_{i=1}^{n} \frac{A_{CS_{i}}}{A_{EC_{i}}} \right ) \times 100\]

Where, \(A_{CS_{i}}\) is the sets of categories in the CS for the \(i\)th trait, \(A_{EC_{i}}\) is the sets of categories in the EC for the \(i\)th trait and \(n\) is the total number of traits.

Value

The Class Coverage value.

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)