cr.evaluate.core {EvaluateCore}

Coincidence Rate of Range

Description

Compute the Coincidence Rate of Range (\(CR\)) (Hu et al. 2000) (originally described by (Diwan et al. 1995) as Mean range ratio) to compare quantitative traits of the entire collection (EC) and core set (CS).

Usage

cr.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.

Details

The Coincidence Rate of Range (\(CR\)) is computed as follows.

\[CR = \left ( \frac{1}{n} \sum_{i=1}^{n} \frac{R_{CS_{i}}}{R_{EC_{i}}} \right ) \times 100\]

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

A representative CS should have a \(CR\) value no less than 70% (Diwan et al. 1995) or 80% (Hu et al. 2000).

Value

The \(CR\) value.

References

Diwan N, McIntosh MS, Bauchan GR (1995). “Methods of developing a core collection of annual Medicago species.” Theoretical and Applied Genetics, 90(6), 755–761.

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.

See Also

wilcox.test

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)))

cr.evaluate.core(data = ec, names = "genotypes",
                 quantitative = quant, selected = core)