cr.evaluate.core: Coincidence Rate of Range

In EvaluateCore: Quality Evaluation of Core Collections

Coincidence Rate of Range

Description

Compute the Coincidence Rate of Range (\mjseqnCR) \insertCitehu_methods_2000EvaluateCore (originally described by \insertCitediwan_methods_1995EvaluateCore as Mean range ratio) to compare quantitative traits of the entire collection (EC) and core set (CS). \loadmathjax

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 (\mjseqnCR) is computed as follows.

\mjsdeqn

CR = \left ( \frac1n \sum_i=1^n \fracR_CS_iR_EC_i \right ) \times 100

Where, \mjseqnR_CS_i is the range of the \mjseqnith trait in the CS, \mjseqnR_EC_i is the range of the \mjseqnith trait in the EC and \mjseqnn is the total number of traits.

A representative CS should have a \mjseqnCR value no less than 70% \insertCitediwan_methods_1995EvaluateCore or 80% \insertCitehu_methods_2000EvaluateCore.

Value

The \mjseqnCR value.

References

\insertAllCited

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)

EvaluateCore documentation built on July 3, 2022, 5:06 p.m.