EvaluateCore (version 0.1.4)

cr.evaluate.core: Coincidence Rate of Range

Description

Compute the following metrics to compare quantitative traits of the entire collection (EC) and core set (CS).

• Changeable Rate of Maximum (CR_) wang_assessment_2007EvaluateCore

• Changeable Rate of Minimum (CR_) wang_assessment_2007EvaluateCore

• Changeable Rate of Mean (CR_) wang_assessment_2007EvaluateCore

Usage

cr.evaluate.core(data, names, quantitative, selected)

Value

The CR value.

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 = ( 1n _i=1^n R_CS_iR_EC_i ) 100

Where, R_CS_i is the range of the ith trait in the CS, R_EC_i is the range of the ith 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_methods_1995EvaluateCore or 80% hu_methods_2000EvaluateCore.

The Changeable Rate of Maximum (CR_) is computed as follows.

CR_ = ( 1n _i=1^n _CS_i_EC_i ) 100

Where, _CS_i is the maximum value of the ith trait in the CS, _EC_i is the maximum value of the ith trait in the EC and n is the total number of traits.

The Changeable Rate of Minimum (CR_) is computed as follows.

CR_ = ( 1n _i=1^n _CS_i_EC_i ) 100

Where, _CS_i is the minimum value of the ith trait in the CS, _EC_i is the minimum value of the ith trait in the EC and n is the total number of traits.

The Changeable Rate of Mean (CR_) is computed as follows.

CR_ = ( 1n _i=1^n _CS_i_EC_i ) 100

Where, _CS_i is the mean value of the ith trait in the CS, _EC_i is the mean value of the ith trait in the EC and n is the total number of traits.

References

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)