EvaluateCore (version 0.1.4)

coverage.evaluate.core: Class Coverage

Description

Compute the Class Coverage kim_PowerCore_2007EvaluateCore to compare the distribution frequencies of qualitative traits between entire collection (EC) and core set (CS).

Usage

coverage.evaluate.core(data, names, qualitative, selected, na.omit = TRUE)

Value

The Class Coverage 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.

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.

na.omit

logical. If TRUE, missing values (NA) are ignored and not included as a distinct factor level for analysis. Default is TRUE.

Details

Class Coverage kim_PowerCore_2007EvaluateCore is computed as follows.

Class\, Coverage = ( 1n _i=1^n k_CS_ik_EC_i ) 100

Where, k_CS_i is the number of phenotypic classes in CS for the ith trait, k_EC_i is the number of phenotypic classes in EC for the ith trait and n is the total number of traits.

References

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)