dist.evaluate.core {EvaluateCore}R Documentation

Distance Measures

Description

Compute average Entry-to-nearest-entry distance (\(E\text{-}EN\)), Accession-to-nearest-entry distance (\(E\text{-}EN\)) and Entry-to-entry distance (\(E\text{-}EN\)) (Odong et al. 2013) to evaluate a core set (CS) selected from an entire collection (EC).

Usage

dist.evaluate.core(data, names, quantitative, qualitative, selected, d = NULL)

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.

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.

d

A distance matrix of class "dist" with individual names in the names column in data as labels. If NULL (default), then a distance matrix is computed using Gower's metric. (Gower 1971).

Value

A data frame with the average values of \(E\text{-}EN\), \(E\text{-}EN\) and \(E\text{-}EN\).

References

Gower JC (1971). “A general coefficient of similarity and some of its properties.” Biometrics, 27(4), 857–871.

Odong TL, Jansen J, van Eeuwijk FA, van Hintum TJL (2013). “Quality of core collections for effective utilisation of genetic resources review, discussion and interpretation.” Theoretical and Applied Genetics, 126(2), 289–305.

See Also

evaluateCore

Examples


####################################
# Use data from R package ccChooser
####################################

library(ccChooser)
data("dactylis_CC")
data("dactylis_EC")

ec <- cbind(genotypes = rownames(dactylis_EC), dactylis_EC[, -1])
ec$genotypes <- as.character(ec$genotypes)
rownames(ec) <- NULL
ec[, c("X1", "X6", "X7")] <- lapply(ec[, c("X1", "X6", "X7")],
                                    function(x) cut(x, breaks = 4))
ec[, c("X1", "X6", "X7")] <- lapply(ec[, c("X1", "X6", "X7")],
                                    function(x) factor(as.numeric(x)))
head(ec)

core <- rownames(dactylis_CC)

quant <- c("X2", "X3", "X4", "X5", "X8")
qual <- c("X1", "X6", "X7")

####################################
# EvaluateCore
####################################

dist.evaluate.core(data = ec, names = "genotypes", quantitative = quant,
                   qualitative = qual, selected = core)


####################################
# Compare with corehunter
####################################

library(corehunter)
# Prepare phenotype dataset
dtype <- c(rep("RD", length(quant)),
           rep("NS", length(qual)))
rownames(ec) <- ec[, "genotypes"]
ecdata <- corehunter::phenotypes(data = ec[, c(quant, qual)],
                                 types = dtype)

# Compute average distances
EN <- evaluateCore(core = rownames(dactylis_CC), data = ecdata,
                   objective = objective("EN", "GD"))
AN <- evaluateCore(core = rownames(dactylis_CC), data = ecdata,
                   objective = objective("AN", "GD"))
EE <- evaluateCore(core = rownames(dactylis_CC), data = ecdata,
                   objective = objective("EE", "GD"))
EN
AN
EE



[Package EvaluateCore version 0.1.2 Index]