dist.evaluate.core: Distance Measures

Description

Compute average Entry-to-nearest-entry distance (), Accession-to-nearest-entry distance () and Entry-to-entry distance () odong_quality_2013EvaluateCore 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.

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. gowerGeneralCoefficientSimilarity1971EvaluateCore.

Value

A data frame with the average values of , and .

References

Examples

Run this code

# NOT RUN {
####################################
# 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)

# }
# NOT RUN {
####################################
# 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
# }
# NOT RUN {
# }