EvaluateCore (version 0.1.4)

dist.evaluate.core: Distance Measures

Description

Compute average Entry-to-nearest-entry distance (E-ENE-ENE-EN), Accession-to-nearest-entry distance (A-ENE-ENA-EN) and Entry-to-entry distance (E-EE-ENE-E) 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)

Value

A data frame with the average values of E-ENE-ENE-EN, A-ENE-ENA-EN and E-EE-ENE-E.

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_general_1971EvaluateCore.

References

See Also

evaluateCore

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)))

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

# \donttest{
####################################
# 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(cassava_CC), data = ecdata,
                   objective = objective("EN", "GD"))
AN <- evaluateCore(core = rownames(cassava_CC), data = ecdata,
                   objective = objective("AN", "GD"))
EE <- evaluateCore(core = rownames(cassava_CC), data = ecdata,
                   objective = objective("EE", "GD"))
EN
AN
EE
# }