pdfdist.evaluate.core: Distance Between Probability Distributions

In EvaluateCore: Quality Evaluation of Core Collections

Distance Between Probability Distributions

Description

Compute Kullback-Leibler \insertCitekullback_information_1951EvaluateCore, Kolmogorov-Smirnov \insertCitekolmogorov_sulla_1933,smirnov_table_1948EvaluateCore and Anderson-Darling distances \insertCiteanderson_asymptotic_1952EvaluateCore between the probability distributions of collection (EC) and core set (CS) for quantitative traits. \loadmathjax

Usage

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

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.

Value

A data frame with the following columns.

Trait	The quantitative trait.
KL_Distance	The Kullback-Leibler distance \insertCitekullback_information_1951EvaluateCore between EC and CS.
KS_Distance	The Kolmogorov-Smirnov distance \insertCitekolmogorov_sulla_1933,smirnov_table_1948EvaluateCore between EC and CS.
KS_pvalue	The p value of the Kolmogorov-Smirnov distance.
AD_Distance	Anderson-Darling distance \insertCiteanderson_asymptotic_1952EvaluateCore between EC and CS.
AD_pvalue	The p value of the Anderson-Darling distance.
KS_significance	The significance of the Kolmogorov-Smirnov distance (*: p \mjseqn\leq 0.01; **: p \mjseqn\leq 0.05; ns: p \mjseqn> 0.05).
AD_pvalue	The significance of the Anderson-Darling distance (*: p \mjseqn\leq 0.01; **: p \mjseqn\leq 0.05; ns: p \mjseqn> 0.05).

See Also

KL.plugin, ks.test, ad.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)))

pdfdist.evaluate.core(data = ec, names = "genotypes",
                      quantitative = quant, selected = core)

EvaluateCore documentation built on July 3, 2022, 5:06 p.m.