# pdfdist.evaluate.core: Distance Between Probability Distributions In EvaluateCore: Quality Evaluation of Core Collections

 pdfdist.evaluate.core R Documentation

## 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).

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.