pdfdist.evaluate.core {EvaluateCore} R Documentation

## Distance Between Probability Distributions

### Description

Compute Kullback-Leibler (Kullback and Leibler 1951), Kolmogorov-Smirnov (Kolmogorov 1933; Smirnov 1948) and Anderson-Darling distances (Anderson and Darling 1952) between the probability distributions of collection (EC) and core set (CS) for quantitative traits.

### 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 (Kullback and Leibler 1951) between EC and CS. KS_Distance The Kolmogorov-Smirnov distance (Kolmogorov 1933; Smirnov 1948) between EC and CS. KS_pvalue The p value of the Kolmogorov-Smirnov distance. AD_Distance Anderson-Darling distance (Anderson and Darling 1952) between EC and CS. AD_pvalue The p value of the Anderson-Darling distance. KS_significance The significance of the Kolmogorov-Smirnov distance (*: p $$\leq$$ 0.01; **: p $$\leq$$ 0.05; ns: p $$>$$ 0.05). AD_pvalue The significance of the Anderson-Darling distance (*: p $$\leq$$ 0.01; **: p $$\leq$$ 0.05; ns: p $$>$$ 0.05).

KL.plugin, ks.test, ad.test

### 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)))

core <- rownames(dactylis_CC)

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

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

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



[Package EvaluateCore version 0.1.2 Index]