pdfdist.evaluate.core {EvaluateCore}

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).

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)