shannon.evaluate.core-deprecated {EvaluateCore}R Documentation

Shannon-Weaver Diversity Index

Description

Compute the Shannon-Weaver Diversity Index (\(H'\)), Maximum diversity (\(H'_{max}\)) and Shannon Equitability Index (\(E_{H}\)) (Shannon and Weaver 1949) to compare the phenotypic diversity for qualitative traits between entire collection (EC) and core set (CS).

Usage

shannon.evaluate.core(data, names, qualitative, selected)

Details

Shannon-Weaver Diversity Index (\(H'\)) is computed as follows.

\[H' = -\sum_{i=1}^{k}p_{i} \ln(p_{i})\]

Where \(p_{i}\) denotes the proportion in the group \(k\).

The maximum value of the index (\(H'_{max}\)) is \(\ln(k)\). This value occurs when each group has the same frequency.

The Shannon equitability index (\(E_{H}\)) is the Shannon diversity index divided by the maximum diversity.

\[E_{H} = \frac{H'}{\ln{(k)}}\]

Value

A data frame with the following columns.

Trait

The qualitative trait.

EC_H

The Shannon-Weaver Diversity Index (\(H'\)) for EC.

EC_H

The Shannon-Weaver Diversity Index (\(H'\)) for CS.

EC_Hmax

The Maximum diversity value (\(H'_{max}\)) for EC.

CS_Hmax

The Maximum diversity value (\(H'_{max}\)) for CS.

EC_EH

The Shannon Equitability Index (\(E_{H}\)) for EC.

CS_EH

The Shannon Equitability Index (\(E_{H}\)) for CS.

References

Shannon CE, Weaver W (1949). The Mathematical Theory of Communication, number v. 2 in The Mathematical Theory of Communication. University of Illinois Press.

See Also

shannon

EvaluateCore-deprecated

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)))

shannon.evaluate.core(data = ec, names = "genotypes",
                      qualitative = qual, selected = core)


[Package EvaluateCore version 0.1.3 Index]