Compute the Variance of Phenotypic Frequency (\(VPF\)) (Li et al. 2002) to compare qualitative traits between entire collection (EC) and core set (CS).

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



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.


Name of column with the individual names as a character string


Name of columns with the qualitative traits as a character vector.


Character vector with the names of individuals selected in core collection and present in the names column.


The Variance of Phenotypic Frequency values for EC and CS.


Variance of Phenotypic Frequency (\(VPF\)) (Li et al. 2002) is computed as follows.

\[VPF = \frac{1}{n} \sum_{i=1}^{n}\left ( \frac{\sum_{j=1}^{k} (p_{ij} - \overline{p_{i}})^{2}}{k - 1} \right )\]

Where, \(p_{ij}\) denotes the proportion/fraction/frequency of accessions in the \(i\)th phenotypic class for the \(i\)th trait, \(\overline{p_{i}}\) is the mean frequency of phenotypic classes for the \(i\)th trait, \(k\) is the number of phenotypic classes for the \(i\)th trait and \(n\) is the total number of traits.


Li Z, Zhang H, Zeng Y, Yang Z, Shen S, Sun C, Wang X (2002). “Studies on sampling schemes for the establishment of corecollection of rice landraces in Yunnan, China.” Genetic Resources and Crop Evolution, 49(1), 67--74.



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",

ec[, qual] <- lapply(ec[, qual],
                     function(x) factor(as.factor(x)))

vpf.evaluate.core(data = ec, names = "genotypes",
                  qualitative = qual, selected = core)
#>     EC_VPF     CS_VPF 
#> 0.04573206 0.03154146