Compute the Variance of Phenotypic Frequency (\(VPF\)) (Li et al. 2002) to compare qualitative traits between entire collection (EC) and core set (CS).
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.
- qualitative
Name of columns with the qualitative traits as a character vector.
- selected
Character vector with the names of individuals selected in core collection and present in the
namescolumn.
Details
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.
References
Li Z, Zhang H, Zeng Y, Yang Z, Shen S, Sun C, Wang X (2002). “Studies on sampling schemes for the establishment of core collection of rice landraces in Yunnan, China.” Genetic Resources and Crop Evolution, 49(1), 67–74.
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)))
vpf.evaluate.core(data = ec, names = "genotypes",
qualitative = qual, selected = core)
#> EC_VPF CS_VPF
#> 0.04573206 0.03154146