vpf.evaluate.core: Variance of Phenotypic Frequency

In EvaluateCore: Quality Evaluation of Core Collections

Variance of Phenotypic Frequency

Description

Compute the Variance of Phenotypic Frequency (\mjseqnVPF) \insertCiteli_studies_2002EvaluateCore to compare qualitative traits between entire collection (EC) and core set (CS).

Usage

vpf.evaluate.core(data, names, qualitative, selected, na.omit = TRUE)

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 names column.
na.omit	logical. If TRUE, missing values (NA) are ignored and not included as a distinct factor level for analysis. Default is TRUE.

Details

Variance of Phenotypic Frequency (\mjseqnVPF) \insertCiteli_studies_2002EvaluateCore is computed as follows.

\mjsdeqn

VPF = \frac1n \sum_i=1^n\left ( \frac\sum_j=1^k (p_ij - \overlinep_i)^2k - 1 \right )

Where, \mjseqnp_ij denotes the proportion/fraction/frequency of accessions in the \mjseqnith phenotypic class for the \mjseqnith trait, \mjseqn\overlinep_i is the mean frequency of phenotypic classes for the \mjseqnith trait, \mjseqnk is the number of phenotypic classes for the \mjseqnith trait and \mjseqnn is the total number of traits.

Value

The Variance of Phenotypic Frequency values for EC and CS.

References

\insertAllCited

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)

EvaluateCore documentation built on April 22, 2026, 9:07 a.m.