Compute the Interquartile Range (IQR) (Upton and Cook 1996) to compare quantitative traits of the entire collection (EC) and core set (CS).
iqr.evaluate.core(data, names, quantitative, 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 quantitative traits as a character vector.
Character vector with the names of individuals selected in
core collection and present in the names column.
A data frame with the IQR values of the EC and CS for the traits
specified as quantitative.
Upton G, Cook I (1996). “General summary statistics.” In Understanding statistics. Oxford University Press.
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)))
iqr.evaluate.core(data = ec, names = "genotypes",
quantitative = quant, selected = core)
#> Trait EC_IQR CS_IQR
#> 1 NMSR 10.000000 9.250000
#> 2 TTRN 2.500000 2.666667
#> 3 TFWSR 4.800000 5.550000
#> 4 TTRW 1.500000 2.266667
#> 5 TFWSS 7.400000 8.300000
#> 6 TTSW 2.200000 2.837500
#> 7 TTPW 11.250000 13.650000
#> 8 AVPW 3.470833 5.110000
#> 9 ARSR 3.000000 3.000000
#> 10 SRDM 6.000000 4.625000