Compare the medians of quantitative traits between entire collection (EC) and core set (CS) by Wilcoxon rank sum test or Mann-Whitney-Wilcoxon test or Mann-Whitney U test (Wilcoxon 1945; Mann and Whitney 1947) .
wilcox.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.
The quantitative trait.
The median value of the trait in EC.
The median value of the trait in CS.
The p value of the Wilcoxon test for equality of medians of EC and CS.
The significance of the Wilcoxon test for equality of medians of EC and CS.
Mann HB, Whitney DR (1947).
“On a test of whether one of two random variables is stochastically larger than the other.”
The Annals of Mathematical Statistics, 18(1), 50--60.
Wilcoxon F (1945).
“Individual comparisons by ranking methods.”
Biometrics Bulletin, 1(6), 80.
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)))
wilcox.evaluate.core(data = ec, names = "genotypes",
quantitative = quant, selected = core)
#> Trait EC_Med CS_Med Wilcox_pvalue Wilcox_significance
#> 1 NMSR 10.000000 9.000000 0.07142419 ns
#> 2 TTRN 3.600000 3.500000 0.88120694 ns
#> 3 TFWSR 4.200000 4.300000 0.36146710 ns
#> 4 TTRW 1.445000 1.580000 0.05166795 ns
#> 5 TFWSS 5.400000 5.400000 0.59705438 ns
#> 6 TTSW 1.933333 2.058333 0.06454022 ns
#> 7 TTPW 10.000000 10.400000 0.41477324 ns
#> 8 AVPW 3.400000 3.600000 0.04066290 *
#> 9 ARSR 1.000000 1.000000 0.54494436 ns
#> 10 SRDM 38.500000 38.150000 0.38825006 ns