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Wilcoxon Rank Sum Test

Source: R/wilcox.evaluate.core.R
wilcox.evaluate.core.Rd

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) .

Usage

wilcox.evaluate.core(data, names, quantitative, selected)

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.

quantitative

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

selected

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

Value

Trait

The quantitative trait.

EC_Med

The median value of the trait in EC.

CS_Med

The median value of the trait in CS.

Wilcox_pvalue

The p value of the Wilcoxon test for equality of medians of EC and CS.

Wilcox_significance

The significance of the Wilcoxon test for equality of medians of EC and CS.

References

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.

See also

wilcox.test

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)))

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