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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.

Count

The accession count (excluding missing data).

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 Count    EC_Med    CS_Med Wilcox_pvalue Wilcox_significance
#> 1   NMSR  1684 10.000000  9.000000    0.07142419                  ns
#> 2   TTRN  1684  3.600000  3.500000    0.88120694                  ns
#> 3  TFWSR  1684  4.200000  4.300000    0.36146710                  ns
#> 4   TTRW  1684  1.445000  1.580000    0.05166795                  ns
#> 5  TFWSS  1684  5.400000  5.400000    0.59705438                  ns
#> 6   TTSW  1684  1.933333  2.058333    0.06454022                  ns
#> 7   TTPW  1684 10.000000 10.400000    0.41477324                  ns
#> 8   AVPW  1684  3.400000  3.600000    0.04066290                   *
#> 9   ARSR  1684  1.000000  1.000000    0.54494436                  ns
#> 10  SRDM  1684 38.500000 38.150000    0.38825006                  ns