Sign Test — signtest.evaluate.core • EvaluateCore

Test difference between means and variances of entire collection (EC) and core set (CS) for quantitative traits by Sign test ( $+$ versus $-$ ) (Basigalup et al. 1995; Tai and Miller 2001) .

Usage

signtest.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

A data frame with the following components.

Comparison: The comparison measure.
ChiSq: The test statistic ( $χ^{2}$ ).
p.value: The p value for the test statistic.
significance: The significance of the test statistic (*: p $\leq$ 0.01; **: p $\leq$ 0.05; ns: p $>$ 0.05).

Details

The test statistic for Sign test ( $χ^{2}$ ) is computed as follows.

$χ^{2} = \frac{(N_{1} - N_{2})^{2}}{N_{1} + N_{2}}$

Where, where $N_{1}$ is the number of variables for which the mean or variance of the CS is greater than the mean or variance of the EC (number of $+$ signs); $N_{2}$ is the number of variables for which the mean or variance of the CS is less than the mean or variance of the EC (number of $-$ signs). The value of $χ^{2}$ is compared with a Chi-square distribution with 1 degree of freedom.

References

Basigalup DH, Barnes DK, Stucker RE (1995). “Development of a core collection for perennial Medicago plant introductions.” Crop Science, 35(4), 1163–1168.

Tai PYP, Miller JD (2001). “A Core Collection for Saccharum spontaneum L. from the World Collection of Sugarcane.” Crop Science, 41(3), 879–885.

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)))

signtest.evaluate.core(data = ec, names = "genotypes",
                       quantitative = quant, selected = core)
#>   Comparison ChiSq    p.value significance
#> 1       Mean   1.6 0.20590321           ns
#> 2   Variance   3.6 0.05777957           ns