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) .
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
namescolumn.
Value
A data frame with the following components.
- Comparison
The comparison measure.
- ChiSq
The test statistic (\(\chi^{2}\)).
- 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 (\(\chi^{2}\)) is computed as follows.
\[\chi^{2} = \frac{(N_{1}-N_{2})^{2}}{N_{1}+N_{2}}\]
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 \(\chi^{2}\) 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