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) .

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 ($$\chi^{2}$$).

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