levene.evaluate.core {EvaluateCore}

Levene's Test

Description

Test for of variances of the entire collection (EC) and core set (CS) for quantitative traits by Levene's test (Levene 1960).

Usage

levene.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 columns

Trait

The quantitative trait.

EC_V

The variance of the EC.

CS_V

The variance of the CS.

EC_CV

The coefficient of variance of the EC.

CS_CV

The coefficient of variance of the CS.

Levene_Fvalue

The test statistic.

Levene_pvalue

The p value for the test statistic.

Levene_significance

The significance of the test statistic (*: p \(\leq\) 0.01; **: p \(\leq\) 0.05; ns: p \( > \) 0.05).

References

Levene H (1960). “Robust tests for equality of variances.” In Olkin I, Ghurye SG, Hoeffding W, Madow WG, Mann HB (eds.), Contribution to Probability and Statistics: Essays in Honor of Harold Hotelling, 278–292. Stanford University Press, Palo Alto, CA.

See Also

leveneTest

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)))

levene.evaluate.core(data = ec, names = "genotypes",
                     quantitative = quant, selected = core)