snk.evaluate.core: Student-Newman-Keuls Test

In EvaluateCore: Quality Evaluation of Core Collections

Student-Newman-Keuls Test

Description

Test difference between means of entire collection (EC) and core set (CS) for quantitative traits by Newman-Keuls or Student-Newman-Keuls test \insertCitenewman_distribution_1939,keuls_use_1952EvaluateCore.

Usage

snk.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.

Trait	The quantitative trait.
EC_Min	The minimum value of the trait in EC.
EC_Max	The maximum value of the trait in EC.
EC_Mean	The mean value of the trait in EC.
EC_SE	The standard error of the trait in EC.
CS_Min	The minimum value of the trait in CS.
CS_Max	The maximum value of the trait in CS.
CS_Mean	The mean value of the trait in CS.
CS_SE	The standard error of the trait in CS.
SNK_pvalue	The p value of the Student-Newman-Keuls test for equality of means of EC and CS.
SNK_significance	The significance of the Student-Newman-Keuls test for equality of means of EC and CS.

References

\insertAllCited

See Also

SNK.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)))

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

EvaluateCore documentation built on July 3, 2022, 5:06 p.m.