ttest.evaluate.core: Student's t Test

In EvaluateCore: Quality Evaluation of Core Collections

Student's t Test

Description

Test difference between means of entire collection (EC) and core set (CS) for quantitative traits by Student's t test (in particular it's adaptation for comparison of groups with unequal variances known as Welch's two sample t-test) \insertCitestudent_probable_1908,welch_generalization_1947EvaluateCore.

Usage

ttest.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

Trait	The quantitative trait.
Count	The accession count (excluding missing data).
Df	The degrees of freedom for the test.
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.
ttest_pvalue	The p value of the Student's t test for equality of means of EC and CS.
ttest_significance	The significance of the Student's t test for equality of means of EC and CS.

References

\insertAllCited

See Also

t.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)))

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

EvaluateCore documentation built on April 22, 2026, 9:07 a.m.