ttest.evaluate.core {EvaluateCore}

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 (Student 1908).

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.

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

Student (1908). “The probable error of a mean.” Biometrika, 6(1), 1–25.

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)