chisquare.evaluate.core: Chi-squared Test for Homogeneity

In EvaluateCore: Quality Evaluation of Core Collections

Chi-squared Test for Homogeneity

Description

Compare the distribution frequencies of qualitative traits between entire collection (EC) and core set (CS) by Chi-squared test for homogeneity \insertCitepearson_x._1900,snedecor_chi-square_1933EvaluateCore. \loadmathjax

Usage

chisquare.evaluate.core(data, names, qualitative, selected, na.omit = TRUE)

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.
qualitative	Name of columns with the qualitative traits as a character vector.
selected	Character vector with the names of individuals selected in core collection and present in the names column.
na.omit	logical. If TRUE, missing values (NA) are ignored and not included as a distinct factor level for analysis. Default is TRUE.

Value

A a data frame with the following columns.

Trait	The qualitative trait.
EC_No.Classes	The number of classes in the trait for EC.
EC_Classes	The frequency of the classes in the trait for EC.
CS_No.Classes	The number of classes in the trait for CS.
CS_Classes	The frequency of the classes in the trait for CS.
chisq_statistic	The \mjseqn\chi^2 test statistic.
chisq_pvalue	The p value for the test statistic.
chisq_significance	The significance of the test statistic (*: p \mjseqn\leq 0.01; **: p \mjseqn\leq 0.05; ns: p \mjseqn > 0.05).

References

\insertAllCited

See Also

chisq.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)))

chisquare.evaluate.core(data = ec, names = "genotypes",
                        qualitative = qual, selected = core)

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