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)

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.

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 July 3, 2022, 5:06 p.m.