chisquare.evaluate.core: 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 pearson_x._1900,snedecor_chi-square_1933EvaluateCore.

Usage

chisquare.evaluate.core(data, names, qualitative, selected, na.omit = 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 ^2 test statistic.
chisq_pvalue: The p value for the test statistic.
chisq_significance: The significance of the test statistic (*: p 0.01; **: p 0.05; ns: p > 0.05).

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.

References

Examples

Run this code


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)