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)

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 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).

References

Examples

Run this code

# NOT RUN {
####################################
# Use data from R package ccChooser
####################################

library(ccChooser)
data("dactylis_CC")
data("dactylis_EC")

ec <- cbind(genotypes = rownames(dactylis_EC), dactylis_EC[, -1])
ec$genotypes <- as.character(ec$genotypes)
rownames(ec) <- NULL
ec[, c("X1", "X6", "X7")] <- lapply(ec[, c("X1", "X6", "X7")],
                                    function(x) cut(x, breaks = 4))
ec[, c("X1", "X6", "X7")] <- lapply(ec[, c("X1", "X6", "X7")],
                                    function(x) factor(as.numeric(x)))
head(ec)

core <- rownames(dactylis_CC)

quant <- c("X2", "X3", "X4", "X5", "X8")
qual <- c("X1", "X6", "X7")

####################################
# EvaluateCore
####################################

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


# }