chisquare.evaluate.core {EvaluateCore}R Documentation

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 1900; Snedecor and Irwin 1933).

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 \(\chi^{2}\) test statistic.

chisq_pvalue

The p value for the test statistic.

chisq_significance

The significance of the test statistic (*: p \(\leq\) 0.01; **: p \(\leq\) 0.05; ns: p \( > \) 0.05).

References

Pearson K (1900). “X. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling.” The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 50(302), 157–175.

Snedecor G, Irwin MR (1933). “On the chi-square test for homogeneity.” Iowa State College Journal of Science, 8, 75–81.

See Also

chisq.test

Examples


####################################
# 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)



[Package EvaluateCore version 0.1.2 Index]