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.

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)))

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]