chisquare.evaluate.core {EvaluateCore} | R Documentation |

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).

```
chisquare.evaluate.core(data, names, qualitative, selected)
```

`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 |

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). |

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.

```
####################################
# 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]