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

Compare the medians of quantitative traits between entire collection (EC) and core set (CS) by Wilcoxon rank sum test or Mann-Whitney-Wilcoxon test or Mann-Whitney U test (Wilcoxon 1945; Mann and Whitney 1947).

```
wilcox.evaluate.core(data, names, quantitative, 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 |

`quantitative` |
Name of columns with the quantitative traits as a character vector. |

`selected` |
Character vector with the names of individuals selected in
core collection and present in the |

`Trait` |
The quantitative trait. |

`EC_Med` |
The median value of the trait in EC. |

`CS_Med` |
The median value of the trait in CS. |

`Wilcox_pvalue` |
The p value of the Wilcoxon test for equality of medians of EC and CS. |

`Wilcox_significance` |
The significance of the Wilcoxon test for equality of medians of EC and CS. |

Mann HB, Whitney DR (1947).
“On a test of whether one of two random variables is stochastically larger than the other.”
*The Annals of Mathematical Statistics*, **18**(1), 50–60.

Wilcoxon F (1945).
“Individual comparisons by ranking methods.”
*Biometrics Bulletin*, **1**(6), 80.

```
####################################
# 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
####################################
wilcox.evaluate.core(data = ec, names = "genotypes",
quantitative = quant, selected = core)
```

[Package *EvaluateCore* version 0.1.2 Index]