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

Test difference between means of entire collection (EC) and core set (CS) for quantitative traits by Newman-Keuls or Student-Newman-Keuls test (Newman 1939; Keuls 1952).

```
snk.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 |

A data frame with the following components.

`Trait` |
The quantitative trait. |

`EC_Min` |
The minimum value of the trait in EC. |

`EC_Max` |
The maximum value of the trait in EC. |

`EC_Mean` |
The mean value of the trait in EC. |

`EC_SE` |
The standard error of the trait in EC. |

`CS_Min` |
The minimum value of the trait in CS. |

`CS_Max` |
The maximum value of the trait in CS. |

`CS_Mean` |
The mean value of the trait in CS. |

`CS_SE` |
The standard error of the trait in CS. |

`SNK_pvalue` |
The p value of the Student-Newman-Keuls test for equality of means of EC and CS. |

`SNK_significance` |
The significance of the Student-Newman-Keuls test for equality of means of EC and CS. |

Keuls M (1952).
“The use of the ,,studentized range" in connection with an analysis of variance.”
*Euphytica*, **1**(2), 112–122.

Newman D (1939).
“The distribution of range in samples from a normal population, expressed in terms of an independent estimate of standard deviation.”
*Biometrika*, **31**(1-2), 20–30.

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

[Package *EvaluateCore* version 0.1.2 Index]