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

Test difference between means and variances of entire collection (EC) and core set (CS) for quantitative traits by Sign test (\(+\) versus \(-\)) (Basigalup et al. 1995; Tai and Miller 2001).

```
signtest.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 |

The test statistic for Sign test (\(\chi^{2}\)) is computed as follows.

\[\chi^{2} = \frac{(N_{1}-N_{2})^{2}}{N_{1}+N_{2}}\]Where, where \(N_{1}\) is the number of variables for which the mean or variance of the CS is greater than the mean or variance of the EC (number of \(+\) signs); \(N_{2}\) is the number of variables for which the mean or variance of the CS is less than the mean or variance of the EC (number of \(-\) signs). The value of \(\chi^{2}\) is compared with a Chi-square distribution with 1 degree of freedom.

A data frame with the following components.

`Comparison` |
The comparison measure. |

`ChiSq` |
The test statistic (\(\chi^{2}\)). |

`p.value` |
The p value for the test statistic. |

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

Basigalup DH, Barnes DK, Stucker RE (1995).
“Development of a core collection for perennial *Medicago* plant introductions.”
*Crop Science*, **35**(4), 1163–1168.

Tai PYP, Miller JD (2001).
“A Core Collection for *Saccharum spontaneum* L. from the World Collection of Sugarcane.”
*Crop Science*, **41**(3), 879–885.

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

[Package *EvaluateCore* version 0.1.2 Index]