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

Compute the following differences between the entire collection (EC) and core set (CS).

Percentage of significant differences of mean (\(MD\%_{Hu}\)) (Hu et al. 2000)

Percentage of significant differences of variance (\(VD\%_{Hu}\)) (Hu et al. 2000)

Average of absolute differences between means (\(MD\%_{Kim}\)) (Kim et al. 2007)

Average of absolute differences between variances (\(VD\%_{Kim}\)) (Kim et al. 2007)

Percentage difference between the mean squared Euclidean distance among accessions (\(\overline{d}D\%\)) (Studnicki et al. 2013)

```
percentdiff.evaluate.core(data, names, quantitative, selected, alpha = 0.05)
```

`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 |

`alpha` |
Type I error probability (Significance level) of difference. |

The differences are computed as follows.

\[MD\%_{Hu} = \left ( \frac{S_{t}}{n} \right ) \times 100\]Where, \(V_{EC_{i}}\) is the variance of the EC for the \(i\)th trait, \(V_{CS_{i}}\) is the variance of the CS for the \(i\)th trait and \(n\) is the total number of traits.

\[\overline{d}D\% = \frac{\overline{d}_{CS}-\overline{d}_{EC}}{\overline{d}_{EC}} \times 100\]Where, \(\overline{d}_{CS}\) is the mean squared Euclidean distance among accessions in the CS and \(\overline{d}_{EC}\) is the mean squared Euclidean distance among accessions in the EC.

A data frame with the values of \(MD\%_{Hu}\), \(VD\%_{Hu}\), \(MD\%_{Kim}\), \(VD\%_{Kim}\) and \(\overline{d}D\%\).

Hu J, Zhu J, Xu HM (2000).
“Methods of constructing core collections by stepwise clustering with three sampling strategies based on the genotypic values of crops.”
*Theoretical and Applied Genetics*, **101**(1), 264–268.

Kim K, Chung H, Cho G, Ma K, Chandrabalan D, Gwag J, Kim T, Cho E, Park Y (2007).
“PowerCore: A program applying the advanced M strategy with a heuristic search for establishing core sets.”
*Bioinformatics*, **23**(16), 2155–2162.

Studnicki M, Madry W, Schmidt J (2013).
“Comparing the efficiency of sampling strategies to establish a representative in the phenotypic-based genetic diversity core collection of orchardgrass (*Dactylis glomerata* L.).”
*Czech Journal of Genetics and Plant Breeding*, **49**(1), 36–47.

`snk.evaluate.core`

,
`snk.evaluate.core`

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

[Package *EvaluateCore* version 0.1.2 Index]