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

Compute Principal Component Analysis Statistics (Mardia et al. 1979) to compare the probability distributions of quantitative traits between entire collection (EC) and core set (CS).

```
pca.evaluate.core(
data,
names,
quantitative,
selected,
center = TRUE,
scale = TRUE,
npc.plot = 6
)
```

`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 |

`center` |
either a logical value or numeric-alike vector of length
equal to the number of columns of |

`scale` |
either a logical value or a numeric-alike vector of length
equal to the number of columns of |

`npc.plot` |
The number of principal components for which eigen values are to be plotted. The default value is 6. |

A list with the following components.

`EC PC Importance` |
A data frame of importance of principal components for EC |

`EC PC Loadings` |
A data frame with eigen vectors of principal components for EC |

```
CS PC
Importance
``` |
A data frame of importance of principal components for CS |

`CS PC Loadings` |
A data frame with eigen vectors of principal components for CS |

`Scree Plot` |
The scree plot of principal components
for EC and CS as a |

`PC Loadings Plot` |
A plot of
the eigen vector values of principal components for EC and CS as specified
by |

Mardia KV, Kent JT, Bibby JM (1979).
*Multivariate analysis*.
Academic Press, London; New York.
ISBN 0-12-471250-9 978-0-12-471250-8 0-12-471252-5 978-0-12-471252-2.

```
####################################
# 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
####################################
pca.evaluate.core(data = ec, names = "genotypes",
quantitative = quant, selected = core,
center = TRUE, scale = TRUE, npc.plot = 4)
```

[Package *EvaluateCore* version 0.1.2 Index]