CVA | R Documentation |

This function performs canonical variate analysis as a descriptive visualisation tool. It is close to the 'lda()' function in the MASS package but it is not meant to be used for discriminant analyses.

```
CVA(dataset, groups, scale = TRUE, constraint = 3)
```

`dataset` |
dataset is a multidimensional matrix of observations |

`groups` |
groups is a vector coding for groupings |

`scale` |
whether the data needs to be standardised prior to analysis. Defaults to TRUE |

`constraint` |
It is the type of scaling for eigenvectors, so that canonical variates have: 1 = unit within-group standard deviations (most common); 2 = unit total standard deviations; 3 = unit within group norms; 4 = unit total norms. It defaults to 3 |

More detail can be found in a blog page, at 'https://www.statforbiology.com/2023/stat_multivar_cva/'. Please, note that preliminary data transformations (e.g.: standardisation) are left to the user and must be performed prior to analyses (see example below).

`TOT` |
matrix of total variances-covariances |

`B` |
matrix of 'between-groups' variances-covariances |

`W` |
matrix of 'within-group' variances-covariances |

`B/W` |
matrix of W^-1 B |

`eigenvalues` |
vector of eigenvalues |

`eigenvectors` |
matrix of eigenvectors |

`proportion` |
a vector containing the proportion of total discriminating ability captured by each canonical variate |

`correlation` |
vector of canonical correlations |

`squared.canonical.correlation` |
vector of squared canonical correlations |

`coefficients` |
matrix of canonical coefficients |

`scores` |
matrix of canonical scores |

`centroids` |
matrix of scores for centroids |

`total.structure` |
matrix of total canonical structure |

`between.structure` |
matrix of between-groups canonical structure |

`within.structure` |
matrix of within-groups canonical structure |

`class.fun` |
matrix of classifications functions |

`class.val` |
matrix of classification values |

`within.structure` |
matrix of within-groups canonical structure |

`class` |
vector of predicted classes |

Andrea Onofri

https://www.statforbiology.com/2023/stat_multivar_cva/

```
fileName <- "https://www.casaonofri.it/_datasets/WheatQuality4years.csv"
dataset <- read.csv(fileName)
dataset$Year <- factor(dataset$Year)
head(dataset)
# Standardise the data
groups <- dataset$Genotype
Z <- apply(dataset[,3:6], 2, scale, center = TRUE, scale = TRUE)
head(Z)
# Performs CVA
cvaobj <- CVA(Z, groups)
cvaobj
```

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.