CVA: Canonical variate analysis for multienvironment and... In OnofriAndreaPG/aomisc: Statistical methods for the agricultural sciences

 CVA R Documentation

Canonical variate analysis for multienvironment and multitrait genotype experiments

Description

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.

Usage

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

Arguments

 `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

Details

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).

Value

 `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

References

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

Examples

``````fileName <- "https://www.casaonofri.it/_datasets/WheatQuality4years.csv"
dataset\$Year <- factor(dataset\$Year)

# Standardise the data
groups <- dataset\$Genotype
Z <- apply(dataset[,3:6], 2, scale, center = TRUE, scale = TRUE)