coda.base (version 1.0.3)

pb_basis: Isometric log-ratio basis based on Principal Balances.

Description

Exact method to calculate the principal balances of a compositional dataset. Different methods to approximate the principal balances of a compositional dataset are also included.

Usage

pb_basis(
  X,
  method,
  constrained.criterion = "variance",
  cluster.method = "ward.D2",
  ordering = TRUE,
  ...
)

Value

matrix

Arguments

X

compositional dataset

method

method to be used with Principal Balances. Methods available are: 'exact', 'constrained' or 'cluster'.

constrained.criterion

Criterion used to compare the partition and the principal balance. Either `variance` (default) or `angle`.

cluster.method

Method to be used with the hclust function (default: `ward.D2`) or any other method available in hclust function

ordering

should the principal balances found be returned ordered? (first column, first principal balance and so on)

...

parameters passed to hclust function

References

Martín-Fernández, J.A., Pawlowsky-Glahn, V., Egozcue, J.J., Tolosana-Delgado R. (2018). Advances in Principal Balances for Compositional Data. Mathematical Geosciencies, 50, 273-298.

Examples

set.seed(1)
X = matrix(exp(rnorm(5*100)), nrow=100, ncol=5)

# Optimal variance obtained with Principal components
(v1 <- apply(coordinates(X, 'pc'), 2, var))
# Optimal variance obtained with Principal balances
(v2 <- apply(coordinates(X,pb_basis(X, method='exact')), 2, var))
# Solution obtained using constrained method
(v3 <- apply(coordinates(X,pb_basis(X, method='constrained')), 2, var))
# Solution obtained using Ward method
(v4 <- apply(coordinates(X,pb_basis(X, method='cluster')), 2, var))

# Plotting the variances
barplot(rbind(v1,v2,v3,v4), beside = TRUE, ylim = c(0,2),
        legend = c('Principal Components','PB (Exact method)',
                   'PB (Constrained)','PB (Ward approximation)'),
        names = paste0('Comp.', 1:4), args.legend = list(cex = 0.8), ylab = 'Variance')