plot_ftree: Plot a hierarchical tree
In functClust: Functional Clustering of Redundant Components of a System

Description Usage Arguments Details Value

Take a tree and plot its representation, trunk at bottom and leaves at top.

1 2	plot_ftree(tree, signifElt = rep(TRUE, length(tree$cor)), cols = "black", main = "", opt.sort = TRUE)

`tree`	a list of a matrix of component affectation and a vector of coefficients of determination.
`signifElt`	a vector of logical. `TRUE` means that deleting the component changes the component clustering. `FALSE` means that deleting the component does not change the clustering.
`cols`	a vector of colours, sorted as the components of tree.
`main`	a string, that appears as main title of the plot.
`opt.sort`	a logical. `opt.sort` = TRUE by default. If `opt.sort` = FALSE, the tree is not re-organized.

A hierarchical tree is recorded as a list of matrix of component affectation and a vector of coefficients of determination. The matrix of component affectation is a square matrix. Its dimension is "number of leaves" = "number of components". The clusters of components are labelled by integers, an integer by cluster. The first line of the square matrix corresponds to the trivial cluster. The trivial cluster get together all the components: all components are thus labelled by one. The second line separates the trivial cluster into 2 sub-clusters: the components are labelled 1 or 2 according to the cluster to which they belong. The following lines separate the trivial cluster into n sub-clusters, labelled from 1 to the line numero. The last line separate each component in a singleton cluster.

The vector of coefficients of determination determines the level of each split of a cluster into two new clusters.

Nothing. It is a procedure that plots a hierarchical tree.

functClust documentation built on Dec. 2, 2020, 5:06 p.m.