arbre.Mp {SARP.compo} | R Documentation |
Grouping composants by changes in cut-off separation
Description
These functions construct a tree based on the successive disjunctions between nodes of the graph when increasing the cut-off value.
Usage
arbre.Mp( Mp, en.log = FALSE, reference = NA, complement = FALSE )
## S3 method for class 'Arbre'
plot(x, seuil.p = 0.05,
xlab = "Composant",
ylab = if ( TRUE == en.log ) "-log seuil" else "Seuil",
col.seuil = "red" , lwd.seuil = 1, lty.seuil = 1,
horiz = FALSE, center = TRUE, edge.root = TRUE,
...)
Arguments
Mp |
A square, symmetric matrix containing p-values. Element
in row |
en.log |
If |
reference |
Either |
complement |
A logical. If TRUE, the tree is built using the complement of the graph, as when using equivalence test to build the graph. |
x |
The tree to be drawn |
seuil.p |
Selected cut-off for analysis. Can also be a |
xlab , ylab |
Legends for the axes |
col.seuil , lwd.seuil , lty.seuil |
Graphical parameters for drawing the analysis cut-off |
horiz , center , edge.root |
Options from
|
... |
Additionnal parameters for |
Details
By increasing the cut-off from 0 to 1, more and more edges between nodes are removed, and disjoint subgraphs appear. This can be used to build a tree of the composants, with nodes of the tree corresponding to the apparition of a new distinct subgraph. Leafs of the tree are the individual components.
Value
The arbre.Mp
function returns a dendrogram.
Author(s)
Emmanuel Curis (emmanuel.curis@parisdescartes.fr)
See Also
creer.Mp
to create a matrix of p-values for all
possible ratios of a compositional vector.
grf.Mp
to convert such a matrix to a graph, once a
cut-off is selected.
coupures.Mp
to obtain the set of p-values
corresponding to the nodes of the tree, that is to the apparition of
new sets of composants.
plot.dendrogram
and
as.dendrogram
for more details on dendrogram
drawing and structure.
Examples
# load the potery data set
data( poteries )
# Compute one-way ANOVA p-values for all ratios in this data set
Mp <- creer.Mp( poteries, c( 'Al', 'Na', 'Fe', 'Ca', 'Mg' ),
f.p = anva1.fpc, v.X = 'Site' )
# Build the tree (in log scale, p-values are all < 0.01)
arbre <- arbre.Mp( Mp, en.log = TRUE )
# It is a dendrogram as defined in the cluster package
str( arbre )
class( arbre )
# Drawing this tree
plot( arbre )