`R/dcsbm.R`

`dcsbm_path-class.Rd`

An S4 class to represent a hierarchical fit of a degree corrected stochastic block model, extend `icl_path-class`

.

`model`

a

`dcsbm-class`

object to store the model fitted`name`

generative model name

`icl`

icl value of the fitted model

`K`

number of extracted clusters over row and columns

`cl`

a numeric vector with row and columns cluster indexes

`obs_stats`

a list with the following elements:

counts: numeric vector of size K with number of elements in each clusters

din: numeric vector of size K which store the sums of in-degrees for each clusters

dout: numeric vector of size K which store the sums of out-degrees for each clusters

x_counts: matrix of size K*K with the number of links between each pair of clusters

`path`

a list of size K-1 with each part of the path described by:

icl1: icl value reach with this solution for alpha=1

logalpha: log(alpha) value were this solution is better than its parent

K: number of clusters

cl: vector of cluster indexes

k,l: index of the cluster that were merged at this step

merge_mat: lower triangular matrix of delta icl values

obs_stats: a list with the elements:

counts: numeric vector of size K with number of elements in each clusters

din: numeric vector of size K which store the sums of in-degrees for each clusters

dout: numeric vector of size K which store the sums of out-degrees for each clusters

x_counts: matrix of size K*K with the number of links between each pair of clusters

`logalpha`

value of log(alpha)

`ggtree`

data.frame with complete merge tree for easy plotting with

`ggplot2`

`tree`

numeric vector with merge tree

`tree[i]`

contains the index of`i`

father`train_hist`

data.frame with training history information (details depends on the training procedure)