Convert cluster_summary to tna Objects

Converts a cluster_summary or mcml object to tna models that can be used with the functions of the tna package. The result holds a macro (cluster-level) model and one model of the internal transitions of each cluster, as a flat group_tna object.

Usage

as_tna(x)

# S3 method for class 'cluster_summary'
as_tna(x)

# S3 method for class 'mcml'
as_tna(x)

# Default S3 method
as_tna(x)

Arguments

x: A cluster_summary object created by csum, or an mcml object created by summarize_clusters. A tna object is returned unchanged, and any other input is an error. The weights are passed to tna::tna(), which row-normalizes them, so a summary computed with type = "raw" gives the same transition probabilities as one computed with type = "tna".

Value

A group_tna object, a flat named list of tna objects. The first element is named "macro" and holds the cluster-level transitions. The remaining elements are named by cluster and hold the internal transitions of each cluster.

macro: A tna object of cluster-level transitions, with weights (k x k transition matrix), inits (initial distribution) and labels (cluster names).
<cluster_name>: One tna object per cluster, with weights (n_i x n_i matrix), inits (initial distribution) and labels (node labels). A cluster that cannot become a tna model is left out with a warning (see Excluded Clusters).

A group_tna object (flat list of tna objects: macro + per-cluster).

A tna object constructed from the input.

Details

Requirements

The tna package must be installed. Without it, the function raises an error.

Excluded Clusters

A per-cluster tna cannot be created when:

The cluster has only 1 node (no internal transitions possible)
Some nodes in the cluster have no outgoing edges (row sums to 0)

These clusters are left out of the result with a warning of class cograph_cluster_dropped, which names each cluster and the nodes that have no transition within it. The macro (cluster-level) model still includes all clusters.

Examples

clusters <- list(C1 = c("Explore", "Reflect", "Discuss"),
                 C2 = c("Plan", "Create", "Share"),
                 C3 = c("Monitor", "Adapt", "Synthesize", "Evaluate"))
as_tna(csum(regulation_net, clusters = clusters, type = "tna"))
#> macro :
#> State Labels : 
#> 
#>    C1, C2, C3 
#> 
#> Transition Probability Matrix :
#> 
#>           C1        C2        C3
#> C1 0.6521739 0.2546584 0.0931677
#> C2 0.1500000 0.2777778 0.5722222
#> C3 0.4036364 0.1745455 0.4218182
#> 
#> Initial Probabilities : 
#> 
#>        C1        C2        C3 
#> 0.3391960 0.2374372 0.4233668 
#> 
#> C1 :
#> State Labels : 
#> 
#>    Explore, Reflect, Discuss 
#> 
#> Transition Probability Matrix :
#> 
#>           Explore   Reflect Discuss
#> Explore 0.0000000 1.0000000       0
#> Reflect 1.0000000 0.0000000       0
#> Discuss 0.4615385 0.5384615       0
#> 
#> Initial Probabilities : 
#> 
#>   Explore   Reflect   Discuss 
#> 0.3333333 0.6666667 0.0000000 
#> 
#> C2 :
#> State Labels : 
#> 
#>    Plan, Create, Share 
#> 
#> Transition Probability Matrix :
#> 
#>        Plan    Create     Share
#> Plan      0 0.3571429 0.6428571
#> Create    0 0.0000000 1.0000000
#> Share     1 0.0000000 0.0000000
#> 
#> Initial Probabilities : 
#> 
#>   Plan Create  Share 
#>   0.21   0.20   0.59 
#> 
#> C3 :
#> State Labels : 
#> 
#>    Monitor, Adapt, Synthesize, Evaluate 
#> 
#> Transition Probability Matrix :
#> 
#>              Monitor     Adapt Synthesize Evaluate
#> Monitor    0.0000000 1.0000000          0        0
#> Adapt      0.0000000 0.0000000          1        0
#> Synthesize 1.0000000 0.0000000          0        0
#> Evaluate   0.4342105 0.5657895          0        0
#> 
#> Initial Probabilities : 
#> 
#>    Monitor      Adapt Synthesize   Evaluate 
#>  0.3448276  0.5086207  0.1465517  0.0000000 
#>