Returns a data frame of all transitions at a given Markov order, sorted by count (descending). Each row shows the full path as a readable sequence of states, along with the observed count and transition probability.
Arguments
- x
A
net_mogenobject frombuild_mogen().- order
Integer. Which order's transitions to extract. Defaults to the optimal order selected by the model.
- min_count
Integer. Minimum observed count to include (default 1). Use this to filter out rare transitions that have unreliable probabilities.
Value
A data frame with columns:
- path
The full state sequence (e.g., "AI -> FAIL -> SOLVE").
- count
Number of times this transition was observed.
- probability
Transition probability P(to | from).
- from
The context / conditioning states (k-gram source node).
- to
The predicted next state.
Details
At order k, each edge in the De Bruijn graph represents a (k+1)-step path.
For example, at order 2, the edge from node "AI -> FAIL" to node
"FAIL -> SOLVE" represents the three-step path AI -> FAIL -> SOLVE.
The path column reconstructs this full sequence for readability.
Examples
# \donttest{
trajs <- list(c("A","B","C","D"), c("A","B","D","C"),
c("B","C","D","A"), c("C","D","A","B"))
m <- build_mogen(trajs, max_order = 3)
mogen_transitions(m, order = 1)
#> path count probability from to
#> 1 A -> B 3 1.0000 A B
#> 2 C -> D 3 1.0000 C D
#> 3 D -> A 2 0.6667 D A
#> 4 B -> C 2 0.6667 B C
#> 5 D -> C 1 0.3333 D C
#> 6 B -> D 1 0.3333 B D
# }