compute_event_transition_matrix()

compute_event_transition_matrix() summarizes how often one event is immediately followed by another in event order.

Function Usage

compute_event_transition_matrix(
    eseq,
    weighted=True,
    normalize=True,
    use_weights=None
)

TraMineR Parameter Mapping

• event_sequences -> TraMineR eseq object
• weighted -> TraMineR::seqetm() weighted

Entry Parameters

Parameter	Required	Type	Description
event_sequences (event_sequences)	✓	EventSequenceData / EventSequenceList	The full event-sequence dataset to summarize.
weighted	✗	bool	Use sequence weights if available.
normalize	✗	bool	If True, convert counts to row-wise probabilities.
use_weights	✗	bool / None	Backward-compatible alias for weighted. If provided, it overrides weighted.

Returns

A square DataFrame where:

• Rows = source event
• Columns = next event in order
• Values = adjacent event-order count or row-wise probability

Example

tm = compute_event_transition_matrix(eseq, normalize=True)
print(tm)

R Counterpart

• Closest R function: TraMineR::seqetm()
• Mapping note: This is a practical Sequenzo helper for summarizing adjacent event-to-event movements. It is related to transition-focused event-sequence workflows, but it should not be confused with the formal event-sequence definition of a transition, where a transition may contain several simultaneous events.

Notes

• When normalize=True, each non-empty row sums to 1.
• Use normalize=False to get raw weighted counts.

See Also

• Event Sequences Introduction explains the workflow and data structures.
• Quickstart Example shows a complete event-mining run.
• Helper Functions lists supporting utilities.

Authors

Code: Yuqi Liang

Documentation: Yuqi Liang

References

Ritschard, G., Bürgin, R., & Studer, M. (2013). Exploratory Mining of Life Event Histories. In J. J. McArdle & G. Ritschard (Eds.), Contemporary Issues in Exploratory Data Mining in the Behavioral Sciences (pp. 221-253). Routledge.