Multidomain Functions: Sequenzo and TraMineR Mapping
This page maps Sequenzo multidomain functions to their closest TraMineR workflows for users migrating from R.
What This Section Covers
This section follows the four multidomain strategies discussed by Ritschard et al. (2023):
- IDCD: independence from domain costs and distances
- CAT: cost additive trick
- DAT: distance additive trick
- CombT: combined typology approach
These strategies differ in what is combined: states, costs, distances, or typologies.
Quick Strategy Guide
| Strategy | What is combined? | Main idea | Best used when |
|---|---|---|---|
| IDCD | States first, then costs/distances at MD level | Treat combined states as regular sequence states | You want to avoid additive assumptions and compute distances directly at the multidomain level |
| CAT | Domain costs | Build MD costs by additively combining domain-level costs | You want multidomain costs to reflect additive domain-level differences |
| DAT | Domain distances | Add separate distance matrices | You want a simple linear combination of domain-specific dissimilarities |
| CombT | Domain typologies | Cross-classify separately derived domain clusters | You want the joint typology to remain directly interpretable as combinations of domain-specific clusters |
Mapping by Multidomain Strategy
| Strategy | Sequenzo function(s) | TraMineR counterpart(s) | Notes |
|---|---|---|---|
| CAT | compute_cat_distance_matrix | seqMD(..., what="cost"/"diss"); related multichannel workflows may also use seqdistmc | Direct conceptual match for CAT-style multidomain costs and CAT-based distances. |
| DAT | compute_dat_distance_matrix | No direct one-function equivalent | In TraMineR this is usually done manually: compute one distance matrix per domain with seqdist(), then add or linearly combine the matrices outside seqdist(). |
| IDCD | create_idcd_sequence_from_csvs + MD-level distance step | Closest workflow: build combined-state sequences with seqMD(..., what="MDseq"), then apply seqdist() directly to the resulting MD sequences with costs set at the MD level | Implemented in Sequenzo for constructing combined-state MD sequences from multiple CSV files. Distances are then computed explicitly in the next step (for example, get_distance_matrix). |
| CombT | get_interactive_combined_typology, merge_sparse_combt_types | No direct one-function equivalent | Usually done as a workflow: cluster each domain, cross-classify domain cluster labels, then optionally merge sparse combined types. |
Supporting Functions Around the Four Strategies
| Purpose | Sequenzo function(s) | TraMineR counterpart(s) | Notes |
|---|---|---|---|
| Check state association between domains (especially relevant for CAT) | get_association_between_domains | custom state cross-tabs | A useful diagnostic before choosing a strategy. This diagnoses state co-occurrence, not trajectory association between domain distance matrices. |
| Linked polyads (separate framework) | linked_polyadic_sequence_analysis | No direct one-function equivalent in TraMineR core | Related multidomain context, but not one of the four strategies in Ritschard et al. (2023). |
Beginner Migration Tips
- First choose your strategy (IDCD, CAT, DAT, or CombT), then choose function(s).
- If your R script used
seqMD(..., what="diss")with costs derived from domain-level costs, you are usually closest to CAT in Sequenzo. - For IDCD, think in two steps: build MD sequences first, then compute MD distances.
- Use
get_association_between_domains()to diagnose state co-occurrence. For DAT, trajectory association is more directly assessed by correlating domain-specific distance matrices. - In papers/reports, always name the strategy explicitly (IDCD/CAT/DAT/CombT), because assumptions differ.
Author
Documentation: Yuqi Liang
References
Ritschard, G., Liao, T. F., & Struffolino, E. (2023). Strategies for multidomain sequence analysis in social research. Sociological Methodology, 53(2), 288-322.