Step 3: Turn Sequences into Trajectories — step3

Clusters aligned state sequences with Nestimate sequence distances and clustering methods. The default Hamming plus PAM combination follows the Nestimate and Carm sequence-clustering interface. Set dissimilarity = "lcs" and method = "ward.D2" to follow the worked VaSSTra chapter.

Usage

step3_trajectories(
  data,
  n_trajectories = "auto",
  dissimilarity = c("hamming", "osa", "lv", "dl", "lcs", "qgram", "cosine", "jaccard",
    "jw"),
  method = c("pam", "ward.D2", "ward.D", "complete", "average", "single", "mcquitty",
    "median", "centroid"),
  backend = c("Nestimate", "base"),
  labels = NULL,
  seed = 123L
)

Arguments

data: A vasstra_sequences object.
n_trajectories: Number of trajectory groups. One number fits exactly that count, several numbers (for example 2:4) compare those candidates with trajectory_choices() and fit the recommended count, and "auto" (default) compares 2 through 6 — or simply matches labels when labels are supplied. Automatic comparison never selects a solution whose smallest group holds under 5 percent of the sequences. Compared candidates are kept in diagnostics$selection and every automated choice is reported with a message.
dissimilarity: One of "hamming", "osa", "lv", "dl", "lcs", "qgram", "cosine", "jaccard", or "jw".
method: One of "pam", "ward.D2", "ward.D", "complete", "average", "single", "mcquitty", "median", or "centroid".
backend: "Nestimate" (default) uses Nestimate::build_clusters(). Use "base" for the small internal equivalence backend, which supports only Hamming/LCS distances.
labels: Optional unique trajectory labels.
seed: Reproducible random seed passed to the clustering backend.

Value

A vasstra_trajectories object with tidy membership, cluster sizes, silhouette, and the complete distance matrix.

Examples

sequences <- step2_sequences(
  data.frame(
    id = rep(1:6, each = 3),
    time = rep(1:3, 6),
    state = c(
      "A", "A", "A", "A", "A", "B",
      "B", "B", "B", "B", "B", "A",
      "C", "C", "C", "C", "C", "B"
    )
  ),
  id = "id",
  time = "time",
  state = "state"
)
trajectories <- step3_trajectories(sequences, n_trajectories = 3)
trajectories
#> VaSSTra Step 3: Sequences -> Trajectories
#>   6 sequences | 3 trajectories | hamming + pam | silhouette 0.611
#>   Sizes: Trajectory 1=2, Trajectory 2=2, Trajectory 3=2