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paths <- list(
s1 = c("A", "B", "C", "D"),
s2 = c("A", "B", "C", "C"),
s3 = c("A", "C", "C", "D"),
s4 = c("D", "C", "B", "A"),
s5 = c("D", "C", "A", "A"),
s6 = c("D", "B", "B", "A")
)
sequence_data <- do.call(rbind, lapply(seq_along(paths), function(i) {
data.frame(sequence_id = names(paths)[i],
sequence_order = seq_along(paths[[i]]),
state = paths[[i]], stringsAsFactors = FALSE)
}))levenshtein <- compute_sequence_distance(sequence_data, method = "levenshtein")
lcs <- compute_sequence_distance(sequence_data, method = "lcs")
om <- compute_sequence_distance(
sequence_data,
method = "optimal_matching",
indel_cost = 1,
substitution_cost = 2,
normalise = "max_length"
)
transition <- compute_sequence_distance(sequence_data, method = "transition")
summarise_sequence_distance(lcs)$overall
#> n_sequences n_pairs mean_distance median_distance min_distance max_distance
#> 1 6 15 4.533333 6 2 6The distance method and all costs are retained as object attributes. The transition method compares first-order transition-probability profiles; it is not a general stochastic-process model.
fit <- cluster_sequences(lcs, k = 2L, method = "hierarchical", linkage = "average")
fit$assignments
#> s1 s2 s3 s4 s5 s6
#> 1 1 1 2 2 2
validate_sequence_clusters(fit)$overall
#> n_sequences n_clusters average_silhouette minimum_silhouette dunn_index
#> 1 6 2 0.6111111 0.5 1.5
#> within_between_ratio singleton_clusters
#> 1 0.3888889 0
extract_representative_sequences(fit)
#> cluster rank sequence_id mean_within_distance
#> 1 1 1 s1 2
#> 2 2 1 s4 2stability <- bootstrap_sequence_clusters(
lcs,
k = 2L,
n_boot = 20L,
sample_fraction = 0.8,
seed = 100L
)
summarise_sequence_cluster_stability(stability)$overall
#> n_boot sample_fraction mean_pairwise_stability min_pairwise_stability
#> 1 20 0.8 1 1Cluster stability describes reproducibility under the selected resampling and clustering settings. It does not establish that the clusters are natural, causal, or substantively meaningful.
transition_fit <- cluster_sequences(
transition,
k = 2L,
method = "hierarchical",
linkage = "average"
)
ensemble <- create_sequence_cluster_ensemble(
fit,
transition_fit,
k = 2L
)
ensemble$assignments
#> s1 s2 s3 s4 s5 s6
#> 1 1 1 2 2 1
ensemble$coassociation
#> s1 s2 s3 s4 s5 s6
#> s1 1.0 1.0 1.0 0.0 0.0 0.5
#> s2 1.0 1.0 1.0 0.0 0.0 0.5
#> s3 1.0 1.0 1.0 0.0 0.0 0.5
#> s4 0.0 0.0 0.0 1.0 1.0 0.5
#> s5 0.0 0.0 0.0 1.0 1.0 0.5
#> s6 0.5 0.5 0.5 0.5 0.5 1.0The ensemble records how often pairs are assigned together across supplied solutions. It does not automatically validate the number or meaning of clusters.
if (requireNamespace("cluster", quietly = TRUE)) {
pam_fit <- cluster_sequences(lcs, k = 2L, method = "pam", seed = 11L)
clara_fit <- cluster_sequences(
lcs,
k = 2L,
method = "clara",
seed = 11L,
samples = 5L,
sampsize = 5L
)
list(pam = pam_fit$assignments, clara = clara_fit$assignments)
}
#> $pam
#> s1 s2 s3 s4 s5 s6
#> 1 1 1 2 2 2
#>
#> $clara
#> s1 s2 s3 s4 s5 s6
#> 1 1 1 2 2 2PAM uses the supplied dissimilarities directly. CLARA uses a
documented classical multidimensional-scaling embedding because
cluster::clara() expects observations rather than a
dissimilarity object.
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They may not be fully stable and should be used with caution. We make no claims about them.
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