Compare Two context trees by Symmetric Divergence with Permutation Test

Computes the count-weighted symmetric Kullback-Leibler divergence between two fitted transitiontrees, then provides a reference distribution by permuting sequence-to-tree assignments.

Use this to ask: do two cohorts (group A vs. group B, baseline vs. intervention) generate significantly different pathway dynamics?

Usage

compare_trees(tree_a, tree_b = NULL, iter = 200L, seed = 1L, symmetric = TRUE)

Arguments

tree_a, tree_b

context trees fit on data subsets A and B. Alternatively, pass a two-element transitiontrees_group (from context_tree(..., group =)) as tree_a and leave tree_b = NULL; its two trees are compared in key order.

iter

Integer. Number of permutations. Default 200.

seed

Integer. RNG seed for reproducibility. Default 1.

symmetric

Logical. Default TRUE.

Value

A transitiontrees_comparison object with components:

pdist

observed scalar distance

null_dist

numeric vector, length iter

p_value

one-sided p-value (proportion of null at least as extreme as observed)

pathways

per-pathway breakdown data.frame

Examples

# \donttest{
set.seed(1)
m1 <- matrix(sample(c("A","B","C"), 200, TRUE), 20)
m2 <- matrix(sample(c("A","B","C"), 200, TRUE), 20)
tr1 <- context_tree(m1, max_depth = 2L, min_count = 3L)
tr2 <- context_tree(m2, max_depth = 2L, min_count = 3L)
compare_trees(tr1, tr2, iter = 50)
#> <transitiontrees_comparison>  iter = 50
#>   observed distance : 0.0575
#>   null mean         : 0.0533
#>   p-value           : 0.314
#> 
#> top divergent pathways:
#>  pathway count_a count_b divergence_ab divergence_ba divergence_sym
#>   B -> B      17      12         0.465         0.372          0.418
#>   C -> A      14       9         0.273         0.293          0.283
#>   A -> A      13      29         0.207         0.250          0.228
#>   C -> C      15      21         0.162         0.199          0.181
#>        A      58      72         0.074         0.080          0.077
#>   A -> B      17      17         0.067         0.066          0.066
# }