Tables of a multi-order generative model

Usage

# S3 method for class 'net_mogen'
hg_get(
  x,
  what = c("orders", "transitions", "paths", "pathways"),
  ...,
  order = NULL,
  k = 2L,
  min_count = 1L,
  top = NULL
)

Arguments

x: A net_mogen object from mogen().
what: "orders" (default) for the per-order model-selection table, "transitions" for the fitted transition probabilities of one layer, "paths" for the frequency of every observed path of k states, or "pathways" for the layer's transitions written as pathways ("a b -> c"), the form simplicial() turns into simplices.
...: For what = "pathways", min_prob (drop transitions below this probability). Otherwise unused.
order: Integer or NULL. For what = "transitions" and "pathways": which layer to read. Default NULL uses the selected order.
k: Integer vector, for what = "paths": the path length or lengths in states, each between 2 and the model's highest order plus one, e.g. k = 3 or k = 2:4. Default 2 (single transitions).
min_count: Integer, for what = "transitions" and "pathways": drop transitions observed fewer times. Default 1.
top: Integer or NULL. Return only the first top rows, applied after any filter and after sort_by, so sort_by and top compose. Default NULL returns every row.

Value

A data.frame. For what = "orders", one row per candidate order: order, log_likelihood, aic, bic, df, layer_df, and optimal (logical, TRUE for the selected order). For what = "transitions", one row per transition: path, count, probability, from, to. For what = "paths", one row per distinct path of each length in k (top applies within each length), grouped by length and most frequent first (ties alphabetical): k, path, count, proportion (share of all k-state paths, rounded to 4 decimals). For what = "pathways", one row per transition with the column pathway.

Examples

seqs <- list(c("a", "b", "c", "a", "b", "c"), c("x", "b", "d", "x", "b", "d"),
             c("a", "b", "c", "a", "b", "c"), c("x", "b", "d", "x", "b", "d"))
mg <- mogen(seqs, max_order = 2)
hg_get(mg)
#>   order log_likelihood      aic      bic df layer_df optimal
#> 1     0     -37.457050 82.91410 87.62631  4        4   FALSE
#> 2     1     -12.712215 35.42443 41.31470  5        1   FALSE
#> 3     2      -7.167038 24.33408 30.22434  5        0    TRUE
hg_get(mg, what = "transitions", top = 5)
#>          path count probability   from to
#> 1 a -> b -> c     4           1 a -> b  c
#> 2 x -> b -> d     4           1 x -> b  d
#> 3 c -> a -> b     2           1 c -> a  b
#> 4 b -> c -> a     2           1 b -> c  a
#> 5 b -> d -> x     2           1 b -> d  x
hg_get(mg, what = "paths", k = 3)
#>   k        path count proportion
#> 1 3 a -> b -> c     4      0.250
#> 2 3 x -> b -> d     4      0.250
#> 3 3 b -> c -> a     2      0.125
#> 4 3 b -> d -> x     2      0.125
#> 5 3 c -> a -> b     2      0.125
#> 6 3 d -> x -> b     2      0.125