Tables of a Markov stability analysis

Usage

# S3 method for class 'net_markov_stability'
hg_get(
  x,
  what = c("states", "passage_time", "stationary"),
  ...,
  sort_by = NULL,
  decreasing = TRUE,
  from = NULL,
  to = NULL,
  top = NULL
)

Arguments

x: A net_markov_stability object from hg_markov_stability().
what: Which table: "states" (default, one row per state), "passage_time" (one row per ordered pair of states) or "stationary" (one row per state, distribution only).
...: Ignored.
sort_by: NULL (construction order, the default) or a column name of the selected table.
decreasing: Logical. Sort sort_by from largest to smallest (TRUE, the default) or smallest to largest (FALSE). Ties are broken by the row key, ascending in both directions: state, or from then to for the passage table.
from, to: NULL (default, every state) or a character vector of states. Restrict the what = "passage_time" table to passages leaving the from states and arriving at the to states. Only that table has from/to columns; using either with another what, or naming a state the chain does not have, raises hypernets_bad_input.
top: Integer or NULL. Keep only the first top rows, applied last: after what, from/to and sort_by.

Value

A base data.frame. For what = "states", one row per state with state, persistence, stationary, return_time, sojourn_time, avg_time_to_others and avg_time_from_others, as the estimator stores them (probabilities rounded to 4 decimals, times to 2). For what = "passage_time", one row per ordered pair with from, to and steps. For what = "stationary", one row per state with state, stationary and return_time; the passage and stationary tables are unrounded.

Examples

P <- matrix(c(0.5, 0.3, 0.2,
              0.2, 0.5, 0.3,
              0.1, 0.4, 0.5), 3, 3, byrow = TRUE,
            dimnames = list(c("A", "B", "C"), c("A", "B", "C")))
ms <- hg_markov_stability(P)
hg_get(ms, sort_by = "stationary")
#>   state persistence stationary return_time sojourn_time avg_time_to_others
#> 1     B         0.5     0.4182        2.39            2               4.92
#> 2     C         0.5     0.3455        2.89            2               4.77
#> 3     A         0.5     0.2364        4.23            2               3.63
#>   avg_time_from_others
#> 1                 2.83
#> 2                 3.95
#> 3                 6.54
hg_get(ms, what = "passage_time", from = "A")
#>   from to    steps
#> 1    A  A 4.230769
#> 2    A  B 3.043478
#> 3    A  C 4.210526
hg_get(ms, what = "stationary")
#>   state stationary return_time
#> 1     A  0.2363636    4.230769
#> 2     B  0.4181818    2.391304
#> 3     C  0.3454545    2.894737