Create a transition network from ordered sequences

Usage

create_transition_network(
  data,
  sequence_id_col = "sequence_id",
  order_col = "sequence_order",
  state_col = "state",
  group_cols = NULL,
  order = 1L,
  include_self = TRUE,
  normalise = c("count", "from", "global"),
  smoothing = 0,
  context_separator = " > "
)

Arguments

data: Long-format sequence data or a prepared result.
sequence_id_col, order_col, state_col: Sequence columns.
group_cols: Optional grouping columns constant within sequence.
order: Markov order. 1 creates ordinary state-to-state edges; larger values create context-to-next-state edges.
include_self: Include first-order self-transitions.
normalise: Edge weight scale: counts, conditional probabilities from each context, or global shares.
smoothing: Non-negative additive smoothing applied to observed edges.
context_separator: Separator used for higher-order contexts.

Value

A data frame of class gp3_transition_network containing context, next state, counts, weights, sequence prevalence, and group columns.

Examples

sequences <- data.frame(
  sequence_id = rep(c("s1", "s2", "s3", "s4"), each = 4L),
  sequence_order = rep(1:4, times = 4L),
  state = c("A", "B", "C", "D", "A", "B", "C", "C",
            "D", "C", "B", "A", "D", "C", "A", "A"),
  group = rep(c("g1", "g2"), each = 8L),
  stringsAsFactors = FALSE
)
create_transition_network(sequences, normalise = "from")
#>   group_key context from_state to_state count sequence_count
#> 1   __all__       A          A        A     1              1
#> 2   __all__       A          A        B     2              2
#> 3   __all__       B          B        A     1              1
#> 4   __all__       B          B        C     2              2
#> 5   __all__       C          C        A     1              1
#> 6   __all__       C          C        B     1              1
#> 7   __all__       C          C        C     1              1
#> 8   __all__       C          C        D     1              1
#> 9   __all__       D          D        C     2              2
#>   sequence_prevalence    weight
#> 1                0.25 0.3333333
#> 2                0.50 0.6666667
#> 3                0.25 0.3333333
#> 4                0.50 0.6666667
#> 5                0.25 0.2500000
#> 6                0.25 0.2500000
#> 7                0.25 0.2500000
#> 8                0.25 0.2500000
#> 9                0.50 1.0000000