Predict the next state from a transition model

Predict the next state from a transition model

Usage

predict_next_state(model, history, top_n = NULL)

Arguments

model

A higher-order transition model.

history

Character vector of observed recent states.

top_n

Optional number of states to retain. Returned probabilities remain on the full-model scale and are not renormalised after truncation.

Value

A probability table ordered from highest to lowest probability, with the context order actually used.

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
)
model <- fit_higher_order_transition_model(sequences, order = 2L)
predict_next_state(model, c("A", "B"))
#>   order context next_state count probability used_order used_context
#> 1     2   A > B          C     2       0.625          2        A > B
#> 2     2   A > B          A     0       0.125          2        A > B
#> 3     2   A > B          B     0       0.125          2        A > B
#> 4     2   A > B          D     0       0.125          2        A > B