predict_next_state: Predict the next state from a transition model

In gp3sequences: Transparent Analysis of Ordered Categorical Sequences

Predict the next state from a transition model

Description

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"))

gp3sequences documentation built on Aug. 23, 2026, 5:10 p.m.