simulate_markov: Simulate Markov chains

In simulab: Simulates a Wide Variety of Data Shapes, Sizes and Distributions

Simulate Markov chains

Description

Simulate Markov chains

Usage

simulate_markov(
  n,
  transition,
  chain_length,
  initial = NULL,
  states = NULL,
  trim_state = NULL,
  id = "id",
  period = "period",
  state = "state",
  seed = NULL,
  batch = NULL
)

Arguments

n	Number of chains.
transition	Square transition matrix, or a tidy transition table with from, to, and probability columns. Every row of the implied matrix must sum to one.
chain_length	Chain length.
initial	Starting-state probabilities or a single fixed start state. When NULL, every chain starts in the first state.
states	Optional state labels.
trim_state	Optional terminal state. The first occurrence is kept and all later observations of that chain are removed.
id, period, state	Output variable names.
seed	Optional random seed.
batch	Optional single positive whole number. When given, the simulator runs batch times and returns a plain list of batch results, each exactly what the same call without batch returns. With a seed, every dataset gets its own seed drawn from seed, so the whole batch is reproducible; without one the datasets are consecutive draws from the session's random-number stream. The default NULL returns a single result.

Value

A long-form simulab_sim base data.frame with columns id, period, and state (renamed by those arguments), one row per chain position. as.data.frame(x, what = ) also returns transitions (from, to, probability), initial_probabilities (state, probability), and wide (one row per chain, columns id, S1, S2, ...).

Examples

transition <- matrix(c(0.7, 0.3, 0.4, 0.6), nrow = 2, byrow = TRUE)

result <- simulate_markov(
  n = 50, transition = transition, chain_length = 20,
  states = c("A", "B"), seed = 1
)
head(result)
summarize_transitions(result, normalize = TRUE)

# Transitions may also be given as a tidy from/to/probability table.
tidy_transitions <- data.frame(
  from = c("A", "A", "B", "B"),
  to = c("A", "B", "A", "B"),
  probability = c(0.7, 0.3, 0.4, 0.6)
)
head(simulate_markov(
  n = 50, transition = tidy_transitions, chain_length = 20, seed = 1
))

simulab documentation built on Oct. 15, 2026, 5:08 p.m.