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Simulate Sequence Data with Known Cluster Structure

Source: R/simulate_latent.R
simulate_seq_clusters.Rd

Generate wide-format sequence data where each row is drawn from one of K Markov chains (clusters), each governed by its own transition matrix. Supports explicit matrix supply or automatic random generation.

Usage

simulate_seq_clusters(
  trans_list = NULL,
  props = NULL,
  n = 300L,
  seq_length = 20L,
  init_probs = NULL,
  n_clusters = 3L,
  n_states = 10L,
  states = NULL,
  seed = NULL
)

Arguments

trans_list

Named or unnamed list of square numeric transition matrices, or NULL for automatic generation. When NULL, n_clusters and n_states are used to build random row-stochastic matrices.

props

Numeric vector of mixing proportions (need not sum to 1; normalised internally). Length must equal K. Defaults to equal mixing.

n

Positive integer. Total number of sequences to generate.

seq_length

Positive integer. Number of time-points per sequence (columns T1...T{seq_length}). Default 20.

init_probs

Either a numeric vector of initial state probabilities shared across clusters, a list of per-cluster vectors, or NULL (uniform). Must be named consistently with the state names derived from the transition matrix row/column names.

n_clusters

Positive integer. Number of clusters when trans_list = NULL. Default 3.

n_states

Positive integer. Number of states when trans_list = NULL. Default 10.

states

Character vector of state names when trans_list = NULL. Defaults to paste0("S", seq_len(n_states)).

seed

Integer or NULL. Random seed for reproducibility.

Value

A named list with elements:

data

data.frame with columns T1...T{seq_length} (character state labels) and true_cluster (integer 1...K).

params

list with trans_list (the K matrices used), props (normalised mixing proportions), and init_probs (per-cluster initial probability vectors as a list).

Examples

m1 <- matrix(c(0.8, 0.2, 0.3, 0.7), nrow = 2, byrow = TRUE,
             dimnames = list(c("A","B"), c("A","B")))
m2 <- matrix(c(0.2, 0.8, 0.7, 0.3), nrow = 2, byrow = TRUE,
             dimnames = list(c("A","B"), c("A","B")))
r  <- simulate_seq_clusters(trans_list = list(m1, m2), n = 100, seed = 1)
head(r$data)
#>   T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13 T14 T15 T16 T17 T18 T19 T20
#> 1  A  B  A  A  B  A  B  A  A   B   B   B   A   B   A   B   B   A   B   A
#> 2  A  B  A  B  B  A  B  A  B   A   B   A   B   A   A   B   A   B   B   A
#> 3  A  A  A  A  A  A  A  A  A   B   B   B   B   B   B   B   B   B   B   B
#> 4  B  A  A  A  B  B  B  B  A   A   A   B   A   A   A   B   B   A   A   B
#> 5  B  A  A  B  B  A  B  B  B   A   B   A   B   B   A   B   A   A   B   B
#> 6  B  B  B  B  B  B  B  B  B   A   B   B   A   B   A   A   A   B   A   A
#>   true_cluster
#> 1            2
#> 2            2
#> 3            1
#> 4            1
#> 5            2
#> 6            1
r$params$props
#> [1] 0.5 0.5