Calls simulate_sequences() repetitions times and stacks the results, so
each dataset is drawn from its own transition system rather than from a
shared one.
Arguments
- repetitions
Number of datasets. A single positive whole number, defaulting to
1.- ...
Arguments passed to
simulate_sequences().- seed
Optional base seed. A single number, or
NULL(the default). Datasetiusesseed + i - 1.
Value
A simulab_sim base data.frame in long form, one row per dataset,
sequence and position, with the column dataset followed by the columns
simulate_sequences() returns (id, period and state). Components:
transitions, the generating transition probabilities, one row per dataset
and from/to pair with columns dataset, from, to and probability;
and initial_probabilities, one row per dataset and state with columns
dataset, state and probability.
Examples
result <- simulate_sequence_batches(
repetitions = 3, n = 20, n_states = 3, chain_length = 10, seed = 1
)
head(result)
#> <simulab_sim:sequence_batches> 6 rows x 4 columns
#> dataset id period state
#> 1 1 1 1 Reflect
#> 2 1 1 2 Learn
#> 3 1 1 3 Reflect
#> 4 1 1 4 Learn
#> 5 1 1 5 Reflect
#> 6 1 1 6 Learn
#>
#> Truth (transitions):
#> dataset from to probability
#> 1 1 Reflect Reflect 0.06261118
#> 2 1 Learn Reflect 0.42158819
#> 3 1 Plan Reflect 0.66416516
#> 4 1 Reflect Learn 0.86660136
#> 5 1 Learn Learn 0.49915316
#> 6 1 Plan Learn 0.16698516
#> 7 1 Reflect Plan 0.07078746
#> 8 1 Learn Plan 0.07925865
#> 9 1 Plan Plan 0.16884968
#> 10 2 Read Read 0.05043712
#> ... 17 more rows
#>
#> Other tables: initial_probabilities. Read one with as.data.frame(x, what = "initial_probabilities").