Simulate grouped educational event logs

Usage

simulate_event_log(
  groups = 5L,
  actors = 10L,
  courses = 1L,
  states = NULL,
  n_states = 8L,
  state_categories = "all",
  sequence_length = c(10L, 30L),
  achievement_levels = c("low", "medium", "high"),
  achievement_probabilities = NULL,
  start_time = as.POSIXct("2020-01-01", tz = "UTC"),
  interval_range = c(60, 600),
  transitions = NULL,
  initial = NULL,
  seed = NULL,
  ...,
  batch = NULL
)

Arguments

groups: Number of groups.
actors: Actors per group.
courses: Number or labels of courses.
states, n_states, state_categories: State-space options.
sequence_length: Fixed length or minimum/maximum range.
achievement_levels: Achievement labels.
achievement_probabilities: Achievement probabilities, one per level and summing to one. When NULL, the levels are equally likely.
start_time: Initial timestamp. Every actor's first event is placed at this time.
interval_range: Minimum and maximum seconds between an actor's consecutive events, drawn uniformly.
transitions: Common matrix, one matrix per group, or NULL.
initial: Initial probabilities.
seed: Optional random seed.
...: Advanced options passed to simulate_group_sequences().
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 one row per actor event and columns group, id, course, achievement, period, state, and timestamp. as.data.frame(x, what = ) also returns transitions (group, from, to, probability), actors (group, id, course, achievement), groups (group, actors), wide (one row per actor, columns id, S1, ..., plus actor metadata), and one_hot (the event log with state replaced by indicator columns).

Examples

result <- simulate_event_log(groups = 2, actors = 10, sequence_length = 8, seed = 1)
head(result)
#> <simulab_sim:event_log> 6 rows x 7 columns
#>     group    id   course achievement period    state           timestamp
#> 1 Group 1 G1_A1 Course 1        high      1 Continue 2020-01-01 00:00:00
#> 2 Group 1 G1_A1 Course 1        high      2 Complete 2020-01-01 00:01:10
#> 3 Group 1 G1_A1 Course 1        high      3 Practice 2020-01-01 00:05:15
#> 4 Group 1 G1_A1 Course 1        high      4    Track 2020-01-01 00:06:32
#> 5 Group 1 G1_A1 Course 1        high      5    Track 2020-01-01 00:09:06
#> 6 Group 1 G1_A1 Course 1        high      6 Complete 2020-01-01 00:10:24
#> 
#> Truth (transitions):
#>      group          from        to probability
#> 1  Group 1      Complete  Complete  0.05326849
#> 2  Group 1     Encourage  Complete  0.21668978
#> 3  Group 1      Continue  Complete  0.29212077
#> 4  Group 1         Doubt  Complete  0.12525831
#> 5  Group 1 Differentiate  Complete  0.20205291
#> 6  Group 1         Track  Complete  0.12203496
#> 7  Group 1         Forum  Complete  0.09929164
#> 8  Group 1      Practice  Complete  0.06490684
#> 9  Group 1      Complete Encourage  0.03248789
#> 10 Group 1     Encourage Encourage  0.01809601
#> ... 118 more rows
#> 
#> Other tables: actors, groups, wide, one_hot. Read one with as.data.frame(x, what = "actors").
components(result)
#>         table rows columns
#> 1        data  160       7
#> 2 transitions  128       4
#> 3      actors   20       4
#> 4      groups    2       2
#> 5        wide   20      12
#> 6     one_hot  160      14