Each candidate is scored on every item by a panel of `raters_per_person` raters drawn at random from the pool.
Usage
df_simulate(
n_persons = 500,
n_items = 4,
n_raters = 12,
raters_per_person = 2,
n_cat = 5,
theta_mean = 0,
theta_sd = 1,
item_sd = 0.5,
severity_sd = 0.5,
tau = NULL,
seed = NULL
)Arguments
- n_persons, n_items, n_raters
Facet sizes.
- raters_per_person
Panel size per candidate.
- n_cat
Number of score categories (scores 0..n_cat-1).
- theta_mean, theta_sd
Candidate ability distribution.
- item_sd, severity_sd
SDs of item difficulty and rater severity (both centered).
- tau
Category thresholds; defaults to equally spaced on [-1.5, 1.5].
- seed
Optional RNG seed.
Value
A `df_sim` object: `$data` (a `df_data`) and `$par`, the true parameters (`theta`, `delta`, `lambda`, `tau`, all named).
Examples
sim <- df_simulate(n_persons = 100, n_items = 3, n_raters = 6, seed = 1)
head(sim$data)
#> person item rater score
#> 1 P0001 I01 R01 1
#> 2 P0001 I02 R01 1
#> 3 P0001 I03 R01 3
#> 4 P0001 I01 R02 1
#> 5 P0001 I02 R02 2
#> 6 P0001 I03 R02 3
sim$par$lambda # true rater severities
#> R01 R02 R03 R04 R05 R06
#> -0.19446882 -0.60077553 0.61016043 0.08487053 0.18160391 -0.08139053