Generate one dataset following the Bakk & Kuha (2018) simulation design

Usage

generate_data(
  n,
  separation = c("low", "mid", "high"),
  scenario = c("covariate", "distal"),
  params = bk2018_params,
  seed = NULL
)

Arguments

n

Integer. Sample size (paper uses 500, 1000, or 2000).

separation

Character. One of "low", "mid", "high". Maps to pi = 0.70, 0.80, 0.90 respectively.

scenario

Character. One of:

"covariate": Zp (discrete, 1-5) predicts latent X via multinomial logit.
"distal": Latent X predicts continuous Zo via linear regression.

params

List of population parameters. Defaults to bk2018_params.

seed

Integer or NULL. Optional random seed for reproducibility.

Value

A data.frame with columns:

Y1 .. Y6: Binary indicators (always present).
X: True latent class, integer 1-3 (not observed in practice).
Zp: Integer covariate 1-5 (scenario "covariate" only).
Zo: Continuous distal outcome (scenario "distal" only).

Examples

# Covariate scenario with high separation
d <- generate_data(n = 200, separation = "high", scenario = "covariate",
                   seed = 1)
head(d)
#>   Y1 Y2 Y3 Y4 Y5 Y6 X Zp
#> 1  1  1  1  1  1  1 1  1
#> 2  1  1  1  1  1  1 1  4
#> 3  1  1  1  0  0  0 2  1
#> 4  1  1  1  0  0  0 2  2
#> 5  0  0  0  0  0  0 3  5
#> 6  1  1  0  1  1  1 1  3
colMeans(d)
#>    Y1    Y2    Y3    Y4    Y5    Y6     X    Zp 
#> 0.580 0.610 0.585 0.350 0.350 0.360 2.035 2.925 

# Distal outcome scenario
d2 <- generate_data(n = 200, separation = "high", scenario = "distal",
                    seed = 2)
head(d2)
#>   Y1 Y2 Y3 Y4 Y5 Y6 X         Zo
#> 1  1  1  1  0  0  0 2  2.2387443
#> 2  1  1  1  1  1  1 1 -0.7681038
#> 3  0  0  0  0  0  0 3 -0.3144379
#> 4  0  1  1  0  0  0 2  2.4997037
#> 5  1  0  1  1  1  1 1 -0.9304256
#> 6  1  1  1  1  1  1 1  0.3340337