Simulate outcomes from a growth mixture model — simulate.latents_growth

Draws new outcomes on the fitted design: each person's class from their membership probabilities, their random effects from that class's covariance, and their outcomes around their own trajectory with the class's residual variance.

Usage

# S3 method for class 'latents_growth_mixture'
simulate(object, nsim = 1, seed = NULL, ...)

Arguments

object: A latents_growth_mixture fit.
nsim: Number of simulated outcome vectors.
seed: NULL or an integer seed; the caller's random state is restored afterwards.
...: Unused.

Value

A base data.frame with one row per fitted row and one column per simulation, sim_1, sim_2, ...

Examples

few_students <- subset(growth_scores, student <= 60)
small_fit <- mixture_regression(score ~ wave, few_students, n_classes = 2,
                                id = "student", class_level = "group",
                                random = "intercept", seed = 1)
head(simulate(small_fit, nsim = 2, seed = 1))
#>      sim_1    sim_2
#> 1 67.09026 48.43381
#> 2 71.08431 55.15826
#> 3 69.35297 64.33275
#> 4 65.95387 63.16687
#> 5 67.99706 66.85900
#> 6 62.30210 68.67314
# \donttest{
# All 300 students, with random intercepts and slopes
fit <- mixture_regression(score ~ wave, growth_scores, n_classes = 3,
                          id = "student", class_level = "group",
                          random = "wave", random_covariance = "equal",
                          n_starts = 2, seed = 1)
head(simulate(fit, nsim = 2, seed = 1))
#>      sim_1    sim_2
#> 1 46.37865 49.53938
#> 2 54.89669 56.64919
#> 3 57.20172 56.30151
#> 4 65.90467 58.25365
#> 5 63.93110 61.80666
#> 6 65.30118 69.37129
# }