A simulated multilevel longitudinal dataset made for the multilevel growth
mixture model. 600 students in 40 schools (15 per school) sit a reading
test on five occasions (wave 0 to 4). Two kinds of student follow two
trajectories: improving (starts near 48, gains 3 points a wave) and
plateauing (starts near 56, gains 0.3). Students vary around their
trajectory (random intercept SD 3, random slope SD 0.6, correlation -0.2;
residual SD 2). Schools are of two types: in supportive schools 80% of
students improve, in struggling schools 25% do, and a school's support
programme (standardized, measured once) makes it more likely to be
supportive. About 8% of tests were missed at random.
Format
A data frame with 2780 rows (600 students, 40 schools) and 7 columns:
- school
Integer school identifier, 1 to 40. Pass it as
cluster.- student
Integer student identifier, 1 to 600. Pass it as
id.- wave
Integer test occasion, 0 to 4. The time variable.
- score
Reading score.
- programme
Standardized school support programme, constant within school.
- school_type
Factor,
"supportive"or"struggling": the school's generating type. Not for fitting; kept to check recovery.- trajectory
Factor,
"improving"or"plateauing": the student's generating kind. Not for fitting; kept to check recovery.
Details
The school types are what a multilevel growth mixture model's group
classes recover: mixture_regression(..., cluster = "school", n_group_classes = 2), with group_membership = "programme".
Examples
descriptives(growth_schools, c("score", "programme"))
#> variable n n_missing mean sd min max n_distinct
#> 1 score 2780 0 55.17003597 5.0187548 35.90 70.50 262
#> 2 programme 2780 0 0.04313669 0.9708406 -2.36 2.25 38
# \donttest{
fit <- mixture_regression(score ~ wave, growth_schools, n_classes = 2,
id = "student", class_level = "group",
random = "wave", random_covariance = "equal",
cluster = "school", n_group_classes = 2,
group_membership = "programme",
n_starts = 2, seed = 1)
get_results(fit, "group_classes")
#> Group classes: trajectory classes within each
#>
#> Group class Class Probability SE Share of clusters Clusters
#> ------------- ------- ----------- ----- ----------------- --------
#> Group class 1 Class 1 0.851 0.024 0.528 21
#> Group class 1 Class 2 0.149 0.024 0.528 21
#> Group class 2 Class 1 0.245 0.029 0.472 19
#> Group class 2 Class 2 0.755 0.029 0.472 19
get_results(fit, "recovery", data = growth_schools, truth = "trajectory")
#> Recovery of a known classification
#>
#> Assigned trajectory Persons Share of assigned
#> -------- ---------- ------- -----------------
#> Class 1 improving 321 0.950
#> Class 2 improving 14 0.053
#> Class 1 plateauing 17 0.050
#> Class 2 plateauing 248 0.947
# }