A simulated dataset for mixture_regression(), built so that every nesting of the
mixture regression has a model that generated some of its columns, and the
truth is kept for checking recovery.
Format
A data frame with 900 rows (150 students by 6 weeks) and 10 columns:
- student
Integer student identifier, 1 to 150.
- week
Integer study week, 1 to 6.
- hours
Hours studied that week, 0 to 12.
- sleep
Hours slept the night before the quiz.
- motivation
Standardized motivation, constant within student.
- score
Quiz score.
- passed
Integer 0/1: passed the weekly check.
- questions
Count of questions the student asked that week.
- strategy
Factor,
"deep"or"surface": the week's generating strategy. Not for fitting; kept to check recovery.- student_type
Factor,
"steady"or"erratic": the student's generating kind. Not for fitting; kept to check recovery.
Details
150 students each report six study weeks. In every week a student studies
with one of two strategies, and the strategy decides how hours become a quiz
score: a deep week gains about 4.5 points per hour from a base of 35, a
surface week under one point per hour from a base of 55. Students are of two
kinds that differ in their mix of strategies – steady students study
deeply in about 85% of weeks, erratic ones in about 25% – and higher
motivation makes a student more likely to be steady. More sleep makes a
deep week more likely.
So score ~ hours is a two-class mixture at the level of the week
(class_level = "observation"), whose class shares differ by student (the
two-level model, id = "student", n_group_classes = 2). passed depends
on the student's kind, not the week's strategy, so it is a mixture at the
level of the student (class_level = "group"). questions is a count whose
Poisson regression depends on the week's strategy.
Examples
fit <- mixture_regression(score ~ hours, data = study_hours, n_classes = 2,
n_starts = 3, seed = 1)
get_results(fit, "coefficients")
#> Regression coefficients (95% CI)
#>
#> Class Term Estimate 95% CI p
#> ------- --------- -------- -------------- -----
#> Class 1 Intercept 34.35 [32.94, 35.76] <.001
#> Class 1 hours 4.57 [ 4.36, 4.77] <.001
#> Class 2 Intercept 54.90 [52.75, 57.04] <.001
#> Class 2 hours 0.81 [ 0.48, 1.14] <.001