Tidy results of a growth mixture model — get_results.latents_growth

Every table of a mixture_regression() fit with random = is a base data.frame with one row per observation unit of that table.

Usage

# S3 method for class 'latents_growth_mixture'
get_results(
  x,
  what = "coefficients",
  level = 0.95,
  vcov_type = NULL,
  time = NULL,
  data = NULL,
  truth = NULL,
  ...
)

# S3 method for class 'latents_growth_mixture'
as.data.frame(
  x,
  row.names = NULL,
  optional = FALSE,
  what = "coefficients",
  ...
)

# S3 method for class 'latents_growth_mixture'
coef(object, ...)

# S3 method for class 'latents_growth_mixture'
vcov(object, type = NULL, ...)

# S3 method for class 'latents_growth_mixture'
confint(object, parm, level = 0.95, ...)

# S3 method for class 'latents_growth_mixture'
logLik(object, ...)

# S3 method for class 'latents_growth_mixture'
nobs(object, ...)

Arguments

x: A latents_growth_mixture fit.
what: Which table: "coefficients" (one row per class and term, with Wald statistics; shared terms have class "common"), "classes" (share with interval, expected and assigned persons, average posterior, residual standard deviation), "random" (per class: random-effect variances, standard deviations and correlations with intervals formed on the log and Fisher-z scales), "membership" (membership logits against class 1), "fit" (one row: log likelihood, information criteria with persons as the sample size, entropy, convergence), "assignments" (one row per person: modal class and posteriors), "random_effects" (one row per person: predicted random effects under the modal class), "trajectories" (one row per class and time point: the mean trajectory with a confidence band), "individual" (one row per observation: observed value, class trajectory and the person's own predicted curve), "starts" (one row per start), "recovery" (needs data and truth: the modal classes cross-tabulated against a known classification that is constant within persons, with counts and shares of each assigned class), or "all" (a named list of every table but "recovery"). A multilevel growth mixture (cluster) adds "group_classes" (one row per group class and class: the class probabilities within the group class, their standard errors without membership covariates, the group class's share of the clusters and its assigned clusters) and "clusters" (one row per cluster: persons, modal group class and posteriors); its "membership" table holds the group-class logits (against group class 1) and the class logits with one intercept per group class, "assignments" gains each person's cluster, and "fit" counts clusters in bic (persons in bic_persons).
level: Confidence level of the intervals.
vcov_type: NULL (the stored type), "observed", "robust" or "opg". A weighted fit allows "robust" only.
time: For "trajectories" and "individual", the numeric variable to draw along; by default the first numeric variable of random.
data, truth: For "recovery": the data frame the model was fitted to and the name of its column holding the known classes.
...: Unused.
row.names, optional: Unused; for the generic.
object: A latents_growth_mixture fit.
type: As vcov_type.
parm: Unused.

Value

A base data.frame (or, for "all", a named list of them).

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)
get_results(small_fit, "classes")
#> Classes
#> 
#> Class    Share        95% CI  Persons  Avg. posterior  Residual SD
#> -------  -----  ------------  -------  --------------  -----------
#> Class 1   0.60  [0.47, 0.71]       36            0.99         4.04
#> Class 2   0.40  [0.29, 0.53]       24            1.00         3.39
get_results(small_fit, "random")
#> Random effects (95% CI)
#> 
#> Class    Parameter  Term       Estimate          95% CI
#> -------  ---------  ---------  --------  --------------
#> Class 1  Variance   Intercept     47.86  [29.32, 78.13]
#> Class 1  SD         Intercept      6.92  [ 5.41,  8.84]
#> Class 2  Variance   Intercept     23.08  [12.49, 42.66]
#> Class 2  SD         Intercept      4.80  [ 3.53,  6.53]
# \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 = 3, seed = 1)
get_results(fit, "classes")
#> Classes
#> 
#> Class    Share        95% CI  Persons  Avg. posterior  Residual SD
#> -------  -----  ------------  -------  --------------  -----------
#> Class 1   0.37  [0.32, 0.43]      112            0.97         2.75
#> Class 2   0.33  [0.28, 0.39]       99            0.98         2.96
#> Class 3   0.30  [0.24, 0.36]       89            0.94         2.77
get_results(fit, "random")
#> Random effects (95% CI)
#> 
#> Class        Parameter    Term              Estimate          95% CI
#> -----------  -----------  ----------------  --------  --------------
#> All classes  Variance     Intercept            16.79  [13.41, 21.01]
#> All classes  Variance     wave                  0.54  [ 0.40,  0.73]
#> All classes  SD           Intercept             4.10  [ 3.66,  4.58]
#> All classes  SD           wave                  0.73  [ 0.63,  0.86]
#> All classes  Correlation  Intercept x wave     -0.24  [-0.40, -0.07]
get_results(fit, "recovery", data = growth_scores, truth = "trajectory")
#> Recovery of a known classification
#> 
#> Assigned  trajectory  Persons  Share of assigned
#> --------  ----------  -------  -----------------
#> Class 1   improving       109              0.973
#> Class 2   improving         0              0.000
#> Class 3   improving         6              0.067
#> Class 1   stable            3              0.027
#> Class 2   stable            3              0.030
#> Class 3   stable           82              0.921
#> Class 1   declining         0              0.000
#> Class 2   declining        96              0.970
#> Class 3   declining         1              0.011
# }