Latent-class growth analysis of longitudinal trajectories

Fits a finite mixture of polynomial growth trajectories to panel data (each subject belongs to one latent class), selecting the number of classes by BIC. Returns per-subject class assignments, posterior class probabilities, and the normalised entropy of the classification.

Usage

latentClassGrowth(
  data,
  subject,
  time,
  outcome,
  n_classes = 1:3,
  degree = 1,
  seed = NULL
)

Arguments

data

A long-format data frame.

subject, time, outcome

Column names for the grouping factor, the time variable, and the response.

n_classes

Integer vector of class counts to compare (default 1:3); the BIC-minimising count is selected.

degree

Polynomial degree of the per-class growth curve (default 1, linear).

seed

Optional RNG seed (flexmix uses random starts).

Value

An AnalysisResult (type "latent_class_growth") whose result holds the selected n_classes, the bic table, the per-subject assignment, the posterior probabilities, the entropy, and the fitted flexmix model.

References

Nagin 2005 (group-based trajectory modelling); Gruen & Leisch 2008 (flexmix).

See also

recoveryTrajectoryLME(), proportionalRecoveryRule()

Examples

set.seed(1)
df <- do.call(rbind, lapply(1:40, function(s) {
  fast <- s <= 20; t <- 0:6
  y <- (if (fast) 5 * t else 0.5 * t) + rnorm(7, 0, 1.5)
  data.frame(subject = s, time = t, y = y)
}))
latentClassGrowth(df, "subject", "time", "y", n_classes = 1:3)
#> <AnalysisResult> latent_class_growth 
#>   estimate: 2 
#>   method: flexmix_lcga 
#>   fields: n_classes, bic, assignment, posterior, entropy, class_sizes, model