Two-step clustering through latent growth curve modeling and k-means

Two-step clustering through latent growth curve modeling and k-means.

lcMethodGCKM(
  formula,
  time = getOption("latrend.time"),
  id = getOption("latrend.id"),
  nClusters = 2,
  center = meanNA,
  standardize = scale,
  ...
)

Arguments

formula

Formula, including a random effects component for the trajectory. See lme4::lmer formula syntax.

time

The name of the time variable..

id

The name of the trajectory identifier variable.

nClusters

The number of clusters.

center

A function that computes the cluster center based on the original trajectories associated with the respective cluster. By default, the mean is computed.

standardize

A function to standardize the output matrix of the representation step. By default, the output is shifted and rescaled to ensure zero mean and unit variance.

...

Arguments passed to lme4::lmer. The following external arguments are ignored: data, centers, trace.

See also

Other lcMethod implementations: getArgumentDefaults(), getArgumentExclusions(), lcMethod-class, lcMethodAkmedoids, lcMethodCrimCV, lcMethodDtwclust, lcMethodFeature, lcMethodFunFEM, lcMethodFunction, lcMethodKML, lcMethodLMKM, lcMethodLcmmGBTM, lcMethodLcmmGMM, lcMethodMclustLLPA, lcMethodMixAK_GLMM, lcMethodMixtoolsGMM, lcMethodMixtoolsNPRM, lcMethodRandom, lcMethodStratify

Examples

data(latrendData)

if (require("lme4")) {
  method <- lcMethodGCKM(Y ~ (Time | Id), id = "Id", time = "Time", nClusters = 3)
  model <- latrend(method, latrendData)
}