latrend (version 1.0.1)

lcMethodGCKM: Two-step clustering through linear mixed modeling and k-means

Description

Two-step clustering through linear mixed modeling and k-means.

Usage

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

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

Optional function for computing the longitudinal cluster centers, with signature (x).

...

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

See Also

Other lcMethod implementations: lcMethod-class, lcMethodAKMedoids, lcMethodCrimCV, lcMethodCustom, lcMethodDtwclust, lcMethodFunFEM, lcMethodKML, lcMethodLMKM, lcMethodLcmmGBTM, lcMethodLcmmGMM, lcMethodLongclust, lcMethodMclustLLPA, lcMethodMixAK_GLMM, lcMethodMixtoolsGMM, lcMethodMixtoolsNPRM, lcMethodRandom, lcMethodStratify, lcMethodTwoStep

Examples

# NOT RUN {
library(lme4)
data(latrendData)
method <- lcMethodGCKM(Y ~ (Time | Id), id = "Id", time = "Time", nClusters = 3)
model <- latrend(method, latrendData)
# }