Two-step clustering through linear mixed modeling and k-means
Two-step clustering through linear mixed modeling and k-means.
lcMethodGCKM(
formula,
time = getOption("latrend.time"),
id = getOption("latrend.id"),
nClusters = 2,
center = meanNA,
...
)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 |
... |
Arguments passed to lme4::lmer. The following external arguments are ignored: data, centers, trace. |
Other lcMethod implementations:
lcMethod-class,
lcMethodAkmedoids,
lcMethodCrimCV,
lcMethodCustom,
lcMethodDtwclust,
lcMethodFeature,
lcMethodFunFEM,
lcMethodKML,
lcMethodLMKM,
lcMethodLcmmGBTM,
lcMethodLcmmGMM,
lcMethodLongclust,
lcMethodMclustLLPA,
lcMethodMixAK_GLMM,
lcMethodMixtoolsGMM,
lcMethodMixtoolsNPRM,
lcMethodRandom,
lcMethodStratify
library(lme4) data(latrendData) method <- lcMethodGCKM(Y ~ (Time | Id), id = "Id", time = "Time", nClusters = 3) model <- latrend(method, latrendData)
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