lcMethodFeature: Feature-based clustering
In latrend: A Framework for Clustering Longitudinal Data

lcMethodFeature

R Documentation

Feature-based clustering

Description

Feature-based clustering.

Usage

lcMethodFeature(
  response,
  representationStep,
  clusterStep,
  standardize = scale,
  center = meanNA,
  time = getOption("latrend.time"),
  id = getOption("latrend.id"),
  ...
)

Arguments

`response`	The name of the response variable.
`representationStep`	A `function` with signature `⁠function(method, data)⁠` that computes the representation per strata, returned as a `matrix`. Alternatively, `representationStep` is a pre-computed representation `matrix`.
`clusterStep`	A `function` with signature `⁠function(repdata)⁠` that outputs a `lcModel`.
`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.
`center`	The `function` for computing the longitudinal cluster centers, used for representing the cluster trajectories.
`time`	The name of the time variable.
`id`	The name of the trajectory identification variable.
`...`	Additional arguments.

Linear regresion & k-means example

In this example we define a feature-based approach where each trajectory is represented using a linear regression model. The coefficients of the trajectories are then clustered using k-means.

Note that this method is already implemented as lcMethodLMKM().

Representation step:

repStep <- function(method, data, verbose) {
  library(data.table)
  library(magrittr)
  xdata = as.data.table(data)
  coefdata <- xdata[,
    lm(method$formula, .SD) 
    keyby = c(method$id)
  ]
  # exclude the id column
  coefmat <- subset(coefdata, select = -1) 
  rownames(coefmat) <- coefdata[[method$id]]
  return(coefmat)
}

Cluster step:

clusStep <- function(method, data, repMat, envir, verbose) {
  km <- kmeans(repMat, centers = method$nClusters)

  lcModelPartition(
    response = method$response,
    data = data,
    trajectoryAssignments = km$cluster
  )
}

Now specify the method and fit the model:

data(latrendData)
method <- lcMethodFeature(
  formula = Y ~ Time,
  response = "Y",
  id = "Id",
  time = "Time",
  representationStep = repStep,
  clusterStep = clusStep

model <- latrend(method, data = latrendData)
)

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