Feature-based clustering

Feature-based clustering.

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)
)

Arguments

Linear regresion & k-means example

See also