Cluster longitudinal data using bootstrapping

Performs bootstrapping, generating samples from the given data at the id level, fitting a lcModel to each sample.

latrendBoot(
  method,
  data,
  samples = 50,
  seed = NULL,
  parallel = FALSE,
  errorHandling = "stop",
  envir = NULL,
  verbose = getOption("latrend.verbose")
)

Arguments

method

An lcMethod object specifying the longitudinal cluster method to apply, or the name (as character) of the lcMethod subclass to instantiate.

data

A data.frame.

samples

The number of bootstrap samples to evaluate.

seed

The seed to use. Optional.

parallel

Whether to enable parallel evaluation. See latrend-parallel. Method evaluation and dataset transformation is done on the calling thread.

errorHandling

Whether to "stop" on an error, or to "remove' evaluations that raised an error.

envir

The environment in which to evaluate the method arguments via compose(). If the data argument is of type call then this environment is also used to evaluate the data argument.

verbose

The level of verbosity. Either an object of class Verbose (see R.utils::Verbose for details), a logical indicating whether to show basic computation information, a numeric indicating the verbosity level (see R.utils::Verbose), or one of c('info', 'fine', 'finest').

Value

A lcModels object of length samples.

See also

Other longitudinal cluster fit functions: latrend(), latrendBatch(), latrendCV(), latrendRep()

Other validation methods: createTestDataFold(), createTestDataFolds(), createTrainDataFolds(), latrendCV(), lcModel-data-filters

Examples

data(latrendData)
method <- lcMethodLMKM(Y ~ Time, id = "Id", time = "Time")
bootModels <- latrendBoot(method, latrendData, samples = 10)

bootMAE <- metric(bootModels, name = "MAE")
mean(bootMAE)
#> [1] 0.2172096
sd(bootMAE)
#> [1] 0.007195816