lcMethod estimation step: logic for fitting the method to the processed data

Note: this function should not be called directly, as it is part of the lcMethod estimation procedure. For fitting an lcMethod object to a dataset, use the latrend() function or one of the other standard estimation functions.

The fit() function of the lcMethod object estimates the model with the evaluated method specification, processed training data, and prepared environment.

fit(method, data, envir, verbose, ...)

# S4 method for class 'lcMethod'
fit(method, data, envir, verbose)

Arguments

method: An object inheriting from lcMethod with all its arguments having been evaluated and finalized.
data: A data.frame representing the transformed training data.
envir: The environment containing variables generated by prepareData() and preFit().
verbose: A R.utils::Verbose object indicating the level of verbosity.
...: Not used.

Value

The fitted object, inheriting from lcModel.

Implementation

This method should be implemented for all lcMethod subclasses.


setMethod("fit", "lcMethodExample", function(method, data, envir, verbose) {
  # estimate the model or cluster parameters
  coefs <- FIT_CODE

  # create the lcModel object
  new("lcModelExample",
    method = method,
    data = data,
    model = coefs,
    clusterNames = make.clusterNames(method$nClusters)
  )
})

Estimation procedure

The steps for estimating a lcMethod object are defined and executed as follows:

compose(): Evaluate and finalize the method argument values.
validate(): Check the validity of the method argument values in relation to the dataset.
prepareData(): Process the training data for fitting.
preFit(): Prepare environment for estimation, independent of training data.
fit(): Estimate the specified method on the training data, outputting an object inheriting from lcModel.
postFit(): Post-process the outputted lcModel object.

The result of the fitting procedure is an lcModel object that inherits from the lcModel class.