lcMethod estimation step: logic for post-processing the fitted lcModel

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 postFit() function of the lcMethod object defines how the lcModel object returned by fit() should be post-processed. This can be used, for example, to:

• Resolve label switching.

• Clean up the internal model representation.

• Correct estimation errors.

• Compute additional metrics.

By default, this method does not do anything. It merely returns the original lcModel object.

This is the last step in the lcMethod fitting procedure. The postFit method may be called again on fitted lcModel objects, allowing post-processing to be updated for existing models.

postFit(method, data, model, envir, verbose, ...)

# S4 method for lcMethod
postFit(method, data, model, 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.

model

The lcModel object returned by fit().

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 updated lcModel object.

Implementation

The method is intended to be able to be called on previously fitted lcModel objects as well, allowing for potential bugfixes or additions to previously fitted models. Therefore, when implementing this method, ensure that you do not discard information from the model which would prevent the method from being run a second time on the object.

In this example, the lcModelExample class is assumed to be defined with a slot named "centers":

setMethod("postFit", "lcMethodExample", function(method, data, model, envir, verbose) {
  # compute and store the cluster centers
  model@centers <- INTENSIVE_COMPUTATION
  return(model)
})

Estimation procedure

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

1. compose(): Evaluate and finalize the method argument values.

2. validate(): Check the validity of the method argument values in relation to the dataset.

3. prepareData(): Process the training data for fitting.

4. preFit(): Prepare environment for estimation, independent of training data.

5. fit(): Estimate the specified method on the training data, outputting an object inheriting from lcModel.

6. postFit(): Post-process the outputted lcModel object.

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