lcMethod estimation step: logic for preparing the training 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 prepareData() function of the lcMethod object processes the training data prior to fitting the method. Example uses:

• Transforming the data to another format, e.g., a matrix.

• Truncating the response variable.

• Computing derived covariates.

• Creating additional data objects.

The computed variables are stored in an environment which is passed to the preFit() function for further processing.

By default, this method does not do anything.

prepareData(method, data, verbose, ...)

# S4 method for lcMethod
prepareData(method, data, 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.

verbose

A R.utils::Verbose object indicating the level of verbosity.

...

Not used.

Value

An environment.

An environment with the prepared data variable(s) that will be passed to preFit().

Implementation

A common use case for this method is when the internal method fitting procedure expects the data in a different format. In this example, the method converts the training data data.frame to a matrix of repeated and aligned trajectory measurements.

setMethod("prepareData", "lcMethodExample", function(method, data, verbose) {
  envir = new.env()
  # transform the data to matrix
  envir$dataMat = tsmatrix(data,
    id = idColumn, time = timeColumn, response = valueColumn)
  return(envir)
})

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.