Note: this function should not be called directly, as it is part of the lcMethod fitting process. For fitting an lcMethod object to a dataset, see latrend().
The validate() function of the lcMethod object validates the method with respect to the training data.
This enables a method to verify, for example:
whether the formula covariates are present.
whether the argument combination settings are valid.
whether the data is suitable for training.
By default, the validate() function checks whether the id, time, and response variables are present as columns in the training data.
# S4 method for lcMethod
validate(method, data, envir = NULL, ...)An object inheriting from lcMethod with all its arguments having been evaluated and finalized.
A data.frame representing the transformed training data.
The environment in which the lcMethod should be evaluated
Not used.
Either TRUE if all validation checks passed,
or a character containing a description of the failed validation checks.
An example implementation checking for the existence of specific arguments and type:
library(assertthat) setMethod("validate", "lcMethodExample", function(method, data, envir = NULL, ...) { validate_that( hasName(method, "myArgument"), hasName(method, "anotherArgument"), is.numeric(method$myArgument) ) })
Each lcMethod subclass defines a type of methods in terms of a series of steps for estimating the method.
These steps, as part of the fitting procedure, are executed by latrend() in the following order:
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.