Create method for NMR data analysis

Create method for NMR data analysis

Usage

new_nmr_data_analysis_method(
  train_evaluate_model,
  train_evaluate_model_params_inner,
  choose_best_inner,
  train_evaluate_model_params_outer,
  train_evaluate_model_digest_outer
)

Arguments

train_evaluate_model

A function. The train_evaluate_model must have the following signature:

    function(x_train, y_train, identity_train, x_test, y_test, identity_test, ...)

The x_train and y_train (and their test counterparts) are self-explanatory.

The identity_ arguments are expected to be factors. They can be used for instance with a callback that uses mixOmics::plsda in a multilevel approach for longitudinal studies. In those studies the identity would be an identifier of the subject.

The ... arguments are free to be defined for each train_evaluate_model.

train_evaluate_model_params_inner, train_evaluate_model_params_outer

A list with additional arguments to pass to train_evaluate_model either in the inner cv loop or in the outer cv loop.

choose_best_inner

A function with a single argument:

function(inner_cv_results)

The argument is a list of train_evaluate_model outputs. The return value of must be a list with at least an element named train_evaluate_model_args. train_evaluate_model_args must be a named list.

• Each element must be named as one of the train_evaluate_model arguments.

• Each element must be a vector as long as the number of outer cross-validations.

• The values of each vector must be the values that the train_evaluate_model argument must take on each outer cross-validation iteration Additional list elements can be returned and will be given back to the user

train_evaluate_model_digest_outer

A function with a single argument:

function(outer_cv_results)

The argument is a list of train_evaluate_model outputs in outer cross-validation. The return value is returned by nmr_data_analysis

Value

An object encapsulating the method dependent functions that can be used with nmr_data_analysis