Equal group sized sampling — equal

Samples are randomly chosen from each level such that the training set has equal numbers of samples for all levels. The number of samples is based on the input proportion and the smallest group size.

Usage

equal_split(p_train = 1, factor_name, ...)

Arguments

p_train: (numeric) The proportion of samples selected for the training set. The default is 1.
factor_name: (character) The name of a sample-meta column to use.
...: Additional slots and values passed to struct_class.

Value

A equal_split object with the following output slots:

`training`	(DatasetExperiment) A DatasetExperiment object containing samples selected for the training set.
`testing`	(DatasetExperiment) A DatasetExperiment object containing samples selected for the testing set.

Inheritance

A equal_split object inherits the following struct classes:

[equal_split] >> [split_data] >> [model] >> [struct_class]

Examples

M = equal_split(
      factor_name = "V1",
      p_train = 0.75)

D = iris_DatasetExperiment()
M = equal_split(factor_name='Species')
M = model_apply(M,D)