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A permutation test generates a "null" model by randomising the response (for regression models) or group labels (for classification models). This is repeated many times to generate a distribution of performance metrics for the null model. This distribution can then be compared to the performance of the true model. If there is overlap between the true and null model performances then the model is overfitted.

Usage

permutation_test(number_of_permutations = 50, factor_name, ...)

Arguments

number_of_permutations

(numeric, integer) The number of permutations. The default is 50.

factor_name

(character) The name of a sample-meta column to use.

...

Additional slots and values passed to struct_class.

Value

A permutation_test object with the following output slots:

results.permuted(data.frame)
results.unpermuted(data.frame)
metric(data.frame)

Inheritance

A permutation_test object inherits the following struct classes:

[permutation_test] >> [resampler] >> [iterator] >> [struct_class]

Examples

M = permutation_test(
      number_of_permutations = 100,
      factor_name = "V1")

I=permutation_test(factor_name='Species')