lind.r_backends.design package

Submodules

lind.r_backends.design.taguchi module

taguchi: Utilities for using the Taguchi method of experimental design.

Developer Note: One of the reasons for including R backends in this package is to make existing DOE packages available to python users. Howerver, many of these R packages use custom R classes instead of R dataframes. Rather than translating these classes into R dataframes and then converting to pandas dataframes, I opted to print the class. Its a quick and dirty interim solution.

lind.r_backends.design.taguchi.taguchi_choose(factor_type_a: int = 0, factor_level_a: int = 0, factor_type_b: int = 0, factor_level_b: int = 0, num_interactions: int = 0) → None

Prints a recommended Taguchi design (orthogonal array) based on the inputes. The function taguchi_design can be used to see the recommended design.

Parameters

• factor_type_a (int) – the level of factor group a; should be >= 2

• factor_level_a (int) – the number of factors in factor group a; should be >= 2

• factor_type_b (int) – the level of factor group b; should be >= 2

• factor_level_b (int) – the number of factors in factor group b; should be >= 2

• num_interactions (int) – the number of interactions across the factor groups

Returns

nothing is returned

Return type

None

Examples

>>> taguchi_choose(
>>>     factor_type_a = 2, factor_level_a = 3,
>>>     factor_type_b = 0, factor_level_b = 0,
>>>     num_interactions = 1)

lind.r_backends.design.taguchi.taguchi_design(design: str = 'L4_2') → None

Prints out the details of the recommended Taguchi design.

Parameters

design (str) – a string defining the name of the taguchi orthogonal array of interest

Returns

nothing is returned

Return type

None

Examples

>>> taguchi_design(design = "L4_2")

Module contents

design related r backends