Some new orthogonal arrays

Kuhfeld, Warren F.; Suen, Chung Yi

doi:10.1016/j.spl.2005.05.006

Cited by 26 publications

(47 citation statements)

References 8 publications

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“…Package DoE.base pursues a different route: It contains the previously-mentioned catalog of orthogonal arrays; most of its arrays have been taken from Kuhfeld (2010). In addition, since version 0.28, it contains a small separate catalog oacat3 of orthogonal arrays of strength at least 3 from other sources.…”

Section: Orthogonal Arrays With Package Doebasementioning

confidence: 99%

“…Kuhfeld (2010) makes much more extensive use of the catalog within a SAS software macro suite for creating experimental designs from the arrays in the catalog, specifically created with marketing applications in mind (Kuhfeld 2010). Such use can certainly also be combined with the catalog in package DoE.base.…”

Section: Further Developmentsmentioning

confidence: 99%

“…Such use can certainly also be combined with the catalog in package DoE.base. The dependent package support.CEs (Aizaki 2012) implements choice experiments in R. However, implementing a functionality as flexible as the one offered in the SAS macros by Kuhfeld (2010) would require substantial additional effort.…”

Section: Further Developmentsmentioning

confidence: 99%

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R Package DoE.base for Factorial Experiments

Grömping¹

2018

J. Stat. Soft.

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The R package DoE.base can be used for creating full factorial designs and general factorial experiments based on orthogonal arrays. Besides design creation, some analysis functionality is also available, particularly (augmented) half-normal effects plots. In addition to this specific functionality, the package provides convenience features for analyzing experimental designs and the infrastructure for a suite of further packages on designing and analyzing experiments. This infrastructure is available for use also by further design of experiments packages.

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Section: Orthogonal Arrays With Package Doebasementioning

confidence: 99%

Section: Further Developmentsmentioning

confidence: 99%

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R Package DoE.base for Factorial Experiments

Grömping¹

2018

J. Stat. Soft.

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“…Extensive online libraries are available in Sloane (2007) and Kuhfeld (2010), but these designs have largely been developed for other types of applications, and tend to involve relatively small numbers of factors and/or limited mixes of the ' x . Crossing several small OAs to obtain a larger OA can result in extremely large designs.…”

Section: Modifying Designs Constructed For Categorical Factorsmentioning

confidence: 99%

Efficient, nearly orthogonal-and-balanced, mixed designs: an effective way to conduct trade-off analyses via simulation

Vieira

Sanchez

Kienitz

et al. 2013

Journal of Simulation

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Designed experiments are powerful methodologies for gaining insights into the behaviour of complex simulation models. In recent years, many new designs have been created to address the large number of factors and complex response surfaces that often arise in simulation studies, but handling discrete-valued or qualitative factors remains problematic. We propose a framework for generating a design, of specified size, that is nearly orthogonal and nearly balanced for any mix of factor types (categorical, numerical discrete, and numerical continuous) and mix of factor levels. These new designs allow decision makers structured methods for trade-off analyses in situations that are not necessarily amenable to other methods for choosing alternatives, such as simulation optimization or ranking and selection approaches. These new designs also compare well to existing approaches for constructing custom designs for smaller experiments, and may also be of interest for exploring computer models in domains where fewer factors are involved.

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“…Our approach is able to improve the design OA (36, 9 1 2 16 ,2) -proposed by Kuhfeld and Suen (2005) -by adding one more two-level factor, one more three-level factor and one more four-level factor. Additionally, we are able to improve some of Taguchi's designs by adding: 5.…”

Section: Improved Designsmentioning

confidence: 99%