R on Test Science Research Document Library https://research.testscience.org/keywords/r/ Recent content in R on Test Science Research Document Library Hugo -- 0.129.0 en-us Institute for Defense Analyses Sun, 01 Jan 2023 00:00:00 +0000 Implementing Fast Flexible Space-Filling Designs in R https://research.testscience.org/post/2023-implementing-fast-flexible-space-filling-designs-in-r/ Sun, 01 Jan 2023 00:00:00 +0000 https://research.testscience.org/post/2023-implementing-fast-flexible-space-filling-designs-in-r/ Modeling and simulation (M&S) can be a useful tool when testers and evaluators need to augment the data collected during a test event. When planning M&S, testers use experimental design techniques to determine how much and which types of data to collect, and they can use space-filling designs to spread out test points across the operational space. Fast flexible space-filling designs (FFSFDs) are a type of space-filling design useful for M&S because they work well in design spaces with disallowed combinations and permit the inclusion of categorical factors.

Modeling and simulation (M&S) can be a useful tool when testers and evaluators need to augment the data collected during a test event. When planning M&S, testers use experimental design techniques to determine how much and which types of data to collect, and they can use space-filling designs to spread out test points across the operational space. Fast flexible space-filling designs (FFSFDs) are a type of space-filling design useful for M&S because they work well in design spaces with disallowed combinations and permit the inclusion of categorical factors. IDA analysts developed a function to create FFSFDs using the free statistical software R. To our knowledge, there are no R packages for creating an FFSFD that can accommodate a variety of user inputs, such as categorical factors. Moreover, users of IDA’s function can share their code to make their work reproducible.

Suggested Citation

Medlin, Rebecca M, and Christopher T Dimapasok. Space-Filling Designs in R. IDA Document NS 3000045. Alexandria, VA: Institute for Defense Analyses, 2023.

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]]> The Purpose of Mixed-Effects Models in Test and Evaluation https://research.testscience.org/post/2019-the-purpose-of-mixed-effects-models-in-test-and-evaluation/ Tue, 01 Jan 2019 00:00:00 +0000 https://research.testscience.org/post/2019-the-purpose-of-mixed-effects-models-in-test-and-evaluation/ Mixed-effects models are the standard technique for analyzing data with grouping structure. In defense testing, these models are useful because they allow us to account for correlations between observations, a feature common in many operational tests. In this article, we describe the advantages of modeling data from a mixed-effects perspective and discuss an R package—ciTools—that equips the user with easy methods for presenting results from this type of model. Suggested Citation Haman, John, Matthew Avery, and Heather Wojton. Mixed-effects models are the standard technique for analyzing data with grouping structure. In defense testing, these models are useful because they allow us to account for correlations between observations, a feature common in many operational tests. In this article, we describe the advantages of modeling data from a mixed-effects perspective and discuss an R package—ciTools—that equips the user with easy methods for presenting results from this type of model.

Suggested Citation

Haman, John, Matthew Avery, and Heather Wojton. “The Purpose of Mixed-Effects Models in Test and Evaluation.” The ITEA Journal of Test and Evaluation 40, no. 4 (2019): 249–55.

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