Exploring Interaction Effects in Two-Factor Studies using the hiddenf Package in R.

Authors

• Christopher Franck, Virginia Tech Department of Statistics
• Jason A. Osborne, North Carolina State University Department of Statistics

Date of this Version

8-2016

Document Type

Article

Citation

The R Journal (August 2016) 8(1); Editor: Michael Lawrence

Comments

Copyright 2016, The R Foundation. Open access material. License: CC BY 3.0 Unported

Abstract

In crossed, two-factor studies with one observation per factor-level combination, interaction effects between factors can be hard to detect and can make the choice of a suitable statistical model difficult. This article describes hiddenf, an R package that enables users to quantify and characterize a certain form of interaction in two-factor layouts. When effects of one factor (a) fall into two groups depending on the level of another factor, and (b) are constant within these groups, the interaction pattern is deemed "hidden additivity" because within groups, the effects of the two factors are additive, while between groups the factors are allowed to interact. The hiddenf software can be used to estimate, test, and report an appropriate factorial effects model corresponding to hidden additivity, which is intermediate between the unavailable full factorial model and the overly-simplistic additive model. Further, the software also conducts five statistical tests for interaction proposed between 1949 and 2014. A collection of 17 datasets is used for illustration.