Automated mixed ANOVA modeling of sensory and consumer data

Mixed effects models have become increasingly prominent in sensory and consumer science. Still applying such models may be challenging for a sensory practitioner due the challenges associated with the choosing the random effects, selecting an appropriate model, interpreting the results. In this paper we introduce an approach for automated mixed ANOVA/ANCOVA modeling together with the open source R package lmerTest developed by the authors that can perform automated complex mixed-effects modeling.

The package can in an automated way investigate and incorporate the necessary random-effects by sequentially removing non-significant random terms in the mixed model, and similarly test and remove fixed effects. Tables and figures provide an overview of the structure and present post hoc analysis. With this approach, complex error structures can be investigated, identified and incorporated whenever necessary.

The package provides type-3 ANOVA output with degrees of freedom corrected F-tests for fixed-effects, which makes the package unique in open source implementations of mixed models. The approach together with the user-friendliness of the package allow to analyze a broad range of mixed effects models in a fast and efficient way.

The benefits of the approach and the package are illustrated on four data sets coming from consumer/sensory studies.