Virtual StatsPD@Waite meeting

Every month, the professional development meetings of statisticians and data scientists at Waite, known as StatsPD@Waite, bring together specialists in various aspects of data sciences in agriculture from Waite, Roseworthy and Adelaide.

Please join us for the next StatsPD@Waite seminar where Chris Brien, Australian Plant Phenomics Facility, University of Adelaide and UniSA STEM, University of South Australia will present on Exposing the confounding in experimental designs to understand and evaluate them, and formulating linear mixed models for analyzing the data from a designed experiment.

Also please note that the StatsPD@Waite meetings are recorded. If you have a question to the speaker but had rather not be recorded, please send your question via chat during the meeting and it will be asked on your behalf.

Please email Beata Sznajder with questions or for details of the Zoom meeting.

Title: Exposing the confounding in experimental designs to understand and evaluate them, and formulating linear mixed models for analyzing the data from a designed experiment

Presenter: Chris Brien, Australian Plant Phenomics Facility, University of Adelaide and UniSA STEM, University of South Australia

In their paper with the same title as this abstract, Brien, C. J., Sermarini, R. A., & Dem´etrio, C. G. B. (2023, https://doi.org/10.1002/bimj.202200284) note that comparative experiments involve the allocation of treatments to units, ideally by randomization. This necessarily confounds treatment information with unit information, which they distinguish from the other forms of information blending, in particular aliasing and marginality. They outline a factor-allocation paradigm for describing experimental designs with the aim of (i) exhibiting the confounding in a design, using analysis-of-variance-like tables, so as to understand and evaluate the design and (ii) formulating a linear mixed model based on the factor allocation that the design involves. The approach exhibits the dispersal of treatments information between units sources, allows designers a choice in the strategy that they adopt for including block-treatment interactions, clarifies differences between experiments, accommodates systematic allocation of factors and provides a consolidated analysis of nonorthogonal designs. It provides insights into the process of designing experiments and issues that commonly arise with designs. The paradigm has pedagogical advantages and is implemented using the R package dae (Brien, 2023, http://CRAN.R-project.org/package=dae).

The talk will present some of their examples to illustrate the use of the factor-allocation paradigm and its advantages.