Login | Request Account (DAF staff only)

Covariance Clustering: Modelling Covariance in Designed Experiments When the Number of Variables is Greater than Experimental Units

Share this record

Add to FacebookAdd to LinkedinAdd to XAdd to WechatAdd to Microsoft_teamsAdd to WhatsappAdd to Any

Export this record

View Altmetrics

Forknall, C. R., Verbyla, A. P., Nazarathy, Y., Yousif, A., Osama, S., Jones, S. H., Kerr, E., Schulz, B. L., Fox, G. P. and Kelly, A. M. (2024) Covariance Clustering: Modelling Covariance in Designed Experiments When the Number of Variables is Greater than Experimental Units. Journal of Agricultural, Biological and Environmental Statistics, 29 . pp. 232-256. ISSN 1537-2693


Article Link: https://doi.org/10.1007/s13253-023-00574-x


The size and complexity of datasets resulting from comparative research experiments in the agricultural domain is constantly increasing. Often the number of variables measured in an experiment exceeds the number of experimental units composing the experiment. When there is a necessity to model the covariance relationships that exist between variables in these experiments, estimation difficulties can arise due to the resulting covariance structure being of reduced rank. A statistical method, based in a linear mixed model framework, is presented for the analysis of designed experiments where datasets are characterised by a greater number of variables than experimental units, and for which the modelling of complex covariance structures between variables is desired. Aided by a clustering algorithm, the method enables the estimation of covariance through the introduction of covariance clusters as random effects into the modelling framework, providing an extension of the traditional variance components model for building covariance structures. The method was applied to a multi-phase mass spectrometry-based proteomics experiment, with the aim of exploring changes in the proteome of barley grain over time during the malting process. The modelling approach provides a new linear mixed model-based method for the estimation of covariance structures between variables measured from designed experiments, when there are a small number of experimental units, or observations, informing covariance parameter estimates.

Item Type:Article
Corporate Creators:Department of Agriculture and Fisheries, Queensland
Business groups:Crop and Food Science
Keywords:Barley k-Means clustering Linear mixed model Mass spectrometry Multi-phase design Proteomics
Subjects:Science > Statistics
Science > Statistics > Experimental design
Science > Statistics > Statistical data analysis
Science > Statistics > Simulation modelling
Science > Statistics > Statistical software
Live Archive:17 Oct 2023 00:40
Last Modified:11 Jul 2024 03:43

Repository Staff Only: item control page


Downloads per month over past year

View more statistics