Tensor Cubic Smoothing Splines in Designed Experiments Requiring Residual Modelling

Arūnas P. Verbyla (), Joanne Faveri, John D. Wilkie and Tom Lewis
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Arūnas P. Verbyla: CSIRO
Joanne Faveri: CSIRO
John D. Wilkie: Queensland Department of Agriculture and Fisheries
Tom Lewis: University of the Sunshine Coast

Journal of Agricultural, Biological and Environmental Statistics, 2018, vol. 23, issue 4, No 4, 478-508

Abstract: Abstract Modelling response surfaces using tensor cubic smoothing splines is presented for three designed experiments. The aim is to show how the analyses can be carried out using the asreml software in the R environment, and details of the analyses including the code to do so are presented in a tutorial style. The experiments were all run over time and involve an explanatory quantitative treatment variable; one experiment is a field trial which has a spatial component and involves an additional treatment. Thus, in addition to the response surface for the time by explanatory variable, modelling of temporal and, for the third experiment, of temporal and spatial effects at the residual level is required. A linear mixed model is used for analysis, and a mixed model representation of the tensor cubic smoothing spline is described and seamlessly incorporated in the full linear mixed model. The analyses show the flexibility and capacity of asreml for complex modelling. Supplementary materials accompanying this paper appear online.

Keywords: asreml; Cubic smoothing spline; Mixed models; Spatial variation; Temporal variation; Tensor spline (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s13253-018-0334-9

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