Spatial Analysis of Field Trials with Splines

Analysis of field trial experiments by modelling spatial trends using two-dimensional Penalised spline (P-spline) models.


o	The input argument 'data' can be an object of class 'data.table' or 'is.tibble'


o	The way of calculating the nominal dimension associated to each random term in the model has been corrected. The nominal dimension corresponds to the upper bound for the effective dimension (i.e., the maximum effective dimension a random term can achive). This nominal dimension is now calculated as \eqn{rank[X, Z_k] - rank[X]}, where \eqn{Z_k} is the design matrix of the k-th random term and \eqn{X} is the design matrix of the fixed part of the model. In most cases (but not always), the nominal dimension corresponds to the model dimension minus one, ``lost'' due to the implicit constraint that ensures the mean of the random effects to be zero. For the genotype (when random), the ratio between the effective dimension and the nominal dimension corresponds to the generalized heritability proposed by Oakey (2006). A deeper discussion can be found in Rodriguez - Alvarez et al. (2018). 

Reference manual

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1.0-9 by Maria Xose Rodriguez-Alvarez, 5 months ago

Browse source code at

Authors: Maria Xose Rodriguez-Alvarez [aut, cre] , Martin Boer [aut] , Paul Eilers [aut] , Fred van Eeuwijk [ctb]

Documentation:   PDF Manual  

GPL license

Imports stats, grDevices, graphics, fields, plot3Drgl, spam, data.table

Suggested by agridat.

See at CRAN