pedigree

<p>This example illustrates how to use spaMM for quantitative genetic analyses. spaMM appears competitive in terms of speed for GLMMs with large data sets, particularly when using the PQL method, which may be a quite good approximation in such cases. For large pedigrees it may be useful to compute the inverse of the relationship matrix using some efficient ad hoc algorithm, then to provide it as argument of the fit using the <code>covStruct(list(precision=...))</code> syntax. If the precision matrix is not specified, spaMM will generally evaluate it to assess whether it should use sparse-precision methods. see <code>sparse_precision</code> for further control of this computation, on another example from quantitative genetics.</p>

Inference based on models with or without spatially-correlated random effects, multivariate responses, or non-Gaussian random effects (e.g., Beta). Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model. Both classical geostatistical models (Rousset and Ferdy 2014 <doi:10.1111/ecog.00566>), and Markov random field models on irregular grids (as considered in the 'INLA' package, <https://www.r-inla.org>), can be fitted, with distinct computational procedures exploiting the sparse matrix representations for the latter case and other autoregressive models. Laplace approximations are used for likelihood or restricted  likelihood. Penalized quasi-likelihood and other variants discussed in the h-likelihood literature (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) are also implemented.

pedigree: Fit mixed-effects models incorporating pedigrees

Description

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See Also