From 898474828b9d5b766e1bcf04e19b898a5338c604 Mon Sep 17 00:00:00 2001 From: francois <francois.rousset@umontpellier.fr> Date: Tue, 5 Mar 2024 00:29:00 +0000 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f22b707..2e885f6 100644 --- a/README.md +++ b/README.md @@ -39,7 +39,7 @@ Also available here is the [Supplementary Appendix G](http://kimura.univ-montp2. For some substantial use of various features of `spaMM`, see e.g. the [IsoriX project](https://github.com/courtiol/IsoriX), or a story about [social dominance in hyaenas](https://doi.org/10.1038/s41559-018-0718-9), or [yet another depressing story about climate change](https://doi.org/10.1038/s41467-019-10924-4), or [the life-history of mothers of twins](https://doi.org/10.1038/s41467-022-30366-9), or a comparison of prediction by LMMs and by random-forest methods (in supplementary material of [a paper on protected area personnel](https://doi.org/10.1038/s41893-022-00970-0)), or [analyses of dyadic interactions in mandrills](https://doi.org/10.7554/eLife.79417). -Initial development drew inspiration from work by Lee and Nelder on _h_-likelihood and more elaborate approximations of likelihood (e.g. [Lee, Nelder & Pawitan](https://doi.org/10.1201/9781420011340), 2006; [Lee & Lee](http://dx.doi.org/10.1007/s11222-011-9265-9) 2012; see also [Molas and Lesaffre](http://dx.doi.org/10.1002/sim.3852), 2010). `spaMM` retains from that work several distinctive features, such as a concept of restricted likelihood applicable beyond LMMs, specific methods to fit models with non-gaussian random effects, structured dispersion models with random effects, and implementation of several variants of Laplace and PQL approximations. However, later versions have increasingly relied on additional insights. Notably, the default likelihood and restricted likelihood approximations now go beyond those discussed in these works. For ML fits, it is the same Laplace approximation as in `TMB` ([Kristensen et al., 2016](https://doi.org/10.18637/jss.v070.i05)) and packages based on `TMB`, because `TMB` and `spaMM` (with default arguments) use the observed Hessian matrix of ``joint likelihood'' where the _h_-likelihood literature only considers the expected Hessian matrix. This makes a difference for GLM families with non-canonical link, or for response families not of the GLM class. +Initial development drew inspiration from work by Lee and Nelder on _h_-likelihood and more elaborate approximations of likelihood (e.g. [Lee, Nelder & Pawitan](https://doi.org/10.1201/9781420011340), 2006; [Lee & Lee](http://dx.doi.org/10.1007/s11222-011-9265-9) 2012; see also [Molas and Lesaffre](http://dx.doi.org/10.1002/sim.3852), 2010). The latter two references, and `spaMM` itself, may actually be more widely understood as applications of Laplace approximations of likelihood than as applications of a distinctive _h_-likelihood concept. `spaMM` retains from these works several distinctive features, such as a concept of restricted likelihood applicable beyond LMMs, specific methods to fit models with non-gaussian random effects, structured dispersion models with random effects, and implementation of several variants of Laplace and PQL approximations. However, later versions have increasingly relied on additional insights. Notably, the default likelihood and restricted likelihood approximations now go beyond those discussed in these works. For ML fits, it is the same Laplace approximation as in `TMB` ([Kristensen et al., 2016](https://doi.org/10.18637/jss.v070.i05)) and packages based on `TMB`, because `TMB` and `spaMM` (with default arguments) use the observed Hessian matrix of ``joint likelihood'' where the _h_-likelihood literature only considers the expected Hessian matrix. This makes a difference for GLM families with non-canonical link, or for response families not of the GLM class. ## Credits Initial development was supported by a PEPS grant from the CNRS and University of Montpellier. -- GitLab