Practical likelihood analysis for Spatial Generalized Linear Mixed Models
نویسنده
چکیده
We propose a standard approach to make inference for spatial generalized linear mixed models using Laplace approximation. Based on analysis of two datasets previous analysed in literature, we compare our approach with different approaches. The first the rhizoctonia root rot dataset is an example of Binomial SGLMM and the second rongelap dataset is an example of Poisson or Negative Binomial SGLMM. Our results show that Laplace approximation provides point estimate really similar to MCMC likelihood, MCEM and modified Laplace approximation. The advantege to use Laplace approximation is to avoid tuning and convergence analysis when using based simulation method. Furthermore, using Laplace approximation we can compute the maximum loglikelihood value and realistic confidence interval based on profile likelihood. We provide R code to use our approach on the supplement material webpage. keywords: Laplace approximation, likelihood inference, spatial data, generalized linear mixed models ∗Corresponding author: [email protected], Dept. Estat́ıstica-UFPR, CP 19.081, Curitiba, PR Brazil, 81.531-990
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