A note on Bayesian R-squared for generalized additive mixed models

Fuente: arXiv
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Autori principali: Jalilian, Abdollah, Vehtari, Aki, Sedda, Luigi
Natura: Preprint
Pubblicazione: 2024
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author Jalilian, Abdollah
Vehtari, Aki
Sedda, Luigi
author_facet Jalilian, Abdollah
Vehtari, Aki
Sedda, Luigi
contents We present a novel Bayesian framework to decompose the posterior predictive variance in a fitted Generalized Additive Mixed Model (GAMM) into explained and unexplained components. This decomposition enables a rigorous definition of Bayesian $R^{2}$. We show that the new definition aligns with the intuitive Bayesian $R^{2}$ proposed by Gelman, Goodrich, Gabry, and Vehtari (2019) [\emph{The American Statistician}, \textbf{73}(3), 307-309], but extends its applicability to a broader class of models. Furthermore, we introduce a partial Bayesian $R^{2}$ to quantify the contribution of individual model terms to the explained variation in the posterior predictions
format Preprint
id arxiv_https___arxiv_org_abs_2410_14002
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A note on Bayesian R-squared for generalized additive mixed models
Jalilian, Abdollah
Vehtari, Aki
Sedda, Luigi
Methodology
62F15, 62J12, 62J20
We present a novel Bayesian framework to decompose the posterior predictive variance in a fitted Generalized Additive Mixed Model (GAMM) into explained and unexplained components. This decomposition enables a rigorous definition of Bayesian $R^{2}$. We show that the new definition aligns with the intuitive Bayesian $R^{2}$ proposed by Gelman, Goodrich, Gabry, and Vehtari (2019) [\emph{The American Statistician}, \textbf{73}(3), 307-309], but extends its applicability to a broader class of models. Furthermore, we introduce a partial Bayesian $R^{2}$ to quantify the contribution of individual model terms to the explained variation in the posterior predictions
title A note on Bayesian R-squared for generalized additive mixed models
topic Methodology
62F15, 62J12, 62J20
url https://arxiv.org/abs/2410.14002