glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling
glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling
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DOI：
10.32614/rj-2017-066
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发表时间：
2017-12-01
期刊：
影响因子：
2.1
通讯作者：
Bolker, Benjamin M.
中科院分区：
文献类型：
--
作者：
Brooks, Mollie E.;Kristensen, Kasper;Bolker, Benjamin M.
当观测数据以需要随机影响的方式进行关联时，可以使用广义线性混合模型来分析计数数据。然而，计数数据通常是零膨胀的，包含的零比从典型误差分布中预期的要多。我们提出了一个新的包，glmTMB，并将其与其他适合零充气混合车型的R包进行了比较。Glmm TMB包适合许多类型的GLMM和扩展，包括具有连续分布响应的模型，但这里我们重点关注计数响应。对于零膨胀建模，glmm TMB比glmm ADMB、MCMCglmm和BRMS更快，并且比INLA和mgcv更灵活。Glmm TMB的一个独特功能(在符合零膨胀混合模型的软件包中)是它能够估计由平均值参数化的Conway-Maxwell-Poisson分布。总体而言，它对新用户最有吸引力的功能可能是速度、灵活性和界面与lme4的相似之处。
Count data can be analyzed using generalized linear mixed models when observations are correlated in ways that require random effects. However, count data are often zero-inflated, containing more zeros than would be expected from the typical error distributions. We present a new package, glmmTMB, and compare it to other R packages that fit zero-inflated mixed models. The glmmTMB package fits many types of GLMMs and extensions, including models with continuously distributed responses, but here we focus on count responses. glmmTMB is faster than glmmADMB, MCMCglmm, and brms, and more flexible than INLA and mgcv for zero-inflated modeling. One unique feature of glmmTMB (among packages that fit zero-inflated mixed models) is its ability to estimate the Conway-Maxwell-Poisson distribution parameterized by the mean. Overall, its most appealing features for new users may be the combination of speed, flexibility, and its interface's similarity to lme4.