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Efficient Bayesian analysis of occupancy models with logit link functions

机译：具有Logit链接功能的乘员模型的有效贝叶斯分析

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Occupancy models (Ecology, 2002; 83: 2248) were developed to infer the probability that a species under investigation occupies a site. Bayesian analysis of these models can be undertaken using statistical packages such as WinBUGS, OpenBUGS, JAGS, and more recently Stan, however, since these packages were not developed specifically to fit occupancy models, one often experiences long run times when undertaking an analysis. Bayesian spatial single‐season occupancy models can also be fit using the R package stocc. The approach assumes that the detection and occupancy regression effects are modeled using probit link functions. The use of the logistic link function, however, is algebraically more tractable and allows one to easily interpret the coefficient effects of an estimated model by using odds ratios, which is not easily done for a probit link function for models that do not include spatial random effects. We develop a Gibbs sampler to obtain posterior samples from the posterior distribution of the parameters of various occupancy models (nonspatial and spatial) when logit link functions are used to model the regression effects of the detection and occupancy processes. We apply our methods to data extracted from the 2nd Southern African Bird Atlas Project to produce a species distribution map of the Cape weaver (Ploceus capensis) and helmeted guineafowl (Numida meleagris) for South Africa. We found that the Gibbs sampling algorithm developed produces posterior samples that are identical to those obtained when using JAGS and Stan and that in certain cases the posterior chains mix much faster than those obtained when using JAGS, stocc, and Stan. Our algorithms are implemented in the R package, Rcppocc. The software is freely available and stored on GitHub ().
机译：开发了占用模型（Ecology，2002； 83：2248）以推断被调查物种占据某个地点的可能性。这些模型的贝叶斯分析可以使用WinBUGS，OpenBUGS，JAGS和最近的Stan等统计软件包进行，但是，由于这些软件包并不是专门为适应占用模型而开发的，因此在进行分析时通常会经历较长的运行时间。也可以使用R包stocc来拟合贝叶斯空间单季占用模型。该方法假定使用概率链接函数对检测和占用率回归效应进行建模。然而，逻辑链接函数的使用在代数上更易于处理，并且允许使用比值比轻松解释估计模型的系数效应，对于不包含空间随机性的模型的概率链接函数而言，这不容易做到效果。当使用logit链接函数对检测和占用过程的回归效应进行建模时，我们开发了一种Gibbs采样器，以从各种占用模型（非空间和空间）的参数的后验分布中获取后验样本。我们将我们的方法应用于从第二个“南部非洲鸟类图集”项目中提取的数据，以生成南非的开普韦弗（Ploceus capensis）和带头盔的豚鼠（Numida meleagris）的物种分布图。我们发现，开发的Gibbs采样算法产生的后验样本与使用JAGS和Stan时获得的样本相同，并且在某些情况下，后链的混合速度比使用JAGS，stocc和Stan时所获得的更快。我们的算法在R包Rcppocc中实现。该软件可免费获得，并存储在GitHub（）上。

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