PrevMap: An R Package for Prevalence Mapping
PrevMap: An R Package for Prevalence Mapping
复制标题
DOI：
10.18637/jss.v078.i08
复制
发表时间：
2017-06-01
影响因子：
5.8
通讯作者：
Diggle, Peter J.
中科院分区：
文献类型：
--
作者：
Giorgi, Emanuele;Diggle, Peter J.
在本文中，我们介绍了一个新的R包，PrevMap，用于空间参考流行数据的分析，包括经典的最大似然方法和贝叶斯方法，用于参数估计和插件或贝叶斯预测。更具体地说，新的程序包基于两种不同的方法实现了二项数据的地质统计模型的拟合。第一种方法使用具有Logistic连接函数、二项误差分布和高斯空间过程的广义线性混合模型作为线性预报器的随机分量。一种更简单但近似的替代方法是将线性高斯模型与经验Logit变换后的数据进行拟合。该包还包括对高斯空间过程实施基于卷积的低阶近似，以实现对大型空间数据集的计算效率分析。我们通过分析喀麦隆和尼日利亚的LOA流行数据来说明该套餐的使用。我们使用一个模拟的地质统计学数据集来说明低阶近似的使用。
In this paper we introduce a new R package, PrevMap, for the analysis of spatially referenced prevalence data, including both classical maximum likelihood and Bayesian approaches to parameter estimation and plug-in or Bayesian prediction. More specifically, the new package implements fitting of geostatistical models for binomial data, based on two distinct approaches. The first approach uses a generalized linear mixed model with logistic link function, binomial error distribution and a Gaussian spatial process as a stochastic component in the linear predictor. A simpler, but approximate, alternative approach consists of fitting a linear Gaussian model to empirical-logit-transformed data. The package also includes implementations of convolution-based low-rank approximations to the Gaussian spatial process to enable computationally efficient analysis of large spatial datasets. We illustrate the use of the package through the analysis of Loa loa prevalence data from Cameroon and Nigeria. We illustrate the use of the low rank approximation using a simulated geostatistical dataset.