bayes.cluster(SpatialEpi)
bayes.cluster()所属R语言包：SpatialEpi
Bayesian Cluster Detection
贝叶斯聚类检测
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Implementation of the Bayesian Cluster detection model of Wakefield and Kim for a study region with n areas. The prior and posterior probabilities of each of the n.zones single zones being a cluster/anti-cluster are estimated using Markov chain Monte Carlo.
实现的贝叶斯聚类检测模型韦克菲尔德和金n领域的研究区域。前和后验概率的n.zones单区聚类/防聚类，利用马尔可夫链蒙特卡罗估计。
用法----------Usage----------
bayes.cluster(E, cases, population, centroids, map, max.prop, k, shape, rate, J, pi0, n.sim.imp, n.sim.prior, n.sim.post)
参数----------Arguments----------
参数：E
vector of length n of the expected number of disease in each area
向量长度n预期数量的在每个区域中的疾病
参数：cases
vector of length n of the observed number of disease in each area
向量长度n在每个区域所观察到的一些疾病的
参数：population
vector of length n of the population in each area
向量的长度n的人口在每个领域
参数：centroids
an n x 2 table of the (x,y)-coordinates of the area centroids. The coordinate system must be grid-based
n x 2表中的（x，y）的坐标的区域的质心。基于网格的坐标系统必须
参数：map
an object of class SpatialPolygons (See SpatialPolygons-class) representing the study region
研究区域的对象类SpatialPolygons（见SpatialPolygons级）
参数：max.prop
maximum proportion of study region's population each single zone can contain
研究区的人口比例最大单区可以包含
参数：k
parameter to tune the prior probability of each single zone being a cluster/anti-cluster as a function of geographical area size
参数来调整每个单个区域的GEO区域的大小作为一个功能是一个聚类/抗聚类的先验概率
参数：shape
narrow/wide shape parameter for gamma prior on relative risk
窄/宽的形状参数的伽玛相对风险
参数：rate
narrow/wide rate parameter for gamma prior on relative risk
窄/宽参数的伽玛相对风险率
参数：J
maximum number of clusters/anti-clusters considered
聚类/反聚类认为的最大数量
参数：pi0
prior probability of no clusters/anti-clusters i.e. probability that J=0
没有联网/反聚类，即概率的先验概率，J = 0
参数：n.sim.imp
number of importance sampling iterations to estimate lambda
重要性采样迭代估计的lambda
参数：n.sim.prior
number of MCMC iterations to estimate prior probabilities associated with each single zone
MCMC迭代估计先验概率与每个单区
参数：n.sim.post
number of MCMC iterations to estimate posterior probabilities associated with each single zone
MCMC迭代估计后验概率与每个单区
值----------Value----------
List containing
List，其中包含
参数：prior.map
A sublist containing, for each area: 1) high.area the prior probability of cluster membership, 2) low.area anti-cluster membership, and 3) RR.est.area smoothed prior estimate of relative risk
子列表中，每个区域：1）high.area的先验概率聚类成员，2）low.area反聚类的成员，和3）RR.est.area平滑事先估计的相对危险度
参数：post.map
A sublist containing, for each area: 1) high.area the posterior probability of cluster membership, 2) low.area anti-cluster membership, and 3) RR.est.area smoothed posterior estimates of the relative risk
子列表中，每个区域：1）high.area的后验概率聚类成员，2）low.area反聚类的成员，和3）RR.est.area平滑后的相对危险性估计
参数：pj.y
posterior probability of j clusters/anti-clusters given y
给定y j的聚类/反聚类的后验概率
（作者）----------Author(s)----------
Albert Y. Kim
参考文献----------References----------
参见----------See Also----------
kulldorff
kulldorff
实例----------Examples----------
## Note for the NYleukemia example, 4 census tracts were completely surrounded by another [＃注意的NYleukemia的例子中，4个普查小区被完全包围的另一个]
## unique census tract; when applying the Bayesian cluster detection model in [＃独特的人口普查时，贝叶斯聚类检测模型]
## \code{\link{bayes.cluster}}, we merge them with the surrounding census tracts yielding [＃\代码\的链接{bayes.cluster}}，我们将它们合并周围的普查区产生]
## \code{n=277} areas.[＃\代码{277}区域。]
## Load data and convert coordinate system from latitude/longitude to grid[＃加载数据，并将其转换纬度/经度坐标系统向电网]
data(NYleukemia)
map <- NYleukemia$spatial.polygon
population <- NYleukemia$data$population
cases <- NYleukemia$data$cases
centroids <- latlong2grid(NYleukemia$geo[, 2:3])
## Identify the 4 census tract to be merged into their surrounding census tracts. [确定被合并到其周围的普查小区的人口普查。]
remove <- NYleukemia$surrounded
add <- NYleukemia$surrounding
## Merge population and case counts and geographical objects accordingly[＃合并人口和病例数和相应的GEO对象]
population[add] <- population[add] + population[remove]
population <- population[-remove]
cases[add] <- cases[add] + cases[remove]
cases <- cases[-remove]
map <- SpatialPolygons(map@polygons[-remove], proj4string=CRS("+proj=longlat"))
centroids <- centroids[-remove, ]
## Expected numbers[＃预计]
E <- expected(population, cases, 1)
## Set Parameters[＃设置参数]
max.prop <- 0.15
k <- 0.00005
shape <- c(2976.3, 2.31); rate <- c(2977.3, 1.31)
J <- 7
pi0 <- 0.95
n.sim.imp <- 0.5*10^3
n.sim.prior <- 0.5*10^4
n.sim.post <- 0.5*10^4
## Compute output[＃计算输出]
output <- bayes.cluster(E, cases, population, centroids, map, max.prop, k,
shape, rate, J, pi0, n.sim.imp, n.sim.prior, n.sim.post)
plotmap(output$prior.map$high.area, map)
plotmap(output$post.map$high.area, map)
plotmap(output$post.map$RR.est.area, map, log=TRUE)
barplot(output$pj.y, names.arg=0:J, xlab="j", ylab="P(j|y)")
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-9-30 12:38:40
