kulldorff(SpatialEpi)
kulldorff()所属R语言包：SpatialEpi
Kulldorff Cluster Detection Method
Kulldorff聚类检测方法
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Kulldorff spatial cluster detection method for a study region with n areas. The method constructs zones by consecutively aggregating nearest-neighboring areas until a proportion of the total study population is included. Given the observed number of cases, the likelihood of each zone is computed using either binomial or poisson likelihoods. The procedure reports the zone that is the most likely cluster and generates significance measures via Monte Carlo sampling. Further, secondary clusters, whose Monte Carlo p-values are below the α-threshold, are reported as well.
Kulldorff空间聚类检测方法与n领域的研究区域。该方法构造区的连续聚合最近的邻近区域，直到总研究人口的比例。由于所观察到的病例数，计算每个区域的可能性，可以使用二项或泊松分布的似然性。过程报告是最有可能的聚类的区域，并产生显着性的措施，通过蒙特卡罗抽样。此外，辅助聚类，蒙地卡罗的p值低于α阈值，报告。
用法----------Usage----------
kulldorff(geo, cases, population, expected.cases=NULL, pop.upper.bound, n.simulations, alpha.level, plot=TRUE)
参数----------Arguments----------
参数：geo
an n x 2 table of the (x,y)-coordinates of the area centroids
n x 2表中的（x，y）的坐标的区域的质心
参数：cases
aggregated case counts for all n areas
所有n区域聚集的病例数
参数：population
aggregated population counts for all n areas
汇总所有n区域的人口数为
参数：expected.cases
expected numbers of disease for all n areas
预计所有n区域的疾病
参数：pop.upper.bound
the upper bound on the proportion of the total population each zone can include
每个区域总人口的比例的上限，可以包括
参数：n.simulations
number of Monte Carlo samples used for significance measures
一些蒙特卡罗样本的显着性措施
参数：alpha.level
α-level threshold used to declare significance
α级阈值用于声明意义
参数：plot
flag for whether to plot histogram of Monte Carlo samples of the log-likelihood of the most likely cluster
标志是否绘制直方图蒙特卡洛样品的对数似然的最有可能的聚类
Details
详细信息----------Details----------
If expected.cases is specified to be NULL, then the binomial likelihood is used. Otherwise, a Poisson model is assumed. Typical values of n.simulations are 99, 999, 9999...
如果expected.cases被指定为NULL，然后使用二项式的可能性。否则，泊松模型的假设。典型值n.simulations是99，999，9999...
值----------Value----------
List containing:
List，其中包含：
参数：most.likely.cluster
information on the most likely cluster
最有可能的聚类的信息
参数：secondary.clusters
information on secondary clusters, if none NULL is returned
辅助聚类的信息，如果没有NULL返回
参数：type
type of likelihood
型的可能性
参数：log.lkhd
log-likelihood of each zone considered
考虑每个区域的对数似然
参数：simulated.log.lkhd
n.simulations Monte Carlo samples of the log-likelihood of the most likely cluster
n.simulations蒙特卡洛样品的最有可能的聚类中的对数似然
注意----------Note----------
The most.likely.cluster and secondary.clusters list elements are themselves lists reporting:<br><br>
most.likely.cluster和secondary.clusters的列表元素本身就是列出报告的：参考<BR>的
（作者）----------Author(s)----------
Albert Y. Kim
参考文献----------References----------
A spatial scan statistic. Communications in Statistics: Theory and Methods, 26, 1481–1496.
Statistics in Medicine, 14, 799–810.
参见----------See Also----------
pennLC, expected
pennLC，expected
实例----------Examples----------
## Load Pennsylvania Lung Cancer Data[＃加载宾夕法尼亚州的肺癌数据]
data(pennLC)
data <- pennLC$data
## Process geographical information and convert to grid[＃GEO信息处理和转换为网格]
geo <- pennLC$geo[,2:3]
geo <- latlong2grid(geo)
## Get aggregated counts of population and cases for each county[＃获取每个县的人口和情况的汇总数]
population <- tapply(data$population,data$county,sum)
cases <- tapply(data$cases,data$county,sum)
## Based on the 16 strata levels, computed expected numbers of disease[＃根据16个阶层的水平，计算预计的疾病]
n.strata <- 16
expected.cases <- expected(data$population, data$cases, n.strata)
## Set Parameters[＃设置参数]
pop.upper.bound <- 0.5
n.simulations <- 999
alpha.level <- 0.05
plot <- TRUE
## Kulldorff using Binomial likelihoods[＃Kulldorff使用二项式似然]
binomial <- kulldorff(geo, cases, population, NULL, pop.upper.bound, n.simulations, alpha.level, plot)
cluster <- binomial$most.likely.cluster$location.IDs.included
## plot[＃图]
plot(pennLC$spatial.polygon,axes=TRUE)
plot(pennLC$spatial.polygon[cluster],add=TRUE,col="red")
title("Most Likely Cluster")
## Kulldorff using Poisson likelihoods[＃Kulldorff使用泊松似然]
poisson <- kulldorff(geo, cases, population, expected.cases, pop.upper.bound, n.simulations, alpha.level, plot)
cluster <- poisson$most.likely.cluster$location.IDs.included
## plot[＃图]
plot(pennLC$spatial.polygon,axes=TRUE)
plot(pennLC$spatial.polygon[cluster],add=TRUE,col="red")
title("Most Likely Cluster Controlling for Strata")
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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-30 12:39:16
