R语言 SpatialEpi包 besag.newell()函数中文帮助文档(中英文对照)

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besag.newell(SpatialEpi)
besag.newell()所属R语言包：SpatialEpi

                                         Besag-Newell Cluster Detection Method
                                         Besag，的纽维尔聚类检测方法

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Besag-Newell cluster detection method.  There are differences with the original paper and our implementation:
Besag纽维尔聚类的检测方法。与原来的纸和实施有差异：

we base our analysis on k cases, rather than k other cases as prescribed in the paper.
我们的分析k的情况下，而不是k其他情况下的文件规定。

we do not subtract 1 from the accumulated numbers of other cases and accumulated numbers of others at risk, as was prescribed in the paper to discount selection bias
我们不减去1的累计数的其他情况下的风险和积累的其他规定的文件折现选择偏倚

M is the total number of areas included, not the number of additional areas included.  i.e. M starts at 1, not 0.
M是总人数的领域包括，不包括其他领域的数量。即M开始于1，而不是0。

p-values are not based on the original value of k, rather the actual number of cases observed until we view k or more cases.  Ex:  if k = 10, but as we consider neighbors we encounter 1, 2, 9 then 12 cases, we base our p-values on k=12
p值没有根据的原始值k，而实际数量的情况下观察，直到我们认为k或更多的情况下。例如：如果k = 10，但我们认为我们遇到的邻居1，2，然后在12例中，我们的基础，我们的p值k=12

we do not provide a Monte-Carlo simulated R:  the number of tests that attain significance at a fixed level α
我们不提供一个蒙特卡罗模拟R：数达到显着性的测试，在一个固定的水平α

The first two and last differences are because we view the testing on an area-by-area level, rather than a case-by-case level.
前两个和最后的差异，是因为我们认为一个区域的区域级别的测试，而不是一个水平的情况下。


用法----------Usage----------


        besag.newell(geo, population, cases, expected.cases=NULL, k, alpha.level)



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：k
number of cases to consider
数的情况下，考虑


参数：alpha.level
α-level threshold used to declare significance
α级阈值用于声明意义


Details

详细信息----------Details----------

For the population and cases tables, the rows are bunched by areas first, and then for each area, the counts for each strata are listed.  It is important that the tables are balanced:  the strata information are in the same order for each area, and counts for each area/strata combination appear exactly once (even if zero).
population和cases表，行揉成区域首，然后每个区域，每个阶层的计数列出。重要的是，表中的平衡：地层的信息是为每个区域中的相同的顺序，并且为每个区域/岩层组合计数出现一次（即使是零）。


值----------Value----------

List containing
List，其中包含


参数：clusters
information on all clusters that are α-level significant, in decreasing order of the p-value
是α-水平显着减少顺序p价值的，所有聚类上的信息


参数：p.values
for each of the n areas, p-values of each cluster of size at least k
n领域，为每个p-值的每个聚类的大小至少k


参数：m.values
for each of the n areas, the number of areas need to observe at least k cases
n领域，为每个需要观察的区域的数量至少k例


参数：observed.k.values
based on m.values, the actual number of cases used to compute the p-values
基于m.values，实际数量的情况下，用于计算的p值


注意----------Note----------

The clusters list elements are themselves lists reporting:<br><br>
列表中的元素是自己clusters列出报告：<BR> <BR>的


（作者）----------Author(s)----------


Albert Y. Kim



参考文献----------References----------

The Detection of Clusters in Rare Diseases Journal of the Royal Statistical Society. Series A (Statistics in Society), 154, 143&ndash;155

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]
k <- 1250
alpha.level <- 0.05

# not controlling for stratas[不控制拉塔斯]
results <- besag.newell(geo, population, cases, expected.cases=NULL, k, alpha.level)

# controlling for stratas[控制拉塔斯]
results <- besag.newell(geo, population, cases, expected.cases, k, alpha.level)

转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


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