nnclean(spatstat)
nnclean()所属R语言包：spatstat
Nearest Neighbour Clutter Removal
最近邻居杂波去除
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Detect features in a 2D or 3D spatial point pattern using nearest neighbour clutter removal.
检测功能，在二维或三维空间点格局最近的邻居的混乱去除。
用法----------Usage----------
nnclean(X, k, ...)
## S3 method for class 'ppp'
nnclean(X, k, ...,
edge.correct = FALSE, wrap = 0.1,
convergence = 0.001, plothist = FALSE,
verbose = TRUE, maxit = 50)
## S3 method for class 'pp3'
nnclean(X, k, ...,
convergence = 0.001, plothist = FALSE,
verbose = TRUE, maxit = 50)
参数----------Arguments----------
参数：X
A two-dimensional spatial point pattern (object of class "ppp") or a three-dimensional point pattern (object of class "pp3").
两维空间点模式（类的对象"ppp"）或三维点模式（类的对象"pp3"）。
参数：k
Degree of neighbour: k=1 means nearest neighbour, k=2 means second nearest, etc.
学位的邻居：k=1是指最近的邻居，k=2是指第二个最近的，等等。
参数：...
Ignored.
忽略。
参数：edge.correct
Logical flag specifying whether periodic edge correction should be performed (only implemented in 2 dimensions).
逻辑标志，指定是否应进行定期的边缘校正（只执行了2维）。
参数：wrap
Numeric value specifying the relative size of the margin in which data will be replicated for the periodic edge correction (if edge.correct=TRUE). A fraction of window width and window height.
数值指定的保证金，其数据将被复制的定期边缘校正的相对大小（如果edge.correct=TRUE“）。一小部分的窗宽，窗高。
参数：convergence
Tolerance threshold for testing convergence of EM algorithm.
EM算法的收敛性测试的耐受阈值。
参数：maxit
Maximum number of iterations for EM algorithm.
最大EM算法的迭代次数。
参数：plothist
Logical flag specifying whether to plot a diagnostic histogram of the nearest neighbour distances and the fitted distribution.
逻辑的标志，指定是否绘制诊断的最近邻距离和拟合分布直方图。
参数：verbose
Logical flag specifying whether to print progress reports.
逻辑的标志，指定是否打印进度报告。
Details
详细信息----------Details----------
Byers and Raftery (1998) developed a technique for recognising features in a spatial point pattern in the presence of random clutter.
Byers和拉夫特里（1998）开发了一种技术，用于识别的随机杂波的存在于一个空间点模式中的功能。
For each point in the pattern, the distance to the kth nearest neighbour is computed. Then the E-M algorithm is used to fit a mixture distribution to the nearest neighbour distances. The mixture components represent the feature and the clutter. The mixture model can be used to classify each point as belong to one or other component.
如果在模式中的每一个点，计算的距离的的k个近邻。然后使用EM算法适合的混合物分布的近邻距离。的混合物组成部分的功能和杂波。混合模型可以被用来分类每个点作为属于一个或其它成分。
The function nnclean is generic, with methods for two-dimensional point patterns (class "ppp") and three-dimensional point patterns (class "pp3") currently implemented.
的功能nnclean是通用的，与二维点模式的方法（类"ppp"）和三维点模式（类"pp3"）目前实施。
The result is a point pattern (2D or 3D) with two additional columns of marks:
其结果是与另外两列的标记的点图案（2D或3D）：
A factor, with levels "noise" and "feature", indicating the maximum likelihood classification of each point.
的一个因素，同级别"noise"和"feature"，表示每个点的最大似然分类。
Numeric vector giving the estimated probabilities that each point belongs to a feature.
数字矢量估计给每个点的概率属于一个功能。
值----------Value----------
An object of the same kind as X, obtained by attaching marks to the points of X.
相同种类的一个目的X，得到的附加标记的点X。
（作者）----------Author(s)----------
Original by Simon Byers and Adrian Raftery.
Adapted for <span class="pkg">spatstat</span> by Adrian Baddeley.
参考文献----------References----------
Nearest-neighbour clutter removal for estimating features in spatial point processes. Journal of the American Statistical Association 93, 577–584.
参见----------See Also----------
nndist, split.ppp, cut.ppp
nndist，split.ppp，cut.ppp
实例----------Examples----------
data(shapley)
X <- nnclean(shapley, k=17)
plot(X, chars=c(".", "+"), cols=1:2)
Y <- split(X)
plot(Y, chars="+", cex=0.5)
marks(X) <- marks(X)$prob
plot(cut(X, breaks=3), chars=c(".", "+", "+"), cols=1:3)
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 13:49:52
