nncorr(spatstat)
nncorr()所属R语言包：spatstat
Nearest-Neighbour Correlation Indices of Marked Point Pattern
最近邻居相关指数的标点模式
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Computes nearest-neighbour correlation indices of a marked point pattern, including the nearest-neighbour mark product index (default case of nncorr), the nearest-neighbour mark index (nnmean), and the nearest-neighbour variogram index (nnvario).
计算最近邻相关指数的一个显着的点模式，包括最近的邻居标志产品指数（nncorr），默认情况下，最近的邻居标记指数（nnmean），和最近的邻居变异函数索引（nnvario）。
用法----------Usage----------
nncorr(X,
f = function(m1, m2) { m1 * m2 },
...,
use = "all.obs", method = c("pearson", "kendall", "spearman"),
denominator=NULL)
nnmean(X)
nnvario(X)
参数----------Arguments----------
参数：X
The observed point pattern. An object of class "ppp".
观测点的模式。对象的类"ppp"。
参数：f
Function f used in the definition of the nearest neighbour correlation. There is a sensible default that depends on the type of marks of X.
功能f的定义中使用的最近邻域相关。有一个合理的默认依赖于类型的标志X。
参数：...
Extra arguments passed to f.
额外的参数传递给f。
参数：use,method
Arguments passed to the standard correlation function cor.
参数传递给标准相关功能cor。
参数：denominator
Internal use only.
仅供内部使用。
Details
详细信息----------Details----------
The nearest neighbour correlation index nbar of a marked point process X is a number measuring the dependence between the mark of a typical point and the mark of its nearest neighbour.
最近的邻居相关指数nbar的标记点过程中X是一个数字测量一个典型的点的标志和商标是其近邻之间的依赖关系。
The command nncorr computes the nearest neighbour correlation index based on any test function f provided by the user. The default behaviour of nncorr is to compute the nearest neighbour mark product index. The commands nnmean and nnvario are convenient abbreviations for other special choices of f.
命令nncorr计算近邻相关指数，基于任何测试功能f由用户提供的。 nncorr的默认行为是最近的邻居标志产品指数计算。的命令nnmean和nnvario是方便其他特殊选择的f的缩写。
In the default case, nncorr(X) computes three different versions of the nearest-neighbour correlation index: the unnormalised, normalised, and classical correlations.
在默认情况下，nncorr(X)计算最近的邻居相关指数的三个不同的版本：unnormalised，规范化，和古典的相关性。
The unnormalised nearest neighbour correlation (Stoyan and Stoyan, 1994, section 14.7) is defined as
unnormalised近邻的相关性（斯托扬和斯托扬，1994，第14.7节）被定义为
where E[] denotes mean value, M is the mark attached to a typical point of the point process, and M* is the mark attached to its nearest neighbour (i.e. the nearest other point of the point process).
E[]表示平均值，M的标志是一个典型的点的点处理，和M*的标志是其最近的邻居（即最近的其他点的点过程）。
Here f is any function f(m1,m2) with two arguments which are possible marks of the pattern, and which returns a nonnegative real value. Common choices of f are: for continuous real-valued marks,
这是f任何功能f(m1,m2)有两个参数，这是可能的商标的图案，并返回一个非负的实际价值。 f的共同选择是：连续的实值标记，
for discrete marks (multitype point patterns),
为的离散标记（多类型，点模式），
and for marks taking values in [0,2 * pi),
和标记值[0,2 * pi)，
For example, in the second case, the unnormalised nearest neighbour correlation nbar[f] equals the proportion of points in the pattern which have the same mark as their nearest neighbour.
例如，在第二种情况下，unnormalised近邻相关nbar[f]等于在模式中的点具有相同的符号，其近邻的比例。
Note that nbar[f] is not a “correlation” in the usual statistical sense. It can take values greater than 1.
请注意，nbar[f]是不是一个在通常的统计意义上的“相关性”。它可以采取大于1的值。
We can define a normalised nearest neighbour correlation by
我们可以定义一个标准化的最近的邻居相关
where again M is the mark attached to a typical point, M* is the mark attached to its nearest neighbour, and M' is an independent copy of M with the same distribution. This normalisation is also not a “correlation” in the usual statistical sense, but is normalised so that the value 1 suggests “lack of correlation”: if the marks attached to the points of X are independent and identically distributed, then mbar[f] = 1. The interpretation of values larger or smaller than 1 depends on the choice of function f.
在那里再次M的标志是一个典型的点，M*的标志是连接到其最近的邻居，和M'是同分布的独立副本，M 。标准化是不是在平时的统计意义上的“相关性”，但规范化，这样的值为1的建议“缺乏相关性”：如果该商标X是独立同分布的点的，然后mbar[f] = 1。大于或小于1的值的解释依赖于选择的功能f。
Finally if the marks of X are real numbers, we can also compute the classical correlation, that is, the correlation coefficient of the two random variables M and M*. The classical correlation has a value between -1 and 1. Values close to -1 or 1 indicate strong dependence between the marks.
最后，如果标记X是实数，我们也可以计算古典的相关性，即，两个随机变量M和M*的相关系数。的经典相关之间的一个值-1和1。值接近-1或1表示强烈的依赖之间的商标。
In the default case where f is not given, nncorr(X) computes
在默认情况下f不给，nncorr(X)计算
If the marks of X are real numbers, the unnormalised and normalised versions of the nearest-neighbour product index E[M * M*], and the classical correlation between M and M*.
如果马克X是实数，最近邻的产品指标E[M * M*]unnormalised和规范化的版本，与古典之间的相关性M和M*。
If the marks of X are factor valued, the unnormalised and normalised versions of the nearest-neighbour equality index P[M = M*].
如果的痕迹X都是因素重视，最近的邻居平等指数P[M = M*]unnormalised和规范化的版本。
The wrapper functions nnmean and nnvario compute the correlation indices for two special choices of the function f(m1,m2).
包装功能nnmean和nnvario计算的相关指数两个特殊选择的功能f(m1,m2)。
nnmean computes the correlation indices for f(m1,m2) = m1. The unnormalised index is simply the mean value of the mark of the neighbour of a typical point, E[M*], while the normalised index is E[M*]/E[M], the ratio of the mean mark of the neighbour of a typical point to the mean mark of a typical point.
nnmean计算相关指数f(m1,m2) = m1的。 unnormalised索引是简单的邻居的一个典型的点的标记的平均值，E[M*]，而归一化指数是E[M*]/E[M]，邻居的一个典型的点的平均标记比一个典型的点的平均马克。
nnvario computes the correlation indices for f(m1,m2) = (1/2) * (m1-m2)^2.
nnvario计算相关指数f(m1,m2) = (1/2) * (m1-m2)^2的。
The argument X must be a point pattern (object of class "ppp") and must be a marked point pattern. (The marks may be a data frame, containing several columns of mark variables; each column is treated separately.)
参数X必须是点模式（类的对象"ppp"），而且必须是一个显着的点模式。 （这些标记可以是一个数据框，包含多个列的标记变量的每一列都被分开处理。）
If the argument f is given, it must be a function, accepting two arguments m1 and m2 which are vectors of equal length containing mark values (of the same type as the marks of X). It must return a vector of numeric values of the same length as m1 and m2. The values must be non-negative.
如果参数f给出，它必须是一个函数，接受两个参数m1和m2是含有相等的长度的标记值（作为标记的相同类型的向量 X“）。它必须返回一个的矢量具有相同的长度的数值作为m1和m2。的值必须为非负数。
The arguments use and method control the calculation of the classical correlation using cor, as explained in the help file for cor.
的参数use和method控制的经典相关的计算cor，在帮助文件中的解释cor。
Other arguments may be passed to f through the ... argument.
其他参数可以传递给f通过...说法。
This algorithm assumes that X can be treated as a realisation of a stationary (spatially homogeneous) random spatial point process in the plane, observed through a bounded window. The window (which is specified in X as X$window) may have arbitrary shape. Biases due to edge effects are treated using the "border method" edge correction.
此算法假定X可以被视为一个实现了一个固定的（的空间均匀）随机空间点在飞机上，观察到有界的窗口。窗口（中指定XX$window的）可以有任意形状的。治疗使用的“边界法”边缘修正边缘效应产生的偏差。
值----------Value----------
Labelled vector of length 2 or 3 containing the unnormalised and normalised nearest neighbour correlations, and the classical correlation if appropriate. Alternatively a matrix with 2 or 3 rows, containing this information for each mark variable.
中标为矢量的长度为2或3个含有unnormalised和归一化的近邻的相关性，并且如果合适的古典相关。另外，与2或3行的矩阵，包含该信息的每个标记变量。
（作者）----------Author(s)----------
Adrian Baddeley
<a href="mailto:Adrian.Baddeley@csiro.au">Adrian.Baddeley@csiro.au</a>
<a href="http://www.maths.uwa.edu.au/~adrian/">http://www.maths.uwa.edu.au/~adrian/</a>
and Rolf Turner
<a href="mailto:r.turner@auckland.ac.nz">r.turner@auckland.ac.nz</a>
参考文献----------References----------
Fractals, random shapes and point fields: methods of geometrical statistics. John Wiley and Sons.
实例----------Examples----------
data(finpines)
nncorr(finpines)
# heights of neighbouring trees are slightly negatively correlated[相邻的树的高度是稍呈负相关]
data(amacrine)
nncorr(amacrine)
# neighbouring cells are usually of different type[相邻小区通常是不同类型的]
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。
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发表于 2012-9-30 13:49:58
