Jcross(spatstat)
Jcross()所属R语言包：spatstat
Multitype J Function (i-to-j)
多类型，的J函数（I--J）
译者：生物统计家园网 机器人LoveR
描述----------Description----------
For a multitype point pattern, estimate the multitype J function summarising the interpoint dependence between points of type i and of type j.
对于多类型的点模式，估计的多类型J函数总结INTERPOINT类型i和j类型的点之间的依赖关系。
用法----------Usage----------
Jcross(X, i, j, eps=NULL, r=NULL, breaks=NULL, ..., correction=NULL)
参数----------Arguments----------
参数：X
The observed point pattern, from which an estimate of the multitype J function Jij(r) will be computed. It must be a multitype point pattern (a marked point pattern whose marks are a factor). See under Details.
观测点的模式，从估计的多类型J函数Jij(r)将被计算。它必须是一个多类型的点模式（一个标记点图案的标记是一个因素）。请参阅“详细信息”下。
参数：i
The type (mark value) of the points in X from which distances are measured. A character string (or something that will be converted to a character string). Defaults to the first level of marks(X).
X距离的测量点的类型（标记值）。一个字符串（或东西都将被转换为一个字符串）。默认的第一级marks(X)。
参数：j
The type (mark value) of the points in X to which distances are measured. A character string (or something that will be converted to a character string). Defaults to the second level of marks(X).
距离的测量点X的类型（标记值）。一个字符串（或东西都将被转换为一个字符串）。默认的第二个层次的marks(X)。
参数：eps
A positive number. The resolution of the discrete approximation to Euclidean distance (see below). There is a sensible default.
一个正数。欧几里德距离（见下文）的分辨率的离散逼近。有一个合理的默认。
参数：r
Optional. Numeric vector. The values of the argument r at which the function Jij(r) should be evaluated. There is a sensible default. First-time users are strongly advised not to specify this argument. See below for important conditions on r.
可选。数字矢量。的参数的值r的功能Jij(r)应进行评估。有一个合理的默认。我们强烈建议用户第一次不指定此参数。请参阅下面的重要条件r。
参数：breaks
An alternative to the argument r. Not normally invoked by the user. See the Details section.
替代到的参数r。通常不是由用户调用。查看详细信息“一节。
参数：...
Ignored.
忽略。
参数：correction
Optional. Character string specifying the edge correction(s) to be used. Options are "none", "rs", "km", "Hanisch" and "best".
可选。字符的字符串指定的边缘校正（s）到被使用。选项"none"，"rs"，"km"，"Hanisch"和"best"。
Details
详细信息----------Details----------
This function Jcross and its companions Jdot and Jmulti are generalisations of the function Jest to multitype point patterns.
此功能Jcross和它的同伴Jdot和Jmulti的功能Jest多类型，点模式的概括。
A multitype point pattern is a spatial pattern of points classified into a finite number of possible “colours” or “types”. In the spatstat package, a multitype pattern is represented as a single point pattern object in which the points carry marks, and the mark value attached to each point determines the type of that point.
一个多类型的模式是一个空间格局分为有限数量的可能的“颜色”或“类型”的点。在spatstat包，多类型图案表示作为一个单一的点图案在该点进行标记的对象，并连接到每个点的标记值确定该点的类型。
The argument X must be a point pattern (object of class "ppp") or any data that are acceptable to as.ppp. It must be a marked point pattern, and the mark vector X$marks must be a factor. The argument i will be interpreted as a level of the factor X$marks. (Warning: this means that an integer value i=3 will be interpreted as the number 3, not the 3rd smallest level).
参数X必须是点模式（类的对象"ppp"）或任何数据到as.ppp是可以接受的。它必须是一个显着的点图案，并标记矢量X$marks必须是一个因素。参数i将被解释为一个水平的因素X$marks。 （警告：这意味着一个整数值i=3将被解释为3号，而不是第三最小的水平）。
The “type i to type j” multitype J function of a stationary multitype point process X was introduced by Van lieshout and Baddeley (1999). It is defined by
“型i键入j”多类型J一个固定的多类型的功能点过程X是由范LIESHOUT和巴德利（1999年）。它被定义为
where Gij(r) is the distribution function of the distance from a type i point to the nearest point of type j, and Fj(r) is the distribution function of the distance from a fixed point in space to the nearest point of type j in the pattern.
Gij(r)是分布函数类型i点j和Fj(r)是分布函数的距离从一个固定的类型的最近点的距离空间中的点的最近点的类型j的格局。
An estimate of Jij(r) is a useful summary statistic in exploratory data analysis of a multitype point pattern. If the subprocess of type i points is independent of the subprocess of points of type j, then Jij(r) = 1. Hence deviations of the empirical estimate of Jij from the value 1 may suggest dependence between types.
Jij(r)的估计是一个多类型模式的探索性数据分析的一个有用的摘要统计。如果子类型i点是独立的子类型j，那么Jij(r) = 1点。因此，偏差的经验估计Jij值1可能建议类型之间的依赖关系。
This algorithm estimates Jij(r) from the point pattern X. It 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$window) may have arbitrary shape. Biases due to edge effects are treated in the same manner as in Jest, using the Kaplan-Meier and border corrections. The main work is done by Gmulti and Fest.
该算法估计Jij(r)点模式X。它假定X可以被视为一个实现了一个固定的（的空间均匀）随机空间点在飞机上，观察到一个有限的窗口。窗口（中指定XX$window的）可以有任意形状的。边缘效应产生的偏差，在相同的方式处理在Jest，采用Kaplan-Meier和边界更正。本文的主要工作是通过Gmulti和Fest。
The argument r is the vector of values for the distance r at which Jij(r) should be evaluated. The values of r must be increasing nonnegative numbers and the maximum r value must exceed the radius of the largest disc contained in the window.
参数r是向量的值的距离r，Jij(r)应该进行评估。 r的值，必须增加非负数和最大r值必须超过包含在窗口中的最大的光盘的半径。
值----------Value----------
An object of class "fv" (see fv.object).
类的一个对象"fv"（见fv.object）。
Essentially a data frame containing six numeric columns
本质上是一个数据框包含6个数字列
参数：J
the recommended estimator of Jij(r), currently the Kaplan-Meier estimator.
建议Jij(r)，目前的Kaplan-Meier估计估计。
参数：r
the values of the argument r at which the function Jij(r) has been estimated
的参数的值的r在哪些函数Jij(r)已估计
参数：km
the Kaplan-Meier estimator of Jij(r)
Kaplan-Meier法估计Jij(r)
参数：rs
the “reduced sample” or “border correction” estimator of Jij(r)
“减少样品”或“边界校正”估计Jij(r)
参数：han
the Hanisch-style estimator of Jij(r)
Hanisch式估计Jij(r)
参数：un
the “uncorrected” estimator of Jij(r) formed by taking the ratio of uncorrected empirical estimators of 1 - Gij(r) and 1 - Fj(r), see Gdot and Fest.
“裸眼”估计Jij(r)形成的裸经验估计1 - Gij(r)和1 - Fj(r)，Gdot和Fest之比。
参数：theo
the theoretical value of Jij(r) for a marked Poisson process, namely 1.
的理论值Jij(r)显着的泊松过程，即1。
The result also has two attributes "G" and "F" which are respectively the outputs of Gcross and Fest for the point pattern.
结果也有两个属性"G"和"F"分别Gcross和Fest点模式的输出。
警告----------Warnings----------
The arguments i and j are always interpreted as levels of the factor X$marks. They are converted to character strings if they are not already character strings. The value i=1 does not refer to the first level of the factor.
的参数i和j总是被解释为水平的因素X$marks。它们被转换为字符串，如果他们不已经字符串。值i=1不是指第一级的因素。
（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----------
A nonparametric measure of spatial interaction in point patterns. Statistica Neerlandica 50, 344–361.
Indices of dependence between types in multivariate point patterns. Scandinavian Journal of Statistics 26, 511–532.
参见----------See Also----------
Jdot, Jest, Jmulti
Jdot，Jest，Jmulti
实例----------Examples----------
# Lansing woods data: 6 types of trees[蓝星树林数据：6种树木]
data(lansing)
Jhm <- Jcross(lansing, "hickory", "maple")
# diagnostic plot for independence between hickories and maples[胡桃树和枫树之间的独立性诊断图]
plot(Jhm)
# synthetic example with two types "a" and "b"[合成例中使用两种类型的“a”和“b”的]
pp <- runifpoint(30) %mark% factor(sample(c("a","b"), 30, replace=TRUE))
J <- Jcross(pp)
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 13:38:07
