Jdot(spatstat)
Jdot()所属R语言包：spatstat
Multitype J Function (i-to-any)
多类型，的J函数（我对任何）
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 the type i points and the points of any type.
对于多类型的点模式，估计的多类型J函数总结INTERPOINT之间的类型i点和点的任何类型的依赖。
用法----------Usage----------
Jdot(X, i, 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 Ji.(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Ji.(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：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
numeric vector. The values of the argument r at which the function Ji.(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.
数字矢量。的参数的值r的功能Ji.(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 Jdot and its companions Jcross and Jmulti are generalisations of the function Jest to multitype point patterns.
此功能Jdot和它的同伴Jcross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将被解释为数字3，而不是第三最小的水平。）
The “type i to any type” multitype J function of a stationary multitype point process X was introduced by Van lieshout and Baddeley (1999). It is defined by
“类型i任何类型的”多类型J一个固定的多类型的功能点过程X由范·利斯豪特和巴德利（1999年）。它被定义为
where Gi.(r) is the distribution function of the distance from a type i point to the nearest other point of the pattern, and F.(r) is the distribution function of the distance from a fixed point in space to the nearest point of the pattern.
Gi.(r)是分布函数的类型i点的距离最近的其他点的格局，F.(r)是从一个固定的点在空间的分布函数的距离到最近的点的图案。
An estimate of Ji.(r) is a useful summary statistic in exploratory data analysis of a multitype point pattern. If the pattern is a marked Poisson point process, then Ji.(r) = 1. If the subprocess of type i points is independent of the subprocess of points of all types not equal to i, then Ji.(r) equals Jii(r), the ordinary J function (see Jest and Van Lieshout and Baddeley (1996)) of the points of type i. Hence deviations from zero of the empirical estimate of Ji.-Jii may suggest dependence between types.
Ji.(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。如果模式是一个显着的Poisson点过程，然后Ji.(r) = 1。如果子类型i点是独立的子进程的的各类不等于i，那么Ji.(r)等于Jii(r)，普通J点功能（见Jest和范·利斯豪特和巴德利（1996））点的类型i。因此，零的经验估计Ji.-Jii的从的偏差可能会建议类型之间的依赖关系。
This algorithm estimates Ji.(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$window) may have arbitrary shape. Biases due to edge effects are treated in the same manner as in Jest, using the Kaplan-Meier and border corrections. The main work is done by Gmulti and Fest.
该算法估计Ji.(r)点模式X。它假定X可以被视为一个实现了一个固定的（的空间均匀）随机空间点在飞机上，观察到一个有限的窗口。窗口（中指定XX$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 Ji.(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，Ji.(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 Ji.(r), currently the Kaplan-Meier estimator.
建议Ji.(r)，目前的Kaplan-Meier估计估计。
参数：r
the values of the argument r at which the function Ji.(r) has been estimated
的参数的值的r在哪些函数Ji.(r)已估计
参数：km
the Kaplan-Meier estimator of Ji.(r)
Kaplan-Meier法估计Ji.(r)
参数：rs
the “reduced sample” or “border correction” estimator of Ji.(r)
“减少样品”或“边界校正”估计Ji.(r)
参数：han
the Hanisch-style estimator of Ji.(r)
Hanisch式估计Ji.(r)
参数：un
the “uncorrected” estimator of Ji.(r) formed by taking the ratio of uncorrected empirical estimators of 1 - Gi.(r) and 1 - F.(r), see Gdot and Fest.
“裸眼”估计Ji.(r)形成的裸经验估计1 - Gi.(r)和1 - F.(r)，Gdot和Fest之比。
参数：theo
the theoretical value of Ji.(r) for a marked Poisson process, namely 1.
的理论值Ji.(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 Gdot and Fest for the point pattern.
结果也有两个属性"G"和"F"分别Gdot和Fest点模式的输出。
警告----------Warnings----------
The argument i is interpreted as a level of the factor X$marks. It is converted to a character string if it is not already a character string. The value i=1 does not refer to the first level of the factor.
解释的参数i作为的因素X$marks的水平。如果它不是已经是一个字符串，它被转换为一个字符串。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----------
Jcross, Jest, Jmulti
Jcross，Jest，Jmulti
实例----------Examples----------
# Lansing woods data: 6 types of trees[蓝星树林数据：6种树木]
data(lansing)
Jh. <- Jdot(lansing, "hickory")
plot(Jh.)
# diagnostic plot for independence between hickories and other trees[山核桃和其他树木之间的独立性诊断图]
Jhh <- Jest(lansing[lansing$marks == "hickory", ])
plot(Jhh, add=TRUE, legendpos="bottom")
## Not run: [＃不运行：]
# synthetic example with two marks "a" and "b"[合成例与2马克的“a”和“b”]
pp <- runifpoint(30) %mark% factor(sample(c("a","b"), 30, replace=TRUE))
J <- Jdot(pp, "a")
## End(Not run)[＃（不执行）]
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 13:38:14
