Jmulti(spatstat)
Jmulti()所属R语言包：spatstat
Marked J Function
明显的J函数
译者：生物统计家园网 机器人LoveR
描述----------Description----------
For a marked point pattern, estimate the multitype J function summarising dependence between the points in subset I and those in subset J.
一个显着的点模式，估计的多类型J函数扼要的点之间的依赖关系的子集I和在子集J的。
用法----------Usage----------
Jmulti(X, I, J, eps=NULL, r=NULL, breaks=NULL, ..., disjoint=NULL,
correction=NULL)
参数----------Arguments----------
参数：X
The observed point pattern, from which an estimate of the multitype distance distribution function J[IJ](r) will be computed. It must be a marked point pattern. See under Details.
所观察到的点图案，从其中一个估计的多类型的距离分布函数J[IJ](r)将被计算出来的。它必须是一个显着的点模式。请参阅“详细信息”下。
参数：I
Subset of points of X from which distances are measured. See Details.
子集点X距离的测量。查看详细信息。
参数：J
Subset of points in X to which distances are measured. See Details.
X距离的测量点的子集。查看详细信息。
参数：eps
A positive number. The pixel resolution of the discrete approximation to Euclidean distance (see Jest). There is a sensible default.
一个正数。欧几里德距离的像素分辨率的离散逼近（见Jest）。有一个合理的默认。
参数：r
numeric vector. The values of the argument r at which the distribution function J[IJ](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的分布函数J[IJ](r)应该进行评估。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：disjoint
Optional flag indicating whether the subsets I and J are disjoint. If missing, this value will be computed by inspecting the vectors I and J.
可选的标志指示的子集是否I和J是不相交的。如果缺少，这个值将被计算检查向量I和J，。
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----------
The function Jmulti generalises Jest (for unmarked point patterns) and Jdot and Jcross (for multitype point patterns) to arbitrary marked point patterns.
的功能Jmulti Jest（未标记的点模式）和Jdot和Jcross下的多点模式任意标记点模式可以推广。
Suppose X[I], X[J] are subsets, possibly overlapping, of a marked point process. Define
假设X[I]，X[J]子集，可能有重叠，出现了明显的点过程。确定
where F[J](r) is the cumulative distribution function of the distance from a fixed location to the nearest point of X[J], and GJ(r) is the distribution function of the distance from a typical point of X[I] to the nearest distinct point of X[J].
F[J](r)是累积分布函数的距离，从固定位置到最近的点X[J]，GJ(r)是一个典型的点之间的距离的分布函数<X >最接近的不同点X[I]。
The argument X must be a point pattern (object of class "ppp") or any data that are acceptable to as.ppp.
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。
The arguments I and J specify two subsets of the point pattern. They may be any type of subset indices, for example, logical vectors of length equal to npoints(X), or integer vectors with entries in the range 1 to npoints(X), or negative integer vectors.
的参数I和J指定两个点模式的子集。它们可以是任何类型的子集的索引，例如，逻辑向量长度等于npoints(X)，或整数向量的条目中的取值范围为1到npoints(X)，或负整数向量。
Alternatively, I and J may be functions that will be applied to the point pattern X to obtain index vectors. If I is a function, then evaluating I(X) should yield a valid subset index. This option is useful when generating simulation envelopes using envelope.
另外，I和J可能是点模式X获得索引向量将被应用到的功能。如果I是一个函数，然后计算I(X)应该产生一个有效的子集指数。此选项是有用的时生成模拟信封使用envelope。
It is assumed 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$window) may have arbitrary shape. Biases due to edge effects are treated in the same manner as in Jest.
它假定，X可以被视为一个固定的（在空间上均匀的）随机空间点在平面上的方法，通过一个有界的窗口观察的一个实现。窗口（中指定XX$window的）可以有任意形状的。边缘效应产生的偏差的处理中相同的方式，当在Jest。
The argument r is the vector of values for the distance r at which J[IJ](r) should be evaluated. It is also used to determine the breakpoints (in the sense of hist) for the computation of histograms of distances. The reduced-sample and Kaplan-Meier estimators are computed from histogram counts. In the case of the Kaplan-Meier estimator this introduces a discretisation error which is controlled by the fineness of the breakpoints.
参数r是向量的值的距离r，J[IJ](r)应该进行评估。它也可以用来确定断点（在感hist）的直方图的距离的计算。减少了样品和Kaplan-Meier估计从直方图数量计算。在Kaplan-Meier法估计的情况下，引入了离散误差控制细度的断点。
First-time users would be strongly advised not to specify r. However, if it is specified, r must satisfy r[1] = 0, and max(r) must be larger than the radius of the largest disc contained in the window. Furthermore, the successive entries of r must be finely spaced.
用户第一次将强烈建议不指定r的。然而，如果它被指定，r必须满足r[1] = 0，和max(r)必须大于包含在窗口中的最大的光盘的半径。此外，连续进入r必须进行精细的间隔。
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：r
the values of the argument r at which the function J[IJ](r) has been estimated
的参数的值的r在哪些函数J[IJ](r)已估计
参数：rs
the “reduced sample” or “border correction” estimator of J[IJ](r)
“减少样品”或“边界校正”估计J[IJ](r)
参数：km
the spatial Kaplan-Meier estimator of J[IJ](r)
Kaplan-Meier生存的空间估计J[IJ](r)
参数：han
the Hanisch-style estimator of J[IJ](r)
Hanisch式估计J[IJ](r)
参数：un
the uncorrected estimate of J[IJ](r), formed by taking the ratio of uncorrected empirical estimators of 1 - G[IJ](r) and 1 - F[J](r), see Gdot and Fest.
的裸眼估计J[IJ](r)，形成的裸经验估计1 - G[IJ](r)和1 - F[J](r)，Gdot和Fest之比。
参数：theo
the theoretical value of J[IJ](r) for a marked Poisson process with the same estimated intensity, namely 1.
的理论值J[IJ](r)显着的泊松过程的估计强度，即1。
（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----------
Indices of dependence between types in multivariate point patterns. Scandinavian Journal of Statistics 26, 511–532.
参见----------See Also----------
Jcross, Jdot, Jest
Jcross，Jdot，Jest
实例----------Examples----------
data(longleaf)
# Longleaf Pine data: marks represent diameter[长叶松数据：标记代表直径]
Jm <- Jmulti(longleaf, marks(longleaf) <= 15, marks(longleaf) >= 25)
plot(Jm)
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。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-30 13:38:29
