Gmulti(spatstat)
Gmulti()所属R语言包：spatstat
Marked Nearest Neighbour Distance Function
标记近邻距离函数
译者：生物统计家园网 机器人LoveR
描述----------Description----------
For a marked point pattern, estimate the distribution of the distance from a typical point in subset I to the nearest point of subset J.
一个显着的点模式，估计分布的距离从一个典型的子集I点的最近点的子集J。
用法----------Usage----------
Gmulti(X, I, J, r=NULL, breaks=NULL, ...,
disjoint=NULL, correction=c("rs", "km", "han"))
参数----------Arguments----------
参数：X
The observed point pattern, from which an estimate of the multitype distance distribution function GIJ(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GIJ(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.
子集点X距离的测量。
参数：J
Subset of points in X to which distances are measured.
X距离的测量点的子集。
参数：r
Optional. Numeric vector. The values of the argument r at which the distribution function GIJ(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的分布函数GIJ(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 Gmulti generalises Gest (for unmarked point patterns) and Gdot and Gcross (for multitype point patterns) to arbitrary marked point patterns.
的功能Gmulti Gest（未标记的点模式）和Gdot和Gcross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. This function computes an estimate of the cumulative distribution function GIJ(r) of the distance from a typical point of X[I] to the nearest distinct point of X[J].
假设X[I]，X[J]子集，可能有重叠，出现了明显的点过程。该函数计算的累积分布函数的估计GIJ(r)的X[I]一个典型的点距离最近的显着点X[J]。
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。
This algorithm estimates the distribution function GIJ(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$window) may have arbitrary shape. Biases due to edge effects are treated in the same manner as in Gest.
该算法估计分布函数GIJ(r)点模式X。它假定X可以被视为一个实现了一个固定的（的空间均匀）随机空间点在飞机上，观察到一个有限的窗口。窗口（中指定XX$window的）可以有任意形状的。边缘效应产生的偏差的处理中相同的方式，当在Gest。
The argument r is the vector of values for the distance r at which GIJ(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，GIJ(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。
The algorithm also returns an estimate of the hazard rate function, lambda(r), of GIJ(r). This estimate should be used with caution as GIJ(r) is not necessarily differentiable.
该算法也将返回的危险率函数的估计，lambda(r)，GIJ(r)。这个估计应谨慎使用，因为GIJ(r)不一定是微。
The naive empirical distribution of distances from each point of the pattern X to the nearest other point of the pattern, is a biased estimate of GIJ. However this is also returned by the algorithm, as it is sometimes useful in other contexts. Care should be taken not to use the uncorrected empirical GIJ as if it were an unbiased estimator of GIJ.
天真的经验分布模式X最近的其他点的模式，每个点的距离，是一个有偏估计GIJ。然而，这也由该算法返回，有时是有用的，因为它是在其他情况下。应注意不要使用未校正的经验GIJ如果它是一个无偏估计GIJ。
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 GIJ(r) has been estimated
的参数的值的r在哪些函数GIJ(r)已估计
参数：rs
the “reduced sample” or “border correction” estimator of GIJ(r)
“减少样品”或“边界校正”估计GIJ(r)
参数：han
the Hanisch-style estimator of GIJ(r)
Hanisch式估计GIJ(r)
参数：km
the spatial Kaplan-Meier estimator of GIJ(r)
Kaplan-Meier生存的空间估计GIJ(r)
参数：hazard
the hazard rate lambda(r) of GIJ(r) by the spatial Kaplan-Meier method
危险率lambda(r)GIJ(r)的空间Kaplan-Meier法
参数：raw
the uncorrected estimate of GIJ(r), i.e. the empirical distribution of the distances from each point of type i to the nearest point of type j
未校正的估计GIJ(r)，即经验分布的距离，每个点类型i到最近点类型j
参数：theo
the theoretical value of GIJ(r) for a marked Poisson process with the same estimated intensity
GIJ(r)显着的估计强度的泊松过程的理论价值
警告----------Warnings----------
The function GIJ does not necessarily have a density.
的功能GIJ不一定具有的密度。
The reduced sample estimator of GIJ is pointwise approximately unbiased, but need not be a valid distribution function; it may not be a nondecreasing function of r. Its range is always within [0,1].
减少样本估计GIJ逐点约是公正的，但不必是一个有效的分布函数，它可能不会是一个非减函数的r。它的范围是总是在[0,1]。
The spatial Kaplan-Meier estimator of GIJ is always nondecreasing but its maximum value may be less than 1.
的空间的Kaplan-Meier估计的GIJ总是非降，但其最大的值可以是小于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----------
John Wiley and Sons, 1991.
Academic Press, 1983.
Displaced amacrine cells in the retina of a rabbit : analysis of a bivariate spatial point pattern. J. Neurosci. Meth. 18, 115–125.
A bivariate spatial point pattern of ants' nests. Applied Statistics 32, 293–303
Methods for analysing spatial processes of several types of points. J. Royal Statist. Soc. Ser. B 44, 406–413.
Cambridge University Press, 1988.
Stochastic geometry and its applications. 2nd edition. Springer Verlag, 1995.
Indices of dependence between types in multivariate point patterns. Scandinavian Journal of Statistics 26, 511–532.
参见----------See Also----------
Gcross, Gdot, Gest
Gcross，Gdot，Gest
实例----------Examples----------
data(longleaf)
# Longleaf Pine data: marks represent diameter[长叶松数据：标记代表直径]
Gm <- Gmulti(longleaf, longleaf$marks <= 15, longleaf$marks >= 25)
plot(Gm)
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 13:31:45
