Gest(spatstat)
Gest()所属R语言包：spatstat
Nearest Neighbour Distance Function G
最近邻距离函数g
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimates the nearest neighbour distance distribution function G(r) from a point pattern in a window of arbitrary shape.
估计的最近邻距离分布函数G(r)从点图案中的任意形状的窗口。
用法----------Usage----------
Gest(X, r=NULL, breaks=NULL, ..., correction=c("rs", "km", "han"))
参数----------Arguments----------
参数：X
The observed point pattern, from which an estimate of G(r) will be computed. An object of class ppp, or data in any format acceptable to as.ppp().
观测点的模式，从一个估算的G(r)将被计算。对象的类ppp，或任何格式的数据中接受的as.ppp()。
参数：r
Optional. Numeric vector. The values of the argument r at which G(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，G(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：correction
Optional. The edge correction(s) to be used to estimate G(r). A vector of character strings selected from "none", "rs", "km", "Hanisch" and "best".
可选。边缘校正（s）到可以用来估计G(r)。一个向量"none"，"rs"，"km"，"Hanisch"和"best"选择的字符串。
Details
详细信息----------Details----------
The nearest neighbour distance distribution function (also called the “event-to-event” or “inter-event” distribution) of a point process X is the cumulative distribution function G of the distance from a typical random point of X to the nearest other point of X.
最近邻距离分布函数（也称为“事件事件”或“跨事件”的分布）的点处理X是累积分布函数G的距离一个典型的随机点X到最近的点X。
An estimate of G derived from a spatial point pattern dataset can be used in exploratory data analysis and formal inference about the pattern (Cressie, 1991; Diggle, 1983; Ripley, 1988). In exploratory analyses, the estimate of G is a useful statistic summarising one aspect of the “clustering” of points. For inferential purposes, the estimate of G is usually compared to the true value of G for a completely random (Poisson) point process, which is
的一个估计G来自空间的点模式数据集可以用于探索数据分析和正式推理有关的图案（经验Cressie，1991; Diggle，1983;里普利，1988）。在探索性分析，估计G总结“聚类”的点的一个方面是一个有用的统计。推理的目的，估计G通常的真正价值G，这是一个完全随机的（泊松）点的过程
where lambda is the intensity (expected number of points per unit area). Deviations between the empirical and theoretical G curves may suggest spatial clustering or spatial regularity.
其中lambda是强度（每单位面积的点的预期数量）。的经验和理论G曲线之间的偏差可能会建议的空间聚类或空间的规律性。
This algorithm estimates the nearest neighbour distance distribution function G 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$window) may have arbitrary shape.
这算法估计最近邻距离分布函数G点模式X。它假定X可以被视为一个实现了一个固定的（的空间均匀）随机空间点在飞机上，观察到一个有限的窗口。窗口（中指定XX$window的）可以有任意形状的。
The argument X is interpreted as a point pattern object (of class "ppp", see ppp.object) and can be supplied in any of the formats recognised by as.ppp().
参数X被解释为一个点图形对象（类"ppp"，看到ppp.object），并且可以在任何认可的as.ppp()的格式提供。
The estimation of G is hampered by edge effects arising from the unobservability of points of the random pattern outside the window. An edge correction is needed to reduce bias (Baddeley, 1998; Ripley, 1988). The edge corrections implemented here are the border method or “reduced sample” estimator, the spatial Kaplan-Meier estimator (Baddeley and Gill, 1997) and the Hanisch estimator (Hanisch, 1984).
估计G阻碍了边缘效应所产生的不可观测点的随机模式窗外。边缘校正是必要的减少偏差（巴德雷，1998;里普利，1988）。在这里实现的边缘修正边界法或“减少样本”的估计，Kaplan-Meier生存的空间估计（Baddeley和吉尔，1997年）和Hanisch估计（Hanisch，1984年）。
The argument r is the vector of values for the distance r at which G(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 estimators are computed from histogram counts. 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，G(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。的估计值计算从直方图数量。这引入了离散误差控制由纤度的断点。
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 G(r). The hazard rate is defined as the derivative
该算法也将返回的危险率函数的估计，lambda(r)，G(r)。衍生物的危害率被定义为
This estimate should be used with caution as G is not necessarily differentiable.
这个估计应谨慎使用，因为G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 G. However it is sometimes useful. It can be returned by the algorithm, by selecting correction="none". Care should be taken not to use the uncorrected empirical G as if it were an unbiased estimator of G.
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G。但有时它是有用的。它可以返回的算法，，通过选择correction="none"。应注意不要使用未校正的经验G如果它是一个无偏估计G。
To simply compute the nearest neighbour distance for each point in the pattern, use nndist. To determine which point is the nearest neighbour of a given point, use nnwhich.
简单地计算每个点的最近邻距离模式，使用nndist。要确定哪个点是一个给定的点最近的邻居，使用nnwhich。
值----------Value----------
An object of class "fv", see fv.object, which can be plotted directly using plot.fv.
类的一个对象"fv"，fv.object，它可以绘制直接使用plot.fv的。
Essentially a data frame containing some or all of the following columns:
本质上是一个数据框包含的部分或全部以下几列：
参数：r
the values of the argument r at which the function G(r) has been estimated
的参数的值的r在哪些函数G(r)已估计
参数：rs
the “reduced sample” or “border correction” estimator of G(r)
“减少样品”或“边界校正”估计G(r)
参数：km
the spatial Kaplan-Meier estimator of G(r)
Kaplan-Meier生存的空间估计G(r)
参数：hazard
the hazard rate lambda(r) of G(r) by the spatial Kaplan-Meier method
危险率lambda(r)G(r)的空间Kaplan-Meier法
参数：raw
the uncorrected estimate of G(r), i.e. the empirical distribution of the distances from each point in the pattern X to the nearest other point of the pattern
未校正的估计G(r)经验分布的图案X中的每个点的距离从其他最近的点的图案，即
参数：han
the Hanisch correction estimator of G(r)
Hanisch校正估计G(r)
参数：theo
the theoretical value of G(r) for a stationary Poisson process of the same estimated intensity.
G(r)一个固定的估计强度的泊松过程的理论价值。
警告----------Warnings----------
The function G does not necessarily have a density. Any valid c.d.f. may appear as the nearest neighbour distance distribution function of a stationary point process.
的功能G不一定具有的密度。任何有效的c.d.f.可能会出现作为近邻距离分布函数的一个固定点过程。
The reduced sample estimator of G 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G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 G is always nondecreasing but its maximum value may be less than 1.
的空间的Kaplan-Meier估计的G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----------
In O.E. Barndorff-Nielsen, W.S. Kendall and M.N.M. van Lieshout (eds) Stochastic Geometry: Likelihood and Computation. Chapman and Hall, 1998. Chapter 2, pages 37-78.
Kaplan-Meier estimators of interpoint distance distributions for spatial point processes. Annals of Statistics 25 (1997) 263-292.
John Wiley and Sons, 1991.
Academic Press, 1983.
function of nearest-neighbour distance in stationary spatial point patterns. Mathematische Operationsforschung und Statistik, series Statistics 15, 409–412.
Cambridge University Press, 1988.
Stochastic geometry and its applications. 2nd edition. Springer Verlag, 1995.
参见----------See Also----------
nndist, nnwhich, Fest, Jest, Kest, km.rs, reduced.sample, kaplan.meier
nndist，nnwhich，Fest，Jest，Kest，km.rs，reduced.sample，kaplan.meier
实例----------Examples----------
data(cells)
G <- Gest(cells)
plot(G)
# P-P style plot[P-P风图]
plot(G, cbind(km,theo) ~ theo)
# the empirical G is below the Poisson G,[的实证G是下面的泊松ĝ，]
# indicating an inhibited pattern[表明抑制模式]
## Not run: [＃不运行：]
plot(G, . ~ r)
plot(G, . ~ theo)
plot(G, asin(sqrt(.)) ~ asin(sqrt(theo)))
## 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。
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。
|
发表于 2012-9-30 13:31:21
