Iest(spatstat)
Iest()所属R语言包：spatstat
Estimate the I-function
的I-函数估价
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimates the summary function I(r) for a multitype point pattern.
估计的汇总函数I(r)一个多类型的点模式。
用法----------Usage----------
Iest(X, ..., eps=NULL, r=NULL, breaks=NULL, correction=NULL)
参数----------Arguments----------
参数：X
The observed point pattern, from which an estimate of I(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I(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：...
Ignored.
忽略。
参数：eps
the resolution of the discrete approximation to Euclidean distance (see below). There is a sensible default.
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 of values for the argument r at which I(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。数字矢量参数的值r，I(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 Details section.
替代到的参数r。通常不是由用户调用。见详图。
参数：correction
Optional. Vector of character strings specifying the edge correction(s) to be used by Jest.
可选。向量的字符串指定的边缘校正（s）到所使用的Jest。
Details
详细信息----------Details----------
The I function summarises the dependence between types in a multitype point process (Van Lieshout and Baddeley, 1999) It is based on the concept of the J function for an unmarked point process (Van Lieshout and Baddeley, 1996). See Jest for information about the J function.
I功能总结在一个多类型的点过程（范·利斯豪特和巴德利，1999年），它是基于对概念的J功能未标记的点过程（范·利斯豪特和巴德利类型之间的依赖关系， 1996）。见Jest信息J功能。
The I function is defined as
I函数被定义为
where J is the J function for the entire point process ignoring the marks, while Jii is the J function for the process consisting of points of type i only, and p[i] is the proportion of points which are of type i.
其中J是J整个过程中，忽略了商标的功能，而Jii是J功能组成的的类型i点的过程，p[i]是的比例类型i点。
The I function is designed to measure dependence between points of different types, even if the points are not Poisson. Let X be a stationary multitype point process, and write X[i] for the process of points of type i. If the processes X[i] are independent of each other, then the I-function is identically equal to 0. Deviations I(r) < 1 or I(r) > 1 typically indicate negative and positive association, respectively, between types. See Van Lieshout and Baddeley (1999) for further information.
I函数是用来衡量不同类型的点之间的依赖关系，即使点不泊松。让我们X是一个固定的多类型点的过程中，写X[i]类型i点的过程中，。如果该进程X[i]是相互独立的，那么I-函数是恒等于0的。偏差I(r) < 1或I(r) > 1通常表示消极和积极的关系，分别类型之间。范·利斯豪特和巴德利（1999年）获得更多信息。
An estimate of I derived from a multitype spatial point pattern dataset can be used in exploratory data analysis and formal inference about the pattern. The estimate of I(r) is compared against the constant function 0. Deviations I(r) < 1 or I(r) > 1 may suggest negative and positive association, respectively.
I来自一个多类型的空间点模式数据集可用于探索数据分析和正式推理有关的图案的一个估计。比较常数函数I(r)0的估计。偏差I(r) < 1或I(r) > 1可能会建议消极和积极的关系，分别。
This algorithm estimates the I-function from the multitype point pattern X. It assumes that X can be treated as a realisation of a stationary (spatially homogeneous) random spatial marked point process in the plane, observed through a bounded window.
该算法估计I功能的多类型模式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）随机空间标记点在飞机上，观察到一个有限的窗口。
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(). It must be a multitype point pattern (it must have a marks vector which is a factor).
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。它必须是一个多类型的点模式（它必须有一个marks向量，这是一个factor）。
The function Jest is called to compute estimates of the J functions in the formula above. In fact three different estimates are computed using different edge corrections. See Jest for information.
函数Jest被称为计算估计的J上述公式中的功能。其实，三种不同的估计，计算使用不同的边缘修正。见Jest的信息。
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
本质上是一个数据框包含
参数：r
the vector of values of the argument r at which the function I has been estimated
的参数的值的矢量r在哪些函数I已估计
参数：rs
the “reduced sample” or “border correction” estimator of I(r) computed from the border-corrected estimates of J functions
“减少样品”或“边界校正”估计I(r)计算边界校正估计J功能
参数：km
the spatial Kaplan-Meier estimator of I(r) computed from the Kaplan-Meier estimates of J functions
Kaplan-Meier生存的空间估计I(r)J函数的Kaplan-Meier法计算出估计的
参数：han
the Hanisch-style estimator of I(r) computed from the Hanisch-style estimates of J functions
I(r)从Hanisch式估计计算J函数估计Hanisch式
参数：un
the uncorrected estimate of I(r) computed from the uncorrected estimates of J
未校正的估计的从裸I(r)估计，J计算
参数：theo
the theoretical value of I(r) for a stationary Poisson process: identically equal to 0
I(r)的平稳泊松过程的理论值：恒等于0
注意----------Note----------
Sizeable amounts of memory may be needed during the calculation.
在计算过程中，可能需要相当大的数量的内存。
（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----------
Jest
Jest
实例----------Examples----------
data(amacrine)
Ic <- Iest(amacrine)
plot(Ic, main="Amacrine Cells data")
# values are below I= 0, suggesting negative association[值都低于I = 0，表明呈负相关]
# between 'on' and 'off' cells.[在“开”和“关闭”单元之间。]
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 13:33:44
