R语言 spatstat包 suffstat()函数中文帮助文档(中英文对照)

loveR

suffstat(spatstat)
suffstat()所属R语言包：spatstat

                                        Sufficient Statistic of Point Process Model
                                         充分统计量的点过程模型

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

The canonical sufficient statistic of a  point process model is evaluated for a given point pattern.
对于一个给定的点图案中的一个点的过程模型的规范的充分统计评价。


用法----------Usage----------


  suffstat(model, X=data.ppm(model))



参数----------Arguments----------

参数：model
A fitted point process model (object of class "ppm").  
已安装点过程模型（对象类"ppm"“）。


参数：X
A point pattern (object of class "ppp").  
点模式（类的对象"ppp"）。


Details

详细信息----------Details----------

The canonical sufficient statistic of model is evaluated for the point pattern X. This computation is useful for various Monte Carlo methods.
点模式modelX规范的充分统计量进行评估。这种计算是有用的各种Monte Carlo方法。

Here model should be a point process model (object of class "ppm", see ppm.object), typically obtained from the model-fitting function ppm. The argument X should be a point pattern (object of class "ppp").
这是model应该是一个点过程模型（对象类"ppm"，ppm.object），通常是从模型的拟合函数ppm。参数X应该是一个点模式（类的对象"ppp"“）。

Every point process model fitted by ppm has a probability density of the form
每一个点过程模型安装ppm有一个概率密度的形式

where x denotes a typical realisation (i.e. a point pattern), theta is the vector of model coefficients, Z(theta) is a normalising constant, and S(x) is a function of the realisation x, called the “canonical sufficient statistic” of the model.
x表示一个典型的实现（即点模式），theta是矢量模型系数，Z(theta)是归一化常数，和S(x)是一个功能实现x，被称为“规范”的模式充分统计量。

For example, the stationary Poisson process has canonical sufficient statistic S(x)=n(x), the number of points in x. The stationary Strauss process with interaction range r (and fitted with no edge correction) has canonical sufficient statistic S(x)=(n(x),s(x)) where s(x) is the number of pairs of points in x which are closer than a distance r to each other.
例如，平稳泊松过程具有典型的充分统计量S(x)=n(x)，在x点的数量。固定施特劳斯过程中的相互作用范围r（没有边缘校正，并配备）有规范的充分统计量S(x)=(n(x),s(x))其中s(x)是在x这点的数量对比距离r彼此更接近。

suffstat(model, X) returns the value of S(x), where S is the canonical sufficient statistic associated with model, evaluated when x is the given point pattern X. The result is a numeric vector, with entries which correspond to the entries of the coefficient vector coef(model).
suffstat(model, X)返回值S(x)，这里S是典型的充分统计量与model，评估，当x是给定的点模式X的。其结果是一个数值向量，与条目对应的项的系数向量coef(model)。

The sufficient statistic S does not depend on the fitted coefficients of the model. However it does depend on the irregular parameters which are fixed in the original call to ppm, for example, the interaction range r of the Strauss process.
的充分统计量S不依赖于该模型的拟合系数。然而，它不依赖于被固定在原来的呼叫ppm，例如，交互范围r施特劳斯进程不规则参数。

The sufficient statistic also depends on the edge correction that was used to fit the model. For example in a Strauss process,
的充分统计量，也取决于被用来适应模型的边缘校正。例如在施特劳斯过程，

If the model is fitted with correction="none", the sufficient statistic is S(x) = (n(x), s(x)) where n(x) is the number of points and s(x) is the number of pairs of points which are closer than r units apart.
如果模型都配有correction="none"，充分统计量是S(x) = (n(x), s(x))其中n(x)点的数量和s(x)是对点的距离小于r单位分开。

If the model is fitted with correction="periodic", the sufficient statistic is the same as above, except that distances are measured in the periodic sense.
如果模型嵌合correction="periodic"，足够的统计量是与上述相同，除了周期意义上的距离的测量。

If the model is fitted with correction="translate", then n(x) is unchanged but s(x) is replaced by a weighted sum (the sum of the translation correction weights for all pairs of points which are closer than r units apart).
如果模型嵌合correction="translate"，那么n(x)是不变，但s(x)被替换由加权和（所有双点更接近比翻译校正的权重的总和r单位分开）。

If the model is fitted with correction="border" (the default), then points lying less than r units from the boundary of the observation window are treated as fixed. Thus n(x) is replaced by the number n[r](x) of points lying at least r units from the boundary of the observation window, and s(x) is replaced by the number s[r](x) of pairs of points, which are closer than r units apart, and at least one of which lies more than r units from the boundary of the observation window.
如果模型都配有correction="border"（默认值），然后点躺在比r单位从观察窗的边界被视为固定。因此n(x)被替换由数量n[r](x)躺在至少r从观察窗口的边界单位的点，和s(x)被替换由数量s[r](x) 对点，这是接近r单位分开，并且其中至少有一个位于超过r单位从观察窗口的边界。

Non-finite values of the sufficient statistic (NA or -Inf) may be returned if the point pattern X is not a possible realisation of the model (i.e. if X has zero probability of occurring under model for all values of the canonical coefficients theta).
非限定值的充分统计量（NA或-Inf）可能会被退回，如果点模式X的是不是一个可能实现的模型（即如果X下发生的概率为零model的所有值的典型系数theta）。


值----------Value----------

A numeric vector of sufficient statistics. The entries correspond to the model coefficients coef(model).
一个数值向量的充分统计量。条目对应的模型系数coef(model)。


（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>




参见----------See Also----------

ppm
ppm


实例----------Examples----------


    data(swedishpines)
    fitS <- ppm(swedishpines, ~1, Strauss(7))
    X <- rpoispp(summary(swedishpines)$intensity, win=swedishpines$window)
    suffstat(fitS, X)

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。