varblock(spatstat)
varblock()所属R语言包：spatstat
Estimate Variance of Summary Statistic by Subdivision
预算差异汇总统计细分
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This command estimates the variance of any summary statistic (such as the K-function) by spatial subdivision of a single point pattern dataset.
该命令的任何空间分割的单点模式数据集的的摘要统计（如K功能）的估计方差。
用法----------Usage----------
varblock(X, fun = Kest, blocks = quadrats(X, nx = nx, ny = ny), ...,
nx = 3, ny = nx)
参数----------Arguments----------
参数：X
Point pattern dataset (object of class "ppp").
点图案集（类的对象"ppp"）。
参数：fun
Function that computes the summary statistic.
函数，计算的汇总统计。
参数：blocks
Optional. A tessellation that specifies the division of the space into blocks.
可选。一个Tessellation（曲面细分），指定的空间划分成块。
参数：...
Arguments passed to fun.
传递参数到fun。
参数：nx,ny
Optional. Number of rectangular blocks in the x and y directions. Incompatible with blocks.
可选。 x和y方向的矩形块数。不相容的blocks。
Details
详细信息----------Details----------
This command computes an estimate of the variance of the summary statistic fun(X) from a single point pattern dataset X using a subdivision method. It can be used to plot confidence intervals for the true value of a summary function such as the K-function.
该命令计算摘要统计fun(X)从单点模式的数据集X使用细分方法的方差的估计。它可以用来绘制置信区间的汇总函数，如K功能的真正价值。
The window containing X is divided into pieces by an nx * ny array of rectangles (or is divided into pieces of more general shape, according to the argument blocks if it is present). The summary statistic fun is applied to each of the corresponding sub-patterns of X as described below. Then the pointwise sample mean, sample variance and sample standard deviation of these summary statistics are computed. The two-standard-deviation confidence intervals are computed.
窗口含有X的被分成数片由nx * ny的矩形阵列（或分为更多的一般形状的片，根据参数blocks，如果它是存在的话）。摘要统计fun被施加到每一个相应的子模式X，如下所述。的逐点的样本均值，样本方差和样本标准差，这些汇总统计计算。计算两个标准偏差的置信区间。
The variance is estimated by equation (4.21) of Diggle (2003, page 52). This assumes that the point pattern X is stationary. For further details see Diggle (2003, pp 52–53).
的方差估计方程（4.21）的Diggle（2003年，第52页）。这假定，点图案X是静止的。有关进一步详情，请参阅Diggle（2003年，第52-53页）。
The estimate of the summary statistic from each block is computed as follows. For most functions fun, the estimate from block B is computed by finding the subset of X consisting of points that fall inside B, and applying fun to these points, by calling fun(X[B]).
摘要统计从每个块的估计值的计算方法如下。对于大多数功能fun，从块B估计是由寻找X的点落入B，并应用fun的子集计算这点，通过调用fun(X[B])。
However if fun is the K-function Kest, or any function which has an argument called domain, the estimate for each block B is computed by calling fun(X, domain=B). In the case of the K-function this means that the estimate from block B is computed by counting pairs of points in which the first point lies in B, while the second point may lie anywhere.
但是，如果fun是K功能Kest，或任何功能，它有一个参数叫做domain，估计每块B计算通过调用fun(X, domain=B)。在箱子K-功能。这意味着从块B的估计是通过计数对点，其中第一点在于在B，而第二点可能在于计算任何地方。
值----------Value----------
A function value table (object of class "fv") that contains the result of fun(X) as well as the sample mean, sample variance and sample standard deviation of the block estimates, together with the upper and lower two-standard-deviation confidence limits.
包含"fv"以及样本均值，样本方差和样本标准差估计块，连同上，下两函数值表（对象类的fun(X)）标准差的置信度。
错误----------Errors----------
If the blocks are too small, there may be insufficient data in some blocks, and the function fun may report an error. If this happens, you need to take larger blocks.
如果块是太小，有可能是在某些块的数据不足，和功能fun可能会报告错误。如果发生这种情况，你需要采取较大的区块。
An error message about incompatibility may occur. The different function estimates may be incompatible in some cases, for example, because they use different default edge corrections (typically because the tiles of the tessellation are not the same kind of geometric object as the window of X, or because the default edge correction depends on the number of points). To prevent this, specify the choice of edge correction, in the correction argument to fun, if it has one. Some edge corrections are only available if you have set spatstat.options(gpclib=TRUE).
约不兼容，可能会出现一个错误消息。不同功能的估计可能是不兼容的，在某些情况下，例如，因为它们使用不同的默认边缘修正（通常是因为瓷砖的镶嵌式X，或因为窗口是不一样的几何对象种预设边缘的正确性依赖于点的数量）。为了防止这种情况，指定的边缘校正的选择，在correctionfun参数，如果它有一个。如果您已设置了spatstat.options(gpclib=TRUE)一些边缘修正。
An alternative to varblock is Loh's mark bootstrap lohboot.
varblock的另一种方法是Loh的标志，引导lohboot。
（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----------
Statistical analysis of spatial point patterns, Second edition. Arnold.
参见----------See Also----------
tess, quadrats for basic manipulation.
tess，quadrats的基本操作。
lohboot for an alternative bootstrap technique.
lohboot引导的替代技术。
实例----------Examples----------
v <- varblock(amacrine, Kest, nx=4, ny=2)
v <- varblock(amacrine, Kcross, nx=4, ny=2)
if(interactive()) plot(v, iso ~ r, shade=c("hiiso", "loiso"))
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 14:19:45
