Kscaled(spatstat)
Kscaled()所属R语言包：spatstat
Locally Scaled K-function
本地缩放K-函数
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimates the template K function of a locally-scaled point process.
估计K本地规模的功能点过程的模板。
用法----------Usage----------
Kscaled(X, lambda=NULL, ..., r = NULL, breaks = NULL,
correction=c("border", "isotropic", "translate"),
sigma=NULL, varcov=NULL)
Lscaled(...)
参数----------Arguments----------
参数：X
The observed data point pattern, from which an estimate of the locally scaled K function will be computed. An object of class "ppp" or in a format recognised by as.ppp().
所观察到的数据点的图案，从本地缩放K函数的估计将被计算。类的一个对象"ppp"或as.ppp()识别的格式。
参数：lambda
Optional. Values of the estimated intensity function. Either a vector giving the intensity values at the points of the pattern X, a pixel image (object of class "im") giving the intensity values at all locations, or a function(x,y) which can be evaluated to give the intensity value at any location.
可选。值的估计强度的功能。要么一个向量给的图案的点处的强度值X，一个像素的图像（对象类"im"），得到在所有位置的强度值，或function(x,y)可以评价，得到的强度值在任何位置。
参数：...
Arguments passed from Lscaled to Kscaled and from Kscaled to density.ppp if lambda is omitted.
传递参数Lscaled到Kscaled和Kscaled到density.ppp如果lambda被省略。
参数：r
vector of values for the argument r at which the locally scaled K function should be evaluated. Not normally given by the user; there is a sensible default.
矢量参数的值r局部缩放K函数应该被评估。不正常的用户是一个明智的默认。
参数：breaks
An alternative to the argument r. Not normally invoked by the user. See Details.
替代到的参数r。通常不是由用户调用。查看详细信息。
参数：correction
A character vector containing any selection of the options "border", "isotropic", "Ripley", "translate", "none" or "best". It specifies the edge correction(s) to be applied.
字符向量含有任何选择的选项"border"，"isotropic"，"Ripley"，"translate"，"none"或"best"。指定，边缘校正（S）。
参数：sigma,varcov
Optional arguments passed to density.ppp to control the smoothing bandwidth, when lambda is estimated by kernel smoothing.
可选参数传递给density.ppp控制的平滑带宽，当lambda核平滑估计。
Details
详细信息----------Details----------
Kscaled computes an estimate of the K function for a locally scaled point process. Lscaled computes the corresponding L function L(r) = sqrt(K(r)/pi).
Kscaled计算一个估计K功能的本地规模。 Lscaled计算相应的L函数L(r) = sqrt(K(r)/pi)的。
Locally scaled point processes are a class of models for inhomogeneous point patterns, introduced by Hahn et al (2003). They include inhomogeneous Poisson processes, and many other models.
本地扩展点是不均匀的点模式，介绍了哈恩等人（2003）一类车型。他们包括非齐次泊松过程，和许多其他模型。
The template K function of a locally-scaled process is a counterpart of the “ordinary” Ripley K function, in which the distances between points of the process are measured on a spatially-varying scale (such that the locally rescaled process has unit intensity).
该模板K函数一个本地缩放过程是一个对应的“普通”雷普利K的函数，这种测量点与点之间的距离的过程中在空间上变化的规模（例如本地重新调整的过程，具有单元的强度）。
The template K function is an indicator of interaction between the points. For an inhomogeneous Poisson process, the theoretical template K function is approximately equal to K(r) = pi * r^2. Values Kscaled(r) > pi * r^2 are suggestive of clustering.
该模板K函数是点之间的相互作用的一个指标。对于非齐次泊松过程，理论模板K函数是约等于K(r) = pi * r^2的。值Kscaled(r) > pi * r^2提示聚类。
Kscaled computes an estimate of the template K function and Lscaled computes the corresponding L function L(r) = sqrt(K(r)/pi).
Kscaled计算的估计值的模板K函数和Lscaled计算对应的L函数L(r) = sqrt(K(r)/pi)。
The locally scaled interpoint distances are computed using an approximation proposed by Hahn (2007). The Euclidean distance between two points is multiplied by the average of the square roots of the intensity values at the two points.
局部缩放INTERPOINT距离使用者哈恩（2007年）提出的近似计算。在两个点之间的欧几里得距离是在两个点的强度值的平均值的平方根乘以。
The argument lambda should supply the (estimated) values of the intensity function lambda. It may be either
参数lambda应提供值（估计值）的强度功能lambda。它可以是
containing the values of the intensity function at the points of the pattern X.
含有的强度函数的值的点处的图案X。
(object of class "im") assumed to contain the values of the intensity function at all locations in the window.
（类的对象"im"）假设在所有位置的窗口中包含的值的强度功能。
which can be evaluated to give values of the intensity at any locations.
它可以在任何地方进行评价，得到的强度的值。
if lambda is omitted, then it will be estimated using a "leave-one-out" kernel smoother.
lambda如果被省略，那么它会被估计顺畅的假期一出“内核。
If lambda is a numeric vector, then its length should be equal to the number of points in the pattern X. The value lambda[i] is assumed to be the the (estimated) value of the intensity lambda(x[i]) for the point x[i] of the pattern X. Each value must be a positive number; NA's are not allowed.
如果lambda是一个数值向量，那么其长度应等于在图案X的点的数量。值lambda[i]被假定为的（估计）值的强度lambda(x[i])点x[i]的图案X。每个值必须是一个正数，“NAs不允许。
If lambda is a pixel image, the domain of the image should cover the entire window of the point pattern. If it does not (which may occur near the boundary because of discretisation error), then the missing pixel values will be obtained by applying a Gaussian blur to lambda using blur, then looking up the values of this blurred image for the missing locations. (A warning will be issued in this case.)
如果lambda是一个像素的图像，图像域覆盖整个窗口的点模式。如果它没有（这可能会发生因为离散误差的边界附近），则丢失的像素值将通过以下方式获得lambda使用blur，然后寻找值施加一个高斯模糊失踪的地点的模糊图像。 （A会发出警告，在这种情况下）。
If lambda is a function, then it will be evaluated in the form lambda(x,y) where x and y are vectors of coordinates of the points of X. It should return a numeric vector with length equal to the number of points in X.
如果lambda是一个函数，然后将评估的形式lambda(x,y)其中x和y是向量的坐标点的X。它应该返回一个数值向量长度相等的点的数量在X。
If lambda is omitted, then it will be estimated using a "leave-one-out" kernel smoother, as described in Baddeley, Moller and Waagepetersen (2000). The estimate lambda[i] for the point X[i] is computed by removing X[i] from the point pattern, applying kernel smoothing to the remaining points using density.ppp, and evaluating the smoothed intensity at the point X[i]. The smoothing kernel bandwidth is controlled by the arguments sigma and varcov, which are passed to density.ppp along with any extra arguments.
如果lambda被省略，那么它会被估计使用顺畅的假期一出“内核，中所描述巴德利，穆勒和Waagepetersen的的（2000年）。的估计lambda[i]点X[i]的计算方法是删除X[i]点模式，应用核平滑剩下的点在使用density.ppp，平滑的强度和评估点X[i]。图像平滑用核的带宽的参数所控制的sigma和varcov，它被传递给density.ppp沿与任何额外的参数。
Edge corrections are used to correct bias in the estimation of Kscaled. First the interpoint distances are rescaled, and then edge corrections are applied as in Kest. See Kest for details of the edge corrections and the options for the argument correction.
边缘校正被用来纠正偏置在估计Kscaled。首先，INTERPOINT距离的重新调整，边缘修正，然后在Kest应用。见Kest的边缘校正的选项参数correction的详细信息。
The pair correlation function can also be applied to the result of Kscaled; see pcf and pcf.fv.
对相关功能也可应用于Kscaled见pcf和pcf.fv的结果。
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 at least the following columns,
本质上是一个数据框，其中至少包含以下几列，
参数：r
the vector of values of the argument r at which the pair correlation function g(r) has been estimated
矢量参数的值r对相关函数g(r)已经估计
参数：theo
vector of values of pi * r^2, the theoretical value of Kscaled(r) for an inhomogeneous Poisson process
pi * r^2Kscaled(r)，理论值的非齐次泊松过程的数值向量
and containing additional columns according to the choice specified in the correction argument. The additional columns are named border, trans and iso and give the estimated values of Kscaled(r) using the border correction, translation correction, and Ripley isotropic correction, respectively.
并包含额外的列，根据correction参数中指定的选择。额外的列名为border，trans和iso和给出的估计值Kscaled(r)使用边框校正，翻译校正，和里普利各向同性的校正。
（作者）----------Author(s)----------
Ute Hahn,
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----------
Global and Local Scaling in the Statistics of Spatial Point Processes. Habilitationsschrift, Universitaet Augsburg.
Inhomogeneous spatial point processes by location-dependent scaling. Advances in Applied Probability 35, 319–336.
Statistics for locally scaled point patterns. In A. Baddeley, P. Gregori, J. Mateu, R. Stoica and D. Stoyan (eds.) Case Studies in Spatial Point Pattern Modelling. Lecture Notes in Statistics 185. New York: Springer Verlag. Pages 99–123.
参见----------See Also----------
Kest, pcf
Kest，pcf
实例----------Examples----------
data(bronzefilter)
X <- unmark(bronzefilter)
K <- Kscaled(X)
fit <- ppm(X, ~x)
lam <- predict(fit)
K <- Kscaled(X, lam)
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。
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发表于 2012-9-30 13:40:39
