Kdot.inhom(spatstat)
Kdot.inhom()所属R语言包：spatstat
Inhomogeneous Multitype K Dot Function
非均匀多类型ķ点功能
译者：生物统计家园网 机器人LoveR
描述----------Description----------
For a multitype point pattern, estimate the inhomogeneous version of the dot K function, which counts the expected number of points of any type within a given distance of a point of type i, adjusted for spatially varying intensity.
对于多类型的点模式，估计版本的不均匀点K函数，它计算的预期数量的任何类型的点给定距离内的一个点的类型i，调整空间上变化的强度。
用法----------Usage----------
Kdot.inhom(X, i, lambdaI=NULL, lambdadot=NULL, ..., r=NULL, breaks=NULL,
correction = c("border", "isotropic", "Ripley", "translate"),
sigma=NULL, varcov=NULL, lambdaIdot=NULL)
参数----------Arguments----------
参数：X
The observed point pattern, from which an estimate of the inhomogeneous cross type K function Ki.(r) will be computed. It must be a multitype point pattern (a marked point pattern whose marks are a factor). See under Details.
所观察到的点图案，从其中的估计的非均匀交叉型K函数Ki.(r)将被计算。它必须是一个多类型的点模式（一个标记点图案的标记是一个因素）。请参阅“详细信息”下。
参数：i
The type (mark value) of the points in X from which distances are measured. A character string (or something that will be converted to a character string). Defaults to the first level of marks(X).
X距离的测量点的类型（标记值）。一个字符串（或东西都将被转换为一个字符串）。默认的第一级marks(X)。
参数：lambdaI
Optional. Values of the estimated intensity of the sub-process of points of type i. Either a pixel image (object of class "im"), a numeric vector containing the intensity values at each of the type i points in X, or a function(x,y) which can be evaluated to give the intensity value at any location.
可选。值的估计强度的的类型i点的子流程。无论是像素的图像（对象的类"im"），于各类型的i点X或function(x,y)可以含有的强度值的一个数值向量评价，得到的强度值在任何位置。
参数：lambdadot
Optional. Values of the estimated intensity of the entire point process, Either a pixel image (object of class "im"), a numeric vector containing the intensity values at each of the points in X, or a function(x,y) which can be evaluated to give the intensity value at any location.
可选。估计强度的整个点过程中，无论是像素的图像（类的对象"im"），在X，或包含在每个点的强度值的一个数值向量<X的值>这在任何位置，可以计算的强度值。
参数：...
Ignored.
忽略。
参数：r
Optional. Numeric vector giving the values of the argument r at which the cross K function Kij(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。数字矢量提供的参数的值r在交叉K函数Kij(r)应进行评估。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
Optional. An alternative to the argument r. Not normally invoked by the user. See the Details section.
可选。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：correction
A character vector containing any selection of the options "border", "bord.modif", "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"，"bord.modif"，"isotropic"，"Ripley"，"translate"，"none"或"best" 。指定，边缘校正（S）。
参数：sigma
Standard deviation of isotropic Gaussian smoothing kernel, used in computing leave-one-out kernel estimates of lambdaI, lambdadot if they are omitted.
用于计算标准差的高斯平滑核，留一出lambdaI，lambdadot，如果省略了它们的内核估计。
参数：varcov
Variance-covariance matrix of anisotropic Gaussian kernel, used in computing leave-one-out kernel estimates of lambdaI, lambdadot if they are omitted. Incompatible with sigma.
方差 - 协方差矩阵的的各向异性高斯内核，用于计算留一出内核的估计lambdaI，lambdadot，如果他们被省略了。不相容的sigma。
参数：lambdaIdot
Optional. A matrix containing estimates of the product of the intensities lambdaI and lambdadot for each pair of points, the first point of type i and the second of any type.
可选。一个矩阵包含了产品的强度估计lambdaI和lambdadot每对点，第一点类型i和任何类型的第二个。
Details
详细信息----------Details----------
This is a generalisation of the function Kdot to include an adjustment for spatially inhomogeneous intensity, in a manner similar to the function Kinhom.
这是一个一般化的功能Kdot包括空间非均匀的强度调整，以类似的方式，在给函数Kinhom。
Briefly, given a multitype point process, consider the points without their types, and suppose this unmarked point process has intensity function lambda(u) at spatial locations u. Suppose we place a mass of 1/lambda(z) at each point z of the process. Then the expected total mass per unit area is 1. The inhomogeneous “dot-type” K function K[i.]inhom(r) equals the expected total mass within a radius r of a point of the process of type i, discounting this point itself.
简单地说，一个多类型点的过程中，考虑的点，没有他们的类型，并假设这未标记的点过程强度功能lambda(u)在空间位置u。假设我们放置质量1/lambda(z)的过程中在每个点z。那么预期的每单位面积的总质量为1。的非均匀的“点 - 型”K函数K[i.]inhom(r)等于预期总质量的半径范围内的r的过程中的一个点的类型i，扣除这一点本身。
If the process of type i points were independent of the points of other types, then K[i.]inhom(r) would equal pi * r^2. Deviations between the empirical Ki. curve and the theoretical curve pi * r^2 suggest dependence between the points of types i and j for j != i.
如果类型的过程中，i点是独立的其他类型的点，然后K[i.]inhom(r)就等于pi * r^2。的经验Ki.曲线和理论曲线之间的偏差pi * r^2建议点之间的类型i和jj != i的依赖。
The argument X must be a point pattern (object of class "ppp") or any data that are acceptable to as.ppp. It must be a marked point pattern, and the mark vector X$marks must be a factor.
参数X必须是点模式（类的对象"ppp"）或任何数据到as.ppp是可以接受的。它必须是一个显着的点图案，并标记矢量X$marks必须是一个因素。
The argument i will be interpreted as a level of the factor X$marks. (Warning: this means that an integer value i=3 will be interpreted as the number 3, not the 3rd smallest level). If i is missing, it defaults to the first level of the marks factor, i = levels(X$marks)[1].
参数i将被解释为一个水平的因素X$marks。 （警告：这意味着一个整数值i=3将被解释为3号，而不是第三最小的水平）。 i如果缺少，默认为第一级的标记因素，i = levels(X$marks)[1]。
The argument lambdaI supplies the values of the intensity of the sub-process of points of type i. It may be either
的参数lambdaI提供子流程的类型i点的强度值。它可以是
a pixel image (object of class "im") which gives the values of the type i intensity at all locations in the window containing X;
像素图像（对象类"im"）的值在所有位置的窗口，其中包含的类型i强度X;
a numeric vector containing the values of the type i intensity evaluated only at the data points of type i. The length of this vector must equal the number of type i points in X.
一个数值向量包含的类型i的值强度进行评估，只有在数据类型i点。这个向量的长度必须等于类型i点X。
of the form function(x,y) which can be evaluated to give values of the intensity at any locations.
的形式function(x,y)可以进行评估，得到的强度的值在任何位置。
if lambdaI is omitted then it will be estimated using a leave-one-out kernel smoother.
如果lambdaI被忽略，那么它会使用一个离开的内核平滑估计。
If lambdaI is omitted, then it will be estimated using a "leave-one-out" kernel smoother, as described in Baddeley, Moller and Waagepetersen (2000). The estimate of lambdaI for a given point is computed by removing the point from the point pattern, applying kernel smoothing to the remaining points using density.ppp, and evaluating the smoothed intensity at the point in question. 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lambdaI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）。估计lambdaI除去点从点图案，对于一个给定的点的计算方法是，施加内核平滑使用density.ppp的其它点，和评价问题点的平滑强度。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。
Similarly the argument lambdadot should contain estimated values of the intensity of the entire point process. It may be either a pixel image, a numeric vector of length equal to the number of points in X, a function, or omitted.
同样的说法lambdadot应该包含的整个过程中强度的估计值。它可以是一个像素的图像，在X的点的数量等于一个数值向量的长度，一个函数，或省略。
For advanced use only, the optional argument lambdaIdot is a matrix containing estimated values of the products of these two intensities for each pair of points, the first point of type i and the second of any type.
仅适用于高级用户使用，可选的参数lambdaIdot是一个矩阵类型i估计值的产品，这两个强度为每对点，第一点和第二个任何类型的。
The argument r is the vector of values for the distance r at which Ki.(r) should be evaluated. The values of r must be increasing nonnegative numbers and the maximum r value must exceed the radius of the largest disc contained in the window.
参数r是向量的值的距离r，Ki.(r)应该进行评估。 r的值，必须增加非负数和最大r值必须超过包含在窗口中的最大的光盘的半径。
The argument correction chooses the edge correction as explained e.g. in Kest.
参数correction选择的边缘校正的解释，例如在Kest。
The pair correlation function can also be applied to the result of Kcross.inhom; see pcf.
对相关功能也可以应用到的结果Kcross.inhom看pcf。
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 numeric columns
本质上是一个数据框包含数字的列
参数：r
the values of the argument r at which the function Ki.(r) has been estimated
的参数的值的r在哪些函数Ki.(r)已估计
参数：theo
the theoretical value of Ki.(r) for a marked Poisson process, namely pi * r^2
理论值的Ki.(r)显着的泊松过程，即pi * r^2，
together with a column or columns named "border", "bord.modif", "iso" and/or "trans", according to the selected edge corrections. These columns contain estimates of the function Ki.(r) obtained by the edge corrections named.
连同一列或多列名为"border"，"bord.modif"，"iso"和/或"trans"，根据选定的边修正。这些列包含的功能Ki.(r)命名的边缘修正的估计。
警告----------Warnings----------
The argument i is interpreted as a level of the factor X$marks. It is converted to a character string if it is not already a character string. The value i=1 does not refer to the first level of the factor.
解释的参数i作为的因素X$marks的水平。如果它不是已经是一个字符串，它被转换为一个字符串。值i=1不是指第一级的因素。
（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 Inference and Simulation for Spatial Point Processes Chapman and Hall/CRC Boca Raton, 2003.
参见----------See Also----------
Kdot, Kinhom, Kcross.inhom, Kmulti.inhom, pcf
Kdot，Kinhom，Kcross.inhom，Kmulti.inhom，pcf
实例----------Examples----------
# Lansing Woods data[蓝星伍兹数据]
data(lansing)
lansing <- lansing[seq(1,lansing$n, by=10)]
ma <- split(lansing)$maple
lg <- unmark(lansing)
# Estimate intensities by nonparametric smoothing[估计非参数平滑的强度]
lambdaM <- density.ppp(ma, sigma=0.15, at="points")
lambdadot <- density.ppp(lg, sigma=0.15, at="points")
K <- Kdot.inhom(lansing, "maple", lambdaI=lambdaM,
lambdadot=lambdadot)
# Equivalent[当量]
K <- Kdot.inhom(lansing, "maple", sigma=0.15)
# synthetic example: type A points have intensity 50,[合成例如：键入一个点的强度为50，]
# type B points have intensity 50 + 100 * x[B型点强度50 + 100 * X]
lamB <- as.im(function(x,y){50 + 100 * x}, owin())
lamdot <- as.im(function(x,y) { 100 + 100 * x}, owin())
X <- superimpose(A=runifpoispp(50), B=rpoispp(lamB))
K <- Kdot.inhom(X, "B", lambdaI=lamB, lambdadot=lamdot)
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 13:39:18
