kppm(spatstat)
kppm()所属R语言包：spatstat
Fit Cluster or Cox Point Process Model
适合聚类或考克斯点的过程模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Fit a homogeneous or inhomogeneous cluster process or Cox point process model to a point pattern.
适合均匀或不均匀的聚类或考克斯点的过程模型阵列点。
用法----------Usage----------
kppm(X, trend = ~1, clusters = "Thomas", covariates = NULL, ...,
statistic="K", statargs=list())
参数----------Arguments----------
参数：X
Point pattern (object of class "ppp") to which the model should be fitted.
点模式（类的对象"ppp"），该模型应安装。
参数：trend
An R formula, with no left hand side, specifying the form of the log intensity.
一个R式中，与没有左手侧，log强度指定的形式。
参数：clusters
Character string determining the cluster model. Partially matched. Options are "Thomas", "MatClust", "Cauchy", "VarGamma" and "LGCP".
字符串确定聚类模型。部分匹配。选项"Thomas"，"MatClust"，"Cauchy"，"VarGamma"和"LGCP"。
参数：covariates
The values of any spatial covariates (other than the Cartesian coordinates) required by the model. A named list of pixel images, functions, windows or numeric constants.
模型所需的任何空间的协变量的值的（笛卡尔坐标以外）。像素的图片，功能，窗口或数字常量命名列表。
参数：...
Arguments passed to thomas.estK or thomas.estpcf or matclust.estK or matclust.estpcf or lgcp.estK or lgcp.estpcf or cauchy.estK or cauchy.estpcf or vargamma.estK or vargamma.estpcf controlling the minimum contrast fitting algorithm.
参数传递给thomas.estK或thomas.estpcf或matclust.estK或matclust.estpcf或lgcp.estK或lgcp.estpcf或cauchy.estK或cauchy.estpcf或vargamma.estK或vargamma.estpcf控制在最小对比度拟合算法。
参数：statistic
The choice of summary statistic: either "K" or "pcf".
摘要统计的选择：是"K"或"pcf"。
参数：statargs
Optional list of arguments to be used when calculating the summary statistic. See Details.
可选的参数列表，计算时要使用的汇总统计。查看详细信息。
Details
详细信息----------Details----------
This function fits a Cox point process model to the point pattern dataset X. Cox models are suitable for spatially clustered point patterns.
此功能适合考克斯的点过程模型的点模式数据X。空间聚集点模式的Cox模型是合适的。
The model may be either a Poisson cluster process or a Cox process. The type of model is determined by the argument clusters. Currently the options are clusters="Thomas" for the Thomas process, clusters="MatClust" for the Matern cluster process, clusters="Cauchy" for the Neyman-Scott cluster process with Cauchy kernel, clusters="VarGamma" for the Neyman-Scott cluster process with Variance Gamma kernel, and clusters="LGCP" for the log-Gaussian Cox process. The first four models are Poisson cluster processes.
该模型可以是一个泊松聚类过程或Cox过程。模型的类型是由参数clusters。目前，该选项是clusters="Thomas"的托马斯过程中，clusters="MatClust"为的Matern的聚类过程中，clusters="Cauchy"奈曼 - 斯科特聚类过程为与柯西核心，clusters="VarGamma"的奈曼斯科特聚类方差Gamma内核的过程，和clusters="LGCP"对的log高斯考克斯的过程。前四个模型是泊松聚类的过程。
If the trend is constant (~1) then the model is homogeneous. The empirical K-function of the data is computed using Kest, and the parameters of the cluster model are estimated by the method of minimum contrast (matching the theoretical K-function of the model to the empirical K-function of the data, as explained in mincontrast).
如果这种趋势是不变的（~1），那么该模型是均匀的。实证的K-功能的数据是使用Kest，和聚类模型的参数估计最小对比度的方法，（匹配的理论K-函数的模型来计算的的经验K功能的数据，解释在mincontrast）。
Otherwise, the model is inhomogeneous. The algorithm first estimates the intensity function of the point process, by fitting a Poisson process with log intensity of the form specified by the formula trend. Then the inhomogeneous K function is estimated by Kinhom using this fitted intensity. Finally the parameters of the cluster model are estimated by the method of minimum contrast using the inhomogeneous K function. This two-step estimation procedure is due to Waagepetersen (2007).
否则，该模型是不均匀的。该算法首先估计功能点过程中，通过拟合的形式指定的公式trend的log强度的泊松过程的强度。 K使用该拟合强度的不均匀Kinhom函数估计。最后聚类模型的参数估计最小对比度的方法，使用的不均匀K函数。这两个步骤的估计过程是由于到Waagepetersen（2007年）。
If statistic="pcf" then instead of using the K-function, the algorithm will use the pair correlation function pcf for homogeneous models and the inhomogeneous pair correlation function pcfinhom for inhomogeneous models. In this case, the smoothing parameters of the pair correlation can be controlled using the argument statargs, as shown in the Examples.
如果statistic="pcf"，而不是使用K功能，该算法将使用对相关函数pcf均匀模型和不均匀性对相关功能pcfinhom非均匀模型。在这种情况下，可以对相关的平滑化参数的使用参数控制statargs，如在实施例中所示。
值----------Value----------
An object of class "kppm" representing the fitted model. There are methods for printing, plotting, predicting, simulating and updating objects of this class.
对象的类"kppm"拟合模型。有一些方法进行印刷，策划，预测，模拟和更新这个类的对象。
（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>
with contributions from Abdollah Jalilian and Rasmus Waagepetersen.
参考文献----------References----------
Decomposition of variance for spatial Cox processes. Manuscript submitted for publication.
An estimating function approach to inference for inhomogeneous Neyman-Scott processes. Biometrics 63, 252–258.
参见----------See Also----------
methods for kppm objects: plot.kppm, predict.kppm, simulate.kppm, update.kppm, vcov.kppm, methods.kppm, Kmodel.kppm, pcfmodel.kppm.
kppm对象：plot.kppm，predict.kppm，simulate.kppm，update.kppm，vcov.kppm，methods.kppm，Kmodel.kppm的方法 pcfmodel.kppm。
Fitting algorithms: thomas.estK, matclust.estK, lgcp.estK, cauchy.estK, vargamma.estK, thomas.estpcf, matclust.estpcf, lgcp.estpcf, cauchy.estpcf, vargamma.estpcf, mincontrast.
拟合算法：thomas.estK，matclust.estK，lgcp.estK，cauchy.estK，vargamma.estK，thomas.estpcf，matclust.estpcf，lgcp.estpcf ，cauchy.estpcf，vargamma.estpcf，mincontrast。
Summary statistics: Kest, Kinhom, pcf, pcfinhom.
汇总统计数据：Kest，Kinhom，pcf，pcfinhom。
See also ppm
ppm
实例----------Examples----------
data(redwood)
kppm(redwood, ~1, "Thomas")
kppm(redwood, ~x, "MatClust")
kppm(redwood, ~x, "MatClust", statistic="pcf", statargs=list(stoyan=0.2))
kppm(redwood, ~1, "LGCP", statistic="pcf")
kppm(redwood, ~x, cluster="Cauchy", statistic="K")
kppm(redwood, cluster="VarGamma", nu.ker = 0.5, statistic="pcf")
if(require(RandomFields) && RandomFieldsSafe()) {
kppm(redwood, ~x, "LGCP", statistic="pcf",
covmodel=list(model="matern", nu=0.3))
}
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 13:40:24
