lgcp.estK(spatstat)
lgcp.estK()所属R语言包：spatstat
Fit a Log-Gaussian Cox Point Process by Minimum Contrast
最低对比度，适合一个记录斯考克斯点的过程
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Fits a log-Gaussian Cox point process model to a point pattern dataset by the Method of Minimum Contrast.
适合一个记录斯考克斯点的过程模型的方法的最低对比度阵列点的数据集。
用法----------Usage----------
lgcp.estK(X, startpar=c(sigma2=1,alpha=1),
covmodel=list(model="exponential"),
lambda=NULL,
q = 1/4, p = 2, rmin = NULL, rmax = NULL, ...)
参数----------Arguments----------
参数：X
Data to which the model will be fitted. Either a point pattern or a summary statistic. See Details.
数据，模型将被安装。无论是点模式或一个简要统计。查看详细信息。
参数：startpar
Vector of starting values for the parameters of the log-Gaussian Cox process model.
向量的起始数高斯Cox过程模型的参数值。
参数：covmodel
Specification of the covariance model for the log-Gaussian field. See Details.
规格的log高斯场的协方差模型。查看详细信息。
参数：lambda
Optional. An estimate of the intensity of the point process.
可选。的点处理的强度的估计值。
参数：q,p
Optional. Exponents for the contrast criterion.
参数：rmin, rmax
Optional. The interval of r values for the contrast criterion.
可选。的间隔r的值的对比标准。
参数：...
Optional arguments passed to optim to control the optimisation algorithm. See Details.
可选参数传递给optim控制的优化算法。查看详细信息。
Details
详细信息----------Details----------
This algorithm fits a log-Gaussian Cox point process model to a point pattern dataset by the Method of Minimum Contrast, using the K function.
该算法适合的一个log斯考克斯点的过程模型的方法的最低对比度阵列点数据集，使用K功能。
The argument X can be either
参数X可以是
a point pattern: An object of class "ppp" representing a point pattern dataset. The K function of the point pattern will be computed using Kest, and the method of minimum contrast will be applied to this.
点模式：一个对象类"ppp"的一个点模式的数据集。使用K，和最小对比度的方法，将被应用到这个Kest函数将被计算的点图案。
a summary statistic: An object of class "fv" containing the values of a summary statistic, computed for a point pattern dataset. The summary statistic should be the K function, and this object should have been obtained by a call to Kest or one of its relatives.
一个简要统计：类的一个对象"fv"值的汇总统计，计算点模式数据集。摘要统计应该是K功能，这个对象应该已经获得通过调用Kest或它的亲戚。
The algorithm fits a log-Gaussian Cox point process (LGCP) model to X, by finding the parameters of the LGCP model which give the closest match between the theoretical K function of the LGCP model and the observed K function. For a more detailed explanation of the Method of Minimum Contrast, see mincontrast.
该算法适合log高斯考克斯点的过程（LGCP）模型X中，通过寻找这给理论K函数的LGCP模型和所观察到的最接近的匹配之间的LGCP模型的参数K功能。最低对比度的方法对于更详细的说明，请参阅mincontrast。
The model fitted is a stationary, isotropic log-Gaussian Cox process (Moller and Waagepetersen, 2003, pp. 72-76). To define this process we start with a stationary Gaussian random field Z in the two-dimensional plane, with constant mean mu and covariance function C(r). Given Z, we generate a Poisson point process Y with intensity function lambda(u) = exp(Z(u)) at location u. Then Y is a log-Gaussian Cox process.
装上该模型是固定的，各向同性的log - 高斯Cox过程（Moller和Waagepetersen，2003年，页72-76）。要定义这个过程中，我们开始一个平稳高斯随机域“Z在二维平面上的，恒定的平均mu和协方差函数C(r)的。鉴于Z，我们产生一个泊松点过程Y与强度函数lambda(u) = exp(Z(u))位置u。然后Y是一个数高斯Cox过程。
The K-function of the LGCP is
K功能的LGCP
The intensity of the LGCP is
强度的LGCP是
The covariance function C(r) is parametrised in the form
parametrised的形式的协方差函数C(r)
where sigma^2 and alpha are parameters controlling the strength and the scale of autocorrelation, respectively, and c(r) is a known covariance function determining the shape of the covariance. The strength and scale parameters sigma^2 and alpha will be estimated by the algorithm. The template covariance function c(r) must be specified as explained below.
sigma^2和alpha是参数控制的自相关的实力和规模，分别为，和c(r)是一个已知的确定形状的协方差协方差函数。的实力和规模参数sigma^2和alpha将估计的算法。必须指定的模板协方差函数c(r)，解释如下。
In this algorithm, the Method of Minimum Contrast is first used to find optimal values of the parameters sigma^2 and alpha^2. Then the remaining parameter mu is inferred from the estimated intensity lambda.
在该算法中，最小对比度的方法是第一次使用的参数sigma^2和alpha^2找到最佳值。然后，其余的参数mu可以推断出，估计强度lambda。
The template covariance function c(r) is specified using the argument covmodel. It may be any of the covariance functions recognised by the command Covariance in the RandomFields package. The default is the exponential covariance c(r) = e^(-r) so that the scaled covariance is
指定的模板协方差函数c(r)使用参数covmodel。它可以是任何由命令识别的协方差函数Covariance在RandomFields的软件包。默认的是指数的协方差c(r) = e^(-r)这样规模的协方差是
The argument covmodel should be of the form list(model="modelname", ...) where modelname is the string name of one of the covariance models recognised by the command Covariance in the RandomFields package, and ... are arguments of the form tag=value giving the values of parameters controlling the shape of these models. For example the exponential covariance is specified by covmodel=list(model="exponential") while the Matern covariance with exponent nu = 0.3 is specified by covmodel=list(model="matern", nu=0.3).
参数covmodel应该是形如list(model="modelname", ...)modelname是认可的命令Covariance的RandomFields包的协方差模型的字符串名称和...是参数的形式tag=value给控制这些模型的形状参数的值。例如，指数的协方差指定的covmodel=list(model="exponential")而在Matern的协方差与指数nu = 0.3指定的covmodel=list(model="matern", nu=0.3)。
If the argument lambda is provided, then this is used as the value of lambda. Otherwise, if X is a point pattern, then lambda will be estimated from X. If X is a summary statistic and lambda is missing, then the intensity lambda cannot be estimated, and the parameter mu will be returned as NA.
如果参数lambda，然后使用的价值的lambda。否则，如果X是一个点的模式，那么lambda将估计X。如果X是一个简要统计和lambda失踪，然后强度lambda无法估计的参数mu将返回NA。
The remaining arguments rmin,rmax,q,p control the method of minimum contrast; see mincontrast.
其余的参数rmin,rmax,q,p的最小对比度控制的方法，请参阅mincontrast。
The optimisation algorithm can be controlled through the additional arguments "..." which are passed to the optimisation function optim. For example, to constrain the parameter values to a certain range, use the argument method="L-BFGS-B" to select an optimisation algorithm that respects box constraints, and use the arguments lower and upper to specify (vectors of) minimum and maximum values for each parameter.
优化算法可以通过额外的参数来控制"..."，是传递给优化函数optim。例如，要限制的参数值在一定范围内，使用参数method="L-BFGS-B"选择尊重框式约束的优化算法，并使用的参数lower和upper，“指定（向量）为每个参数的最小值和最大值。
值----------Value----------
An object of class "minconfit". There are methods for printing and plotting this object. It contains the following main components:
对象的类"minconfit"。有这个对象的打印和绘图的方法。它包含以下主要组件：
参数：par
Vector of fitted parameter values.
拟合参数值的向量。
参数：fit
Function value table (object of class "fv") containing the observed values of the summary statistic (observed) and the theoretical values of the summary statistic computed from the fitted model parameters.
函数值表（对象类"fv"）的观测值的汇总统计（observed）与理论值拟合模型参数的汇总统计计算。
注意----------Note----------
This function is considerably slower than lgcp.estpcf because of the computation time required for the integral in the K-function.
此功能是相当慢比lgcp.estpcf因为为在K-函数的积分所需的计算时间。
Computation can be accelerated, at the cost of less accurate results, by setting spatstat.options(fastK.lgcp=TRUE).
计算可以被加速，在成本较不准确的结果，通过设置spatstat.options(fastK.lgcp=TRUE)。
（作者）----------Author(s)----------
Rasmus Waagepetersen
<a href="mailto:rw@math.auc.dk">rw@math.auc.dk</a>.
Adapted for <span class="pkg">spatstat</span> by 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>
Further modifications by Rasmus Waagepetersen
and Shen Guochun.
参考文献----------References----------
Log Gaussian Cox Processes. Scandinavian Journal of Statistics 25, 451–482.
Statistical Inference and Simulation for Spatial Point Processes. Chapman and Hall/CRC, Boca Raton.
An estimating function approach to inference for inhomogeneous Neyman-Scott processes. Biometrics 63, 252–258.
参见----------See Also----------
lgcp.estpcf for alternative method of fitting LGCP.
lgcp.estpcf替代装修LGCP方法。
matclust.estK, thomas.estK for other models.
matclust.estK，thomas.estK其它型号。
mincontrast for the generic minimum contrast fitting algorithm, including important parameters that affect the accuracy of the fit.
mincontrast最小对比度的通用拟合算法，包括一些重要的参数，影响测量精度的契合。
Covariance in the RandomFields package, for covariance function models.
CovarianceRandomFields包，协方差函数模型。
Kest for the K function.
KestK功能。
实例----------Examples----------
u <- lgcp.estK(redwood, c(sigma2=0.1, alpha=1))
u
if(interactive()) plot(u)
if(require(RandomFields) && RandomFieldsSafe()) {
lgcp.estK(redwood, 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：
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发表于 2012-9-30 13:42:29
