lambdahat(spatialkernel)
lambdahat()所属R语言包：spatialkernel
Kernel Density Estimation of Intensity Function
强功能的内核密度估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Kernel density estimation of the intensity function
核密度估计的强度功能
用法----------Usage----------
lambdahat(pts, h, gpts = NULL, poly = NULL, edge = TRUE)
参数----------Arguments----------
参数：pts
matrix containing the x,y-coordinates of the data point locations.
基质中含有x,y坐标的数据点的位置。
参数：h
numeric value of the bandwidth used in the kernel smoothing.
在内核中平滑使用的带宽的数值。
参数：gpts
matrix containing the x,y-coordinates of point locations at which to calculate the intensity function, usually a fine grid points within poly, default NULL to estimate intensity function at data locations.
基质中含有x,y坐标点位置的强度计算功能，通常是一个精细的网格点内poly，默认NULL估计强度函数在数据的位置。
参数：poly
matrix containing the x,y-coordinates of the vertices of the polygon boundary in an anticlockwise order.
矩阵包含x,y-坐标的顶点的多边形的边界的逆时针方向的顺序。
参数：edge
logical, with default TRUE to do edge-correction.
逻辑，默认TRUE做边修正。
Details
详细信息----------Details----------
Kernel smoothing methods are widely used to estimate the intensity of a spatial point process. One problem which arises is the need to handle edge effects. Several methods of edge-correction have been proposed. The adjustment factor proposed in Berman and Diggle (1989) is a double integration int_AK[(x-x_0)/h]/h^2, where A is a polygonal area, K is the smoothing kernel and h is the bandwidth used for the smoothing. Zheng, P. et\ al (2004) proposed an algorithm for fast calculate of Berman and Diggle's adjustment factor.
核平滑方法被广泛采用，来估计空间点过程的强度。由此产生的一个问题是需要处理的边缘效应。的边缘校正的几种方法已经被提出。 Berman和Diggle（1989年）中提出的调整因数是一个双集成int_AK[(x-x_0)/h]/h^2，其中A是一个多边形区域，K是平滑的内核和h是带宽用于平滑化。郑，P. \人（2004）提出了一个算法，伯曼和Diggle的调整系数的快速计算。
When gpts is NULL, lambdahat uses a leave-one-out estimator for the intensity at each of the data points, as been suggested in Baddeley et al (2000). This leave-one-out estimate at each of the data points then can be used in the inhomogeneous K function estimation kinhat when the true intensity function is unknown.
当gpts是NULL，lambdahat采用了留一出估计在每个数据点的强度，有人建议在巴德利等人（2000年）。这留一出估计在每个数据点，然后，可以使用在非均匀的K函数估计kinhat时的真实强度功能是未知的。
The default kernel is the Gaussian. The kernel function is selected by calling setkernel.
默认的内核是高斯。的核心功能是通过调用setkernel。
值----------Value----------
A list with components
组件列表
参数：lambda
numeric vector of the estimated intensity function.
数值向量的估计强度函数。
参数：...
copy of the arguments pts, gpts, h, poly, edge.
参数的副本pts, gpts, h, poly, edge。
注意----------Note----------
In principle, the double adaptive double integration algorithm of Zheng, P. et\ al (2004) can be applied to other kernel functions.
原则郑，体育等\人（2004年），双自适应双积分算法可以应用到其他的内核函数。
Other source codes used in the implementation of the double integration algorithm include
实施双积分算法中使用的源代码
Laurie, D.P. (1982) adaptive cubature code in Fortran;
劳里，D.P. （1982）自适应数值积分在Fortran代码;
Shewchuk, J.R. triangulation code in C;
Shewchuk，J.R.三角代码，在C;
Alan Murta's polygon intersection code in C (Project: Generic Polygon Clipper).
艾伦·默塔的多边形相交的代码，在C（项目：通用多边形快船）。
参考文献----------References----------
of the second-order intensity of a spatial point process, J. R. Stat. Soc. B, 51, 81–92.
Kernel Smoothing — When Is It Necessary? Proceedings of the GisVet Conference 2004, University of Guelph, Ontario, Canada, June 2004.
(2000) Non and semi-parametric estimation of interaction in inhomogeneous point patterns, Statistica Neerlandica, 54, 3, 329–350.
over a Triangle. ACM–Trans. Math. Software, 8, 210–218.
Generator and Delaunay Triangulator at http://www-2.cs.cmu.edu/~quake/triangle.html.
http://www.cs.man.ac.uk/~toby/alan/software/\#gpc.
NAG's Fortran 90 Library. (http://www.nag.co.uk/numeric/fn/manual/html/c11_fn03.html)
参见----------See Also----------
setkernel, kinhat, density
setkernel，kinhat，density
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 12:47:30
