LSCV.risk(sparr)
LSCV.risk()所属R语言包：sparr
Leave-one-out least-squares cross-validation (LSCV) bandwidths for the relative risk function
留出最小二乘交叉-的验证（LSCV）的带宽的相对危险性功能
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Attempts to estimate a jointly optimal, common case-control fixed bandwidth for use in the kernel-smoothed relative risk function via leave-one-out least-squares cross-validation (LSCV). The user can choose between two methods described in Kelsall and Diggle (1995a;b) and Hazelton (2008).
尝试，估计一个共同的最优，通常情况下，控制固定带宽的的内核平滑的相对风险的功能，通过留一出最小二乘交叉，的验证（LSCV）。用户可以在Kelsall和Diggle（1995年a，B）和黑泽尔顿（2008）介绍的两种方法之间的选择。
用法----------Usage----------
LSCV.risk(cases, controls, hlim = NULL,
method = c("kelsall-diggle", "hazelton"), res = 128,
WIN = NULL, edge = TRUE, comment = TRUE)
参数----------Arguments----------
参数：cases
An object of type data.frame, list, matrix, or ppp describing the observed case data from which we wish to calculate the LSCV bandwidth. See "Details" for further information.
类型的对象，data.frame，list，matrix或ppp描述所观察到的情况下，我们希望计算的LSCV的带宽的数据。的详细信息，请参阅“详细信息”。
参数：controls
As for cases, but for the control observations. Both cases and controls must be of the same object class.
至于cases，但对照观察。这两个cases和controls必须是同一对象类的。
参数：hlim
A numeric vector of length 2 giving the interval over which to search for the common bandwidth which minimises the selection criterion. If NULL (default), the function attempts to automatically select an appropriate range based on multiples of Stoyan and Stoyan's (1994) rule-of-thumb. The user is strongly recommended to supply their own hlim.
给一个数值向量长度为2的时间间隔要搜索的带宽最小化的选择标准。如果NULL（默认），该函数尝试的基础上斯托扬和斯托扬（1994）的经验规则的倍数来自动选择一个合适的范围内。强烈建议用户提供他们自己的hlim。
参数：method
A character vector giving the specific selection criterion to minimise; see either Kelsall and Diggle (1995b) or Hazelton (2008). See "Details". Defaults to "kelsall-diggle".
字符向量，具体选择标准，以尽量减少，无论是Kelsall和Diggle（1995年）或黑泽尔顿（2008年）。请参阅“详细信息”。默认为"kelsall-diggle"的。
参数：res
Single integer giving the square grid resolution over which evaluation of the selection criterion takes place. Defaults to a 128 by 128 grid.
单整数的正方形网格分辨率的选择标准评估的发生。默认为128，128格。
参数：WIN
A polygonal owin object giving the study region. Ignored if data is already a ppp.object.
一个多边形的owin对象，使研究区域。 data如果已经是一个ppp.object忽略。
参数：edge
Boolean. Whether or not to employ edge-correction in the calculations. Defaults to TRUE.
布尔值。是否要在计算中采用边缘校正。默认为TRUE的。
参数：comment
Boolean. Whether or not to print function progress during execution. Defaults to TRUE.
布尔值。无论打印功能在执行过程中的进展。默认为TRUE的。
Details
详细信息----------Details----------
This function calculates a "jointly optimal", common isotropic LSCV bandwidth for the (Gaussian) kernel-smoothed relative risk function (case-control density-ratio). If the cases, controls arguments are data.frame or matrix objects, these must each have exactly two columns containing the x ([,1]) and y ([,2]) data values. Should they be lists, these must have two vector components of equal length named x and y. Alternatively, cases and controls may be objects of class ppp (see ppp.object), and the argument WIN can be ignored.<br>
此函数计算一个共同的最佳“，常见的各向同性LSCV带宽（高斯）的内核平滑的相对风险函数（病例对照密度比）。如果cases，controls参数是data.frame或matrix对象，这些每个人都必须有两列包含x（[,1]）和Y（[,2]）的数据值。他们应该被list的，必须有两个矢量分量的长度相等名为x和y。另外，cases和controls可能是对象的类ppp（见ppp.object）的说法WIN可以忽略不计。<BR>
It can be shown that choosing a bandwidth that is equal for both case and control density estimates is preferable to computing "separately optimal" bandwidths (Kelsall and Diggle, 1995a). Setting method = "kelsall-diggle", LSCV.risk computes the common bandwidth which minimises the approximate mean integrated squared error of the log-transformed risk surface (see specifically Kelsall and Diggle, 1995b).<br>
它可以显示选择的带宽等于这两种情况下，控制密度估计计算分别最优的带宽（Kelsall和Diggle，1995）是优选的。设置method = "kelsall-diggle"，LSCV.risk计算共同的带宽最小的近似平均集成的平方误差的对数变换的风险表面（见具体Kelsall和Diggle，1995b）的。<br>物理化学学报
Alternatively, the user has the option of computing the common case-control bandwidth which minimises a weighted mean integrated squared error of the (raw) relative risk function (see Hazelton, 2008). Generally, this author has found the Kelsall-Diggle method to provide more stable performance.
可替代地，用户可以选择的计算最小化的加权平均集成的平方误差的（原始）的相对风险函数（见黑泽尔顿，2008）的常见的情况下的控制带宽。一般情况下，笔者找到了Kelsall-Diggle的方法，以提供更稳定的性能。
值----------Value----------
A single numeric value of the estimated bandwidth. The user may need to experiment with adjusting hlim to find a suitable minimum.
一个数值的估计带宽。用户可根据需要进行实验的调整hlim找到一个合适的最低。
警告----------Warning----------
Leave-one-out LSCV for jointly optimal, common bandwidth selection in the kernel-smoothed risk function is even more unstable (in terms of high variability) than the standalone density version. Caution is advised; not all applications will yield a successful result (this is termed “a breakdown of the methodology” by Kelsall and Diggle, 1995a). Undersmoothing has been noted in this author's personal experience. This method can also be computationally expensive for large data sets and fine evaluation grid resolutions.
留出LSCV共同最优的，共同的带宽选择的内核平滑风险的功能更不稳定的高变异性比单独的密度版本。应注意，不是所有的应用程序都将产生一个成功的结果（这被称为“明细账的方法”由Kelsall Diggle，1995年）。 Undersmoothing已经注意到在笔者的个人经验。这种方法也可以计算昂贵的大型数据集和精细评价电网决议。
（作者）----------Author(s)----------
T.M. Davies
参考文献----------References----------
Kelsall, J.E. and Diggle, P.J. (1995b), Non-parametric estimation of spatial variation in relative risk, Statistics in Medicine, 14, 2335-2342.<br><br> Hazelton, M. L. (2008), Letter to the editor: Kernel estimation of risk surfaces without the need for edge correction, Statistics in Medicine, 27, 2269-2272.<br><br> Stoyan, D. and Stoyan, H. (1994), Fractals, Random Shapes and Point Fields. Wiley, Great Britain. ISBN 0-471-93757-6.
参见----------See Also----------
spatstat's function bw.relrisk
spatstat的功能bw.relrisk
实例----------Examples----------
## Not run: [＃不运行：]
data(chorley)
LSCV.risk(cases = split(chorley)[[1]], controls = split(chorley)[[2]],
hlim = c(0.1,2))
## End(Not run)[＃（不执行）]
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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-30 12:28:53
