R语言 sparr包 sparr-package()函数中文帮助文档(中英文对照)

loveR · 发表于 2012-9-30 12:29:34

sparr-package(sparr)
sparr-package()所属R语言包：sparr

 The sparr Package: SPAtial Relative Risk
 sparr包装：空间相对风险

 译者：生物统计家园网机器人LoveR

描述----------Description----------

Provides functions to estimate fixed and adaptive kernel-smoothed relative risk surfaces via the density-ratio method and perform subsequent inference.
估计固定和自适应的内核平滑的相对风险通过表面的密度比的方法，并进行后续的推论提供了多种功能。

Details

详细信息----------Details----------

Kernel smoothing, and the flexibility afforded by this methodology, provides an attractive approach to estimating complex probability density functions. This is particularly of interest when exploring problems in geographical epidemiology, the study of disease dispersion throughout some spatial region, given a population. The so-called "relative risk surface", constructed as a ratio of estimated case to control densities (Bithell, 1990; 1991), describes the variation in the "risk" of the disease, given the underlying at-risk population. This is a technique that has been applied successfully for mainly exploratory purposes in a number of different examples (see for example Sabel et al., 2000; Prince et al., 2001; Wheeler, 2007).
核平滑，并且通过这种方法的灵活性，提供了一个有吸引力的复杂的概率密度函数估计方法。探索GEO流行病学中存在的问题时，这是特别的兴趣，研究疾病的分散在整个某些空间区域，由于人口。所谓的“相对风险表面，构成为的估计的情况下的比率控制密度（Bithell，1990年，1991年），描述了在”风险“的疾病的变化，由于底层的高危人群。这是一种技术，已被成功地应用探索为主要目的在若干不同的实施例（参见例如扎贝尔等人，2000;王子等人，2001;惠勒，2007年）。

This package provides functions for bivariate kernel density estimation (KDE), implementing both fixed and "variable" or "adaptive" (Abramson, 1982) smoothing parameter options (see the function documentation for more information). A selection of bandwidth calculators for bivariate KDE and the relative risk function are provided, including one based on the maximal smoothing principle (Terrell, 1990), and others involving a leave-one-out least-squares cross-validation (see below). In addition, the ability to construct asymptotically derived p-value surfaces ("tolerance" contours of which signal statistically significant sub-regions of "extremity" in a risk surface - Hazelton and Davies, 2009; Davies and Hazelton, 2010), as well as some flexible visualisation tools, are provided.
这个包提供的功能为二元内核密度估计（KDE），实施固定和变量或自适应（艾布拉姆森，1982年）的平滑参数选择（有关更多信息，请参见函数文档）。提供一个选择的二元KDE和带宽计算器的相对危险性功能，其中包括一个基于最大平滑原则（特雷尔，1990年），以及其他涉及留一出最小二乘交叉验证（见下文）。此外，的能力建设渐近得出的p值的表面（“宽容”的轮廓，其中的“肢体”的风险表面信号统计上显着的次区域 - 黑泽尔顿和戴维斯，2009年，戴维斯和黑泽尔顿，2010年），以及一些灵活的可视化工具，所提供。

The content of sparr can be broken up as follows: 
被打破的内容sparr如下：参考

Datasets PBC a case/control planar point pattern (ppp) concerning liver disease in northern England. Also available is the case/control dataset chorley of the spatstat package, which concerns the distribution of laryngeal cancer in an area of Lancashire, England. Bandwidth calculators OS estimation of an isotropic smoothing parameter for bivariate KDE, based on the oversmoothing principle introduced by Terrell (1990). NS estimation of an isotropic smoothing parameter for bivariate KDE, based on the optimal value for a normal density (bivariate normal scale rule - see e.g. Wand and Jones, 1995). LSCV.density a least-squares cross-validated (LSCV) estimate of an isotropic bandwidth for bivariate KDE (see e.g. Bowman and Azzalini, 1997). LSCV.risk a least-squares cross-validated (LSCV) estimate of a jointly optimal, common isotropic case-control bandwidth for the kernel-smoothed risk function (see Kelsall and Diggle, 1995a;b and Hazelton, 2008). Bivariate functions KBivN bivariate normal (Gaussian) kernel KBivQ bivariate quartic (biweight) kernel bivariate.density kernel density estimate of bivariate data; fixed or adaptive smoothing Relative risk and p-value surfaces risk estimation of a (log) relative risk function tolerance calculation of asymptotic p-value surface Printing and summarising objects S3 methods (print.bivden, print.rrs, summary.bivden and summary.rrs) are available for the bivariate density and risk function objects. Visualisation Most applications of the relative risk function in practice require plotting the relative risk within the study region (especially for an inspection of tolerance contours). To this end, sparr provides a number of different ways to achieve attractive and flexible visualisation. The user may produce a heat plot, a perspective plot, a contour plot, or an interactive 3D perspective plot (that the user can pan around and zoom - courtesy of the powerful rgl package; see below) for either an estimated relative risk function or a bivariate density estimate. These capabilities are available through S3 support of the plot function; see plot.bivden for visualising a single bivariate density estimate from bivariate.density, and plot.rrs for visualisation of an estimated relative risk function from risk.
数据集 PBC情况/控制平面点模式（ppp）的肝脏疾病，在英格兰北部的。同时还提供了包装/控制数据集chorley的spatstat包，其中涉及分布在英国兰开夏郡，区的喉癌。 带宽计算器参考 X>估计的二元KDE，各向同性的平滑参数介绍的特雷尔（1990）oversmoothing原则的基础上。参考OS二元KDE是各向同性的平滑参数估计，基于最优值，正常的密度（二元正常范围规则 - 请魔杖和琼斯，1995年）。参考NS最小二乘法交叉验证（LSCV）估计的各向同性的带宽，二元KDE（例如，见鲍曼和Azzalini的， 1997年）。参考LSCV.density的最小二乘交叉验证（LSCV）估计的一个共同的最优，共同各向同性的情况下，控制带宽的内核平滑风险的功能（见Kelsall和Diggle，1995年;黑泽尔顿，2008年）。参考参考二元函数的参考LSCV.risk二元正态（高斯）内核的参考KBivN二元四次（biweight）的核心 KBivQ内核固定或自适应平滑参考参考的相对风险和p值表面 bivariate.density估计的相对危险度（log）功能 risk的计算密度估计的二元数据;渐近p值表面 印刷及总结对象 的tolerance方法（S3，print.bivden，print.rrs和summary.bivden）是的二元密度函数和风险函数对象。 可视化 大多数应用程序的相对危险性功能在实践中需要绘制研究区域内的相对危险（特别是检查公差轮廓）。为此，summary.rrs提供了许多不同的方式来实现吸引力和灵活的可视化。用户可能会产生热图，透视图，等高线图，或一个估计一个交互式的三维透视图（用户可以平移和缩放 - 由强大的sparr包，见下文）相对风险函数或二元密度估计。这些功能都可以通过rgl的支持S3功能的 plotplot.bivden可视化的单一的二元密度估计，并参考<X >可视化的估计相对风险的功能bivariate.density。

依存关系----------Dependencies----------

The sparr package depends upon some other important contributions to CRAN in order to operate; their uses here are indicated: spatstat - Fast-fourier transform assistance with fixed and adaptive density estimation, as well as region handling; see Baddeley and Turner (2005). rgl - Interactive 3D plotting of densities and surfaces; see Adler and Murdoch (2009). MASS - Utility support for internal functions; see Venables and Ripley (2002).
sparr软件包依赖于CRAN，以经营一些其他重要的贡献，其用途在这里表示： spatstat - 快速傅立叶变换的协助固定和自适应密度估计，以及区域的处理;巴德利和Turner（2005年）。 rgl： - 互动3D绘图的密度和表面;阿德勒和默多克（2009）。参考MASS - 实用工具支持内部功能;维纳布尔斯和Ripley（2002年）。

引用----------Citation----------

To cite use of sparr in publications, the user may refer to the following work: Davies, T.M., Hazelton, M.L. and Marshall, J.C. (2011), sparr: Analyzing spatial relative risk using fixed and adaptive kernel density estimation in R, Journal of Statistical Software 39(1), 1-14.
要举使用sparr的出版物，用户可参考以下工作：参考戴维斯，TM，ML，黑泽尔顿马歇尔，JC（2011年），sparr：分析使用固定和自适应核密度估计的空间相对风险R，统计软件学报“，39（1），1-14。

（作者）----------Author(s)----------

T.M. Davies 
Dept. of Mathematics & Statistics, University of Otago, Dunedin, New Zealand; 
M.L. Hazelton and J.C. Marshall 
Institute of Fundamental Sciences - Statistics, Massey University, Palmerston North, New Zealand. 

Maintainer: T.M.D. <a href="mailto:tdavies@maths.otago.ac.nz">tdavies@maths.otago.ac.nz</a> 
Feedback welcomed.

参考文献----------References----------

Adler, D. and Murdoch, D. (2009), rgl: 3D visualization device system (OpenGL). R package version 0.87; URL: http://CRAN.R-project.org/package=rgl Baddeley, A. and Turner, R. (2005), Spatstat: an R package for analyzing spatial point patterns, Journal of Statistical Software, 12(6), 1-42. Bithell, J.F. (1990), An application of density estimation to geographical epidemiology, Statistics in Medicine, 9, 691-701. Bithell, J.F. (1991), Estimation of relative risk function,. Statistics in Medicine, 10, 1745-1751. Bowman, A.W. and Azzalini, A. (1997), Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations. Oxford University Press Inc., New York. ISBN 0-19-852396-3. Davies, T.M. and Hazelton, M.L. (2010), Adaptive kernel estimation of spatial relative risk, Statistics in Medicine, 29(23) 2423-2437. 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. Hazelton, M.L. and Davies, T.M. (2009), Inference based on kernel estimates of the relative risk function in geographical epidemiology, Biometrical Journal, 51(1), 98-109. Kelsall, J.E. and Diggle, P.J. (1995a), Kernel estimation of relative risk, Bernoulli, 1, 3-16. Kelsall, J.E. and Diggle, P.J. (1995b), Non-parametric estimation of spatial variation in relative risk, Statistics in Medicine, 14, 2335-2342. Prince, M. I., Chetwynd, A., Diggle, P. J., Jarner, M., Metcalf, J. V. and James, O. F. W. (2001), The geographical distribution of primary biliary cirrhosis in a well-defined cohort, Hepatology 34, 1083-1088. Sabel, C. E., Gatrell, A. C., Loytonenc, M., Maasiltad, P. and Jokelainene, M. (2000), Modelling exposure opportunitites: estimating relative risk for motor disease in Finland, Social Science & Medicine 50, 1121-1137. Terrell, G.R. (1990), The maximal smoothing principle in density estimation, Journal of the American Statistical Association, 85, 470-477. Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S, Fourth Edition, Springer, New York. Wand, M.P. and Jones, C.M., 1995. Kernel Smoothing, Chapman & Hall, London. Wheeler, D. C. (2007), A comparison of spatial clustering and cluster detection techniques for childhood leukemia incidence in Ohio, 1996-2003, International Journal of Health Geographics, 6(13).
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。

账号		自动登录	找回密码
密码			注册