sdtoolkit-package(sdtoolkit)
sdtoolkit-package()所属R语言包：sdtoolkit
Scenario Discovery Tools to Support Robust Decision Making
方案发现工具，以支持强大的决策
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This package is designed to provide a means for easily integrating multiple algorithms for scenario discovery and assessment, an element of the Robust Decision Making process for decisionmaking under uncertainty. It currently implements a coverage-oriented version of Friedman and Fisher's Patient Rule Induction Method (PRIM), and provides additional diagnostic tools to assess the quality of the scenarios that emerge.
这个包的设计提供了一种方法，可方便地集成多种算法的情况下发现和评估，决策过程，为决策不确定性条件下的鲁棒元素。目前，它实现了一个覆盖面向弗里德曼和费舍尔的病人的规则归纳法（PRIM）版本，并提供了额外的诊断工具的情况下，出现的质量进行评估。
Details
详细信息----------Details----------
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This package provides interactive functions for scenario discovery that should be usable by those not particularly fluent in R, as well as more direct and powerful code that can be used or extended by those more familiar with both R and the scenario discovery process. The package is built around the function sdprim (“Scenario Discovery PRIM”), which implements a very interactive, diagnostic-laden, slightly modified version of Friedman and Fisher's Patient Rule Induction Method. The function sd.start is a helper function that aids the user in reading in their data, checking for and resolving inappropriate data features, and exploring different output variables and potential thresholds.
这个包提供了互动功能的情况下发现，应该是使用那些不是特别流利的R，以及更直接，更有力的代码，可以使用或延长R和熟悉的情况下发现的过程。包的功能是建立在sdprim（“方案发现PRIM”），它实现了一个非常互动的，充满诊断，略加修改的版本，弗里德曼和费舍尔的病人规则归纳法。的功能sd.start是一个辅助功能，帮助用户在阅读他们的数据，检查和解决不恰当的数据功能，并探索不同的输出变量和潜在的阈值。
In addition, there are several <DFN>post-discovery</DFN> functions that allow the user to visualize the results of running the algorithm(s). The are currently three high-level functions of interest: seq.info, which redisplays all information identified by the algorithm, including the definition of all relevant boxes, and statistics for the entire sequence. The plotting function dimplot graphically displays normalized dimension restrictions defining a given box, and the plotting function scatterbox displays boxes over a scatterplot of the dataset, in which the points are color-coded according to their 0/1 value.
此外，有几个<DFN>的后发现</ DFN>的功能，允许用户运行的算法（）的结果可视化。目前有三个高级别感兴趣的功能：seq.info，识别算法，包括所有相关的方块的定义，统计整个序列的重新显示所有信息。绘图功能dimplot以图形方式显示标准化的尺寸限制，确定一个给定的框，和绘图功能scatterbox显示在一个散点图的数据集，其中的要点是颜色编码为0/1盒值。
Lastly, there are many “almost external” functions for which the time constraints on cleaning up and documenting as user-level functions was slightly too high for this round of work. One line descriptions of all code functions can be found in the undocumented help file, which should be useful guidance for anyone looking to capitalize on existing but less thoroughly documented work.
最后，有许多“差不多外部”功能的时间限制，清理和记录用户级别的功能是稍微偏高，这一轮的工作。一号线的所有代码的功能描述中可以找到undocumented帮助文件，这应该是有用的指引，任何人都希望利用现有的但不充分的记录工作。
（作者）----------Author(s)----------
Benjamin P. Bryant
Maintainer: The same <a href="mailto:bryant@prgs.edu">bryant@prgs.edu</a>
Note of Acknowledgment: The fundamental functions used in the operation of PRIM (specifically, peeling and pasting to form a trajectory of induced boxes) were initially taken from Duong's <span class="pkg">prim</span> package. Several pieces of code follow very closely the original functions provided by Duong, however because most functions used required small to large modifications to suit the specific needs of our scenario discovery task, it was easier to integrate these into <span class="pkg">sdtoolkit</span> rather than build a package dependency on <span class="pkg">prim</span>. The internal functions <code>peel.one</code> and <code>paste.one</code> were taken almost as is, and the primary workhorse function <code>find.traj</code> was derived heavily from Duong's <code>find.box</code>.
The funding for the development of this package came from Evolving Logic, RAND, and a National Science Foundation grant (SES-0345925) to the RAND Corporation. Evolving Logic provided funds for the majority of the customized algorithm implementation, the RAND Pardee Center for Longer Range Global Policy and the Future Human Condition supported the documentation of the package, and the National Science Foundation supported the research that led to the development of the scenario discovery methods employed in this package.
参考文献----------References----------
Bryant, B.P. and R.J. Lempert. (2009). “Thinking Inside the Box: A Participatory, Computer-Assisted Approach to Scenario Discovery”. Technological Forecasting and Social Change (forthcoming).
Lempert, R.J., B.P. Bryant and S.C. Bankes. (2008). “Comparing Algorithms for Scenario Discovery.” RAND Working Paper Series. http://www.rand.org/pub/working\_papers/2008/RAND\_WR557.pdf
Friedman, J.H., N.I. Fisher. (1999). “Bump hunting in high-dimensional data.” Statistics and Computing 9, 123-143.
Groves, D.G. and R.J. Lempert. (2007). “A New Analytic Method for Finding Policy-Relevant Scenarios”. Global Environmental Change, 17, p73-85. http://www.rand.org/pubs/reprints/RP1244/
Lempert, R.J., D.G. Groves, et al. (2006). “A general, analytic method for generating robust strategies and narrative scenarios.” Management Science 52(4): 514-528.
实例----------Examples----------
## Not run: [＃不运行：]
#Note that all the examples shown here do not illustrate use of the extra [请注意，此处所示的所有的实施例不说明使用额外的]
#options that can be passed to the functions as additional arguments. The[可以传递的功能作为附加参数的选项。 “]
#Individual function help files illustrate the use of optional[各功能的帮助文件说明使用可选的]
#arguments in more details. [更详细的参数。]
#Also, here and in other example sections of code you may see the code preceded [此外，在这里和在其他例子的代码段，你可能看到前面的代码]
#by "## Not run:" - this refers to sections of code being exempt from R's [“＃＃不运行” - 这是指免除R的代码段]
#automatic example code checking due to their interactive nature. Lines of code[自动检查示例代码由于其互动性。代码行]
#that immediately follow "## Not run:" should still be runnable, provided you[紧跟“＃＃不运行：”应该仍然是可运行的，只要你]
#remove the "## Not run:" comment.[删除“＃＃不执行”的评论。]
### ===== SD.START: =====[＃＃===== SD.START：=====]
#Users not familiar with importing and manipulating data in R will wish to start[数据在R的导入和操作不熟悉的用户将开始]
#with:[使用：]
\dontrun{
myboxes <- sd.start()
}
#This will prompt for things like directory and file name, and then walk through [这将促使的东西，如目录和文件名，然后穿行]
#data inspection, thresholding, and offer to call sdprim. [数据检查，阈值，并提供调用sdprim。]
### ===== SDPRIM: =====[＃＃===== SDPRIM：=====]
#Users confident in the soundness and appropriate formatting of their data may [用户的信心，他们的数据的合理性和适当的格式]
#take the following more direct actions: [采取更直接的行动：]
\dontrun{
#LOAD the data, either via:[加载数据，可以通过：]
mydata <- read.csv("mycsvfile.csv")
#OR[或]
loadedname <- load("myrdafile.rda")
mydata <- get(loadedname)
#Then define their input variables:[然后，定义输入变量：]
xmatrix <- mydata[,columnindexes]
#Then define their output variable using EITHER[然后定义输出变量，无论是使用]
outputvar <- mydata[,outputcolumnnumber]
#OR[或]
outputvar <- mydata[,"outputname"]
#If output var is already a 0-1 variable, then sdprim can be called as:[如果输出的无功是一个0-1变量，然后sdprim可以称为：]
myboxes <- sdprim(x=xmatrix, y=outputvar)
#Otherwise, first threshold the output variable as follows:[否则，第一阈值的输出变量，如下所示：]
outthresh <- 1*(outputvar>threshold)
#Then call sdprim:[然后调用sdprim：]
myboxes <- sdprim(x=xmatrix, y=outthresh)
}
### ===== SEQ.INFO: =====[＃＃===== SEQ.INFO：=====]
#To see a summary of sdprim output, [看到一个摘要sdprim输出，]
data(exboxes) #an example box sequence object included with the package[一个例子box序列的包中包含的对象]
boxinfo <- seq.info(exboxes)
### ===== DIMPLOT: =====[＃＃===== DIMPLOT：=====]
#To see a 'Normalized Dimension Restriction Plot' for box i, type:[盒I型，“归尺寸的限制图”：]
data(exboxes)
dimplot(exboxes, 1)
## End(Not run)[＃（不执行）]
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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 2012-9-29 23:39:29
