ABeginnersGuide(RMark)
ABeginnersGuide()所属R语言包：RMark
A beginners introduction and guide to RMark
一个初学者的介绍和指南RMark
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The RMark package is a collection of R functions that can be used as an interface to MARK for analysis of capture-recapture data.
RMark包是一家集R功能，可以作为一个接口，标记为捕获 - 再捕获数据的分析。
Details
详细信息----------Details----------
The library contains various functions that import/export capture data, build capture-recapture models, run the FORTRAN program MARK.EXE, and extract and display output. Program MARK has its own user interface; however, model development can be rather tedious and error-prone because the parameter structure and design matrix are created by hand. This interface in R was created to use the formula and design matrix functions in R to ease model development and reduce errors. This R interface has the following advantages: 1) Uses model notation to create design matrices rather than designing them by hand in MARK or in EXCEL, which makes model development faster and more reliable. All-different PIMS are automatically created for each group (if any). 2) Allows models based on group (factor variables) and individual covariates with groups created on the fly. Age, cohort, group and time variables are pre-defined for use in formulas. 3) Both real and beta labels are automatically added for easy output interpretation. 4) Input, output and specific results (eg parameter estimates, AICc etc) are stored in an R object where they can be manipulated as deemed useful (eg plotting, further calculations, simulation etc). 5) Parameter estimates can be displayed in triangular PIM format (if appropriate) for ease of interpretation. 6) Easy setup of batch jobs and the calls to the R functions document the model specifications and allow models to be easily reproduced or re-run if data are changed. 7) Covariate-specific estimates of real parameters can be computed within R without re-running the analysis.
该库包含了各种功能，导入/导出采集数据，建立捕获 - 再捕获模型运行的FORTRAN程序MARK.EXE的，提取和显示输出。节目标志有其自己的用户界面，然而，模型的发展是相当繁琐和容易出错的手工创建的，因为该参数的结构和设计矩阵。在R创建该接口使用的公式和设计矩阵函数在R，以纾缓模型的发展和减少错误的发生。 R接口具有以下优点：1）采用模型表示法来创建设计矩阵，而不是他们的手设计中的标记或在EXCEL中，这使得模型的开发更快，更可靠的。所有不同的PIMS是自动创建的每个组（如果有）。 2）允许基于组（因素变量）和动态创建的组的单个协变量的模型。年龄，队列，组和时间变量是预先定义在公式中使用。 3）不管是真实的测试版的标签会被自动添加简单的输出解释。 4）输入，输出和存储在特定的结果（例如，参数估计，国际会议中心等）的R对象，他们认为有用的（例如绘图，进一步的计算，模拟等），可以被操纵。 5）参数估计值可以显示在三角PIM格式（如适用）为便于解释。 6）批处理作业，易于安装到R的函数的调用文件的规格型号和模型复制或重新运行，如果数据被更改。 7）协方差估计实际参数，可以计算出在R，而无需重新运行分析。
The following are the MARK capture-recapture models that
以下是MARK捕获 - 再捕获模型
There is one limitation of this interface beyond the obvious that it does not currently handle all of the models and capabilities in MARK. All models in this interface are developed via a design matrix approach rather than coding the model structure via parameter index matrices (PIMS). In most cases, a logit or other link is used by default which has implications for ability of MARK to count the number of identifiable parameters (see dipper for an example). However, beginning with v1.7.6 the sin link is now supported if the formula specifies an identity design matrix for the parameter.
明显超出这个接口，它目前不处理所有的模型和能力MARK有一个限制。在此界面中的所有型号都通过设计矩阵的方法，而不是通过参数的指数矩阵（PIMS）编码的模型结构。在大多数情况下，Logit或其它连接使用的默认情况下，有能力的影响MARK数数的识别参数（见dipper的一个例子）。然而，开始v1.7.6罪链接现在支持公式的参数指定的形象设计矩阵。
Before you begin, you must have installed MARK (http://www.cnr.colostate.edu/~gwhite/mark/mark.htm) on your computer or at least have a current copy of MARK.EXE. As long as you selected the default location for the MARK install (c:/Program Files/Mark), the RMark library will be able to find it. If for some reason, you chose to install it in a different location, see the note section in mark for instructions on setting the variable MarkPath to specify the path. In addition to installing MARK, you must have installed the RMark library into the R library directory. Once done with those tasks, run R and enter library(RMark) (or put it in your .First function) to attach the library of functions.
在开始之前，你必须安装：MARK（http://www.cnr.colostate.edu/~gwhite /标记/ mark.htm的），您的计算机上，或者至少有一个当前副本的MARK.EXE的。只要你选择MARK安装的默认位置（C :/ Program Files文件/符号），RMark库将能够找到它。如果由于某种原因，你选择安装在不同的位置，请参见注释部分在mark的说明，设置的的变量MarkPath到指定的路径。在除了安装MARK，您必须安装到RRMark库库目录。一旦完成这些任务，运行R库（RMark）的（或把它放在你的第一个功能），并输入附加的函数库。
The following is a categorical listing of the functions in the library with a link to the help for each function. To start, read the help for functions import.chdata and mark to learn how to import your data and fit a simple model. The text files for the examples shown in import.chdata are in the subdirectory data within the R Library directory in RMark. Next look at the example data sets and analyses dipper, edwards.eberhardt, and example.data. After you see the structure of the examples and the use of functions to fit a series of analyses, explore the remaining functions under Model Fitting, Batch Analyses, Model Selection and Summary and Display. If your data and models contain individual covariates, read the section on Real Parameter Computation to learn how to compute estimates of real parameters at various covariate values.
下面是一个绝对的上市的函数库中的每个函数的帮助的链接。要启动，读取功能的帮助import.chdata和mark学习如何将数据导入和适应一个简单的模型。所示的例子中的文本文件，以便在import.chdata内的R图书馆在RMark目录的子目录中的数据。接下来看的示例数据集和分析dipper，edwards.eberhardt和example.data。当你看到的例子和使用功能的结构，以适应一系列的分析，探讨模型拟合，批量分析，模型选择和总结，显示下，其余的职能。如果您的数据和模型包含个人的协变量，阅读部分的实际参数计算，学习如何在不同的协变量值计算的实际参数的估计。
Input/Output data & results
输入/输出数据和效果。
import.chdata,read.mark.binary, extract.mark.output
import.chdata，read.mark.binary，extract.mark.output
Exporting Models to MARK interface
导出模型标记接口
export.chdata, export.model
export.chdata，export.model
Model Fitting
模型拟合
mark, process.data, make.design.data, add.design.data, make.mark.model, run.mark.model merge_design.covariates
mark，process.data，make.design.data，add.design.data，make.mark.model，run.mark.modelmerge_design.covariates
Batch analyses with functions
批与功能分析
run.models, collect.models, create.model.list, mark.wrapper
run.models，collect.models，create.model.list，mark.wrapper
Summary and display
总结和展示
summary.mark, print.mark, print.marklist, get.real, compute.real, print.summary.mark
summary.mark，print.mark，print.marklist，get.real，compute.real，print.summary.mark
Model Selection/Goodness of fit
模式选择/拟合优度
adjust.chat, adjust.parameter.count, model.table , release.gof, model.average
adjust.chat，adjust.parameter.count，model.table，release.gof，model.average
Real Parameter computation
实际参数计算
find.covariates, fill.covariates, compute.real , covariate.predictions
find.covariates，fill.covariates，compute.real，covariate.predictions
Utility and internal functions
实用程序和内部功能
collect.model.names, compute.design.data, extract.mark.output, inverse.link, deriv.inverse.link, setup.model, setup.parameters, valid.parameters, cleanup
collect.model.names，compute.design.data，extract.mark.output，inverse.link，deriv.inverse.link，setup.model，setup.parameters，valid.parameters，cleanup
For examples, see dipper for CJS and POPAN, see example.data for CJS with multiple grouping variables, see edwards.eberhardt for various closed-capture models, see mstrata for Multistrata, and see Blackduck for known fate. The latter two are examples of the use of mark.wrapper for a shortcut approach to creating a series of models. Other examples have been added for the various other models.
有关示例，请参阅dipperCJS和POPAN，看到example.dataCJS与多个分组变量，请参阅edwards.eberhardt各种封闭的捕获模型，请参阅mstrata的多，和看到Blackduck已知的命运。后面的两个例子，一个快捷的方法来创建一个系列的机型使用mark.wrapper。其他的例子已经加入了其他各种车型。
（作者）----------Author(s)----------
Jeff Laake
参考文献----------References----------
Biology, Colorado State University, Fort Collins, Colorado, USA http://www.cnr.colostate.edu/~gwhite/mark/mark.htm
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 2012-9-26 23:37:23
