estimate.model(zipfR)
estimate.model()所属R语言包：zipfR
Estimate LNRE Model Parameters (zipfR)
的估计LNRE模型参数（zipfR）
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Internal function: Generic method for estimation of LNRE model parameters. Based on the class of its first argument, the method dispatches to a suitable implementation of the estimation procedure.
内部功能：通用LNRE模型参数估计的方法。基于它的第一个参数的类，方法分派到一个合适的实现估计过程。
Unless you are a developer working on the zipfR source code, you are probably looking for the lnre manpage.
除非你是zipfR源代码的开发人员，你可能看lnre联机帮助。
用法----------Usage----------
estimate.model(model, spc, param.names,
method, cost.function, m.max=15,
debug=FALSE, ...)
参数----------Arguments----------
参数：model
LNRE model object of the appropriate class (a subclass of lnre). All parameters of the LNRE model that are not listed in param.names must have been initialized to their prespecified values in the model object.
LNRE模型对象的适当的类（子lnre）。所有参数的LNRE模型中未列出的param.names必须被初始化为预先设定的值，在model对象。
参数：spc
an observed frequency spectrum, i.e. an object of class spc. The values of the missing parameters will be estimated from this frequency spectrum.
观测到的频谱，即的对象类spc。缺少的参数的值，将被估计从该频谱。
参数：param.names
a character vector giving the names of parameters for which values have to be estimated ("missing" parameters)
给其值必须估计参数的名称（“失踪”的参数字符向量）
参数：method
name of the minimization algorithm used for parameter estimation (see lnre for details)
用于参数估计的最小化算法的名称（见lnre的详细信息）
参数：cost.function
cost function to be minimized (see lnre for details). NB: this is a direct reference to the function object rather than just the name of the cost function. Look-up of the appropriate cost function implementation is performed in the lnre constructor.
成本函数最小化（见lnre详情）。注：这是一个直接引用的函数对象，而不仅仅是成本函数的名称。查找适当的成本函数的实现是在lnre构造。
参数：m.max
number of spectrum elements that will be used to compute the cost function (passed on to cost.function)
将用于计算的成本函数的谱的元素数量的（通过cost.function）
参数：debug
if TRUE, some debugging and progress information will be printed during the estimation procedure
如果TRUE，一些调试和进步的信息将被打印在评估过程
参数：...
additional arguments are passed on and may be used by some implementations
额外的参数传递，并可能会被一些实现
Details
详细信息----------Details----------
By default, estimate.model dispatches to a generic implementation of the estimation procedure that can be used with all types of LNRE models (estimate.model.lnre).
默认情况下，estimate.model分派到一个通用的实施程序，可以使用与所有类型的LNRE模型（estimate.model.lnre）的估计。
This generic implementation can be overridden for specific LNRE models, e.g. to calculate better init values or improve the estimation procedure in some other way. To provide a custom implementation for Zipf-Mandelbrot models (of class lnre.zm), for instance, it is sufficient to define the corresponding method implementation estimate.model.lnre.zm. If no custom implementation is provided but the user has selected the Custom method (which is the default), estimate.model falls back on Nelder-Mead for multi-dimensional minimization and NLM for one-dimensional minimization (where Nelder-Mead is considered to be unreliable).
这个通用的实施，可以覆盖为特定的LNRE模式，例如：更好的初始化值来计算，或以某种其他方式改善估计过程。为了提供一个自定义实现的齐普夫曼德尔布罗模型（类lnre.zm），例如，定义相应的方法实现estimate.model.lnre.zm就足够。如果没有自定义的实现提供，但用户选择了Custom方法（这是默认值），estimate.model回落Nelder-Mead多维最小化和NLM一维的最小化（其中内尔德米德认为是不可靠的）。
Parmeter estimation is performed by minimization of the cost function passed in the cost.function argument (see lnre for details). Depending on the method argument, a range of different minimization algorithms can be used (see lnre for a complete listing). The minimization algorithm always operates on transformed parameter values, making use of the transform utility provided by LNRE models (see lnre.details for more information about utility functions). All parameters are initialized to 0 in the transformed scale, which should translate to sensible starting points.
Parmeter估计是通过最小化成本函数在cost.function参数传递（见lnre）。根据method参数，不同的最小化算法可以使用（见lnre的完整列表）。最小化算法总是变换的参数值，transformLNRE模型（见lnre.details实用功能的更多信息）实用程序提供了使用。所有参数都被初始化为0，在转化的规模，这应翻译到明智的起点。
Note that the estimate.model implementations do not perform any error checking. It is the responsibility of the caller to make sure that the arguments are sensible and complete. In particular, all model parameters that will not be estimated (i.e. are not listed in param.names) must have been initialized to their prespecified values in the model passed to the function.
需要注意的是estimate.model实现不执行任何错误检查。它是调用者的责任，以确保该参数是明智的和完整的。特别是，所有将无法估计的模型参数（即未列出的param.names）必须被初始化为预先设定的值在model传递给函数。
值----------Value----------
A modified version of model, where the missing parameters listed in param.names have been estimated from the observed frequency spectrum spc. In addition, goodness-of-fit information is added to the object.
修改后的版本model，缺少的参数中列出param.names已经从观测到的频谱spc估计。此外，配合善信息被添加到该对象。
参见----------See Also----------
The user-level function for estimating LNRE models is lnre. Its manpage also lists available cost functions and minimization algorithms.
的用户级功能估计LNRE模型的是lnre。它的手册页还列出了可用的成本函数和最小化算法。
The internal structure of lnre objects (representing LNRE models) is described on the lnre.details manpage, which also outlines the necessary steps for implementing a new LNRE model.
的内部结构lnre：对象（占LNRE模型）lnre.details手册页，其中还列出了必要的步骤来实施一个新的LNRE模型。
The minimization algorithms used are described in detail on the nlm and optim manpages from R's standard library.
所使用的最小化算法中详细描述了nlm和optim R的标准库的联机帮助页。
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 2012-10-2 07:47:08
