flowFPModel(flowFP)
flowFPModel()所属R语言包：flowFP
Fingerprint model constructor
指纹模型构造
译者：生物统计家园网 机器人LoveR
描述----------Description----------
A constructor for objects of type flowFPModel-class.
一个类型flowFPModel级对象的构造。
用法----------Usage----------
flowFPModel(fcs, name="Default Model", parameters=NULL, nRecursions="auto",
dequantize=TRUE, sampleSize=NULL, excludeTime=TRUE)
参数----------Arguments----------
参数：fcs
Training data for model, either a flowFrame or flowSet.
训练数据模型，无论是flowFrame或flowSet。
参数：parameters
A vector of parameters to be considered during model construction.
模型的建立过程中要考虑的一个参数向量。
参数：nRecursions
Number of times the FCS training data will be subdivided. Each level generated 2^nRecursions bins. A warning will be generated if the number of expected events in each bin is < 1. (e.g. if your training set had 1000 events, and you specified level=10.)
FCS的训练数据将被细分倍。每个级别产生2^nRecursions箱。将生成一个警告，如果在每个容器的预期事件的数量<1。 （例如，如果你的训练集有1000个事件，您指定的level=10。）
参数：dequantize
If TRUE, all of the events in the training set will be made unique by adding a tiny value (proportional to the ordinal position of each event) to the data.
如果为TRUE，在训练集的所有事件将通过独特的数据加入一个很小的值（每个事件的序号位置成正比）。
参数：sampleSize
Used to specify the per-flowFrame sample size of the data to use in model generation. If NULL, all of the data in x is used. Setting this to a smaller number will speed up processing, at the cost of accuracy.
用于指定使用模型生成的数据每个-flowFrame的样本大小。如果为NULL，所有的数据在x。设置一个较小的数字，将加快处理速度，在成本的准确性。
参数：name
A descriptive name assigned to the model.
分配给模型一个描述性的名称。
参数：excludeTime
If TRUE (default) and no parameters are specified, the model constructor will atempt to exclude the time parameter from consideration when building the model. The time parameter is identified as have the label 'time', matched ignoring case.
如果指定True（默认）和无参数，模型构造atempt建立模型时，排除考虑的时间参数。时间参数标识标签时间，忽略大小写匹配。
Details
详情----------Details----------
This function is used to create a flowFPModel, which can then be used to create a set of fingerprints using flowFP. A model is a representation of the multivariate probability density function for the training set x. This representation is in the form of a set of bins, each of which contains (nearly) the same number of events in the training set. Thus, the model can be regarded as a multivariate histogram of x with fixed bin count and variable bin size. Bins will be smallest in regions of high density, and largest in regions that are sparsely populated.
此函数用于创建一个flowFPModel，然后可以用来对一套使用flowFP指纹创建。一个模型是一个多变量的概率密度函数为训练集X表示。这表示是在一箱的形式，其中每个都包含（几乎）相同数量的事件在训练集。因此，该模型可以被视为与固定槽数和可变斌大小x的直方图多元。箱在高密度，最大的区域，在人口稀少的区域将是最小的。
Bins are constructed by recursively subdividing the multivariate space specified by parameters. At the first level, the entire space is divided in half in the direction of the parameter with the highest variance. At the next level, each of the halves from the first level is itself divided in half, again in the direction of the parameter whose variance (among the events in the parent bin) is the highest. Thus, for each level the number of resulting bins is doubled. The number of bins in the model is thus 2^nRecursions.
箱构造递归细分参数所指定的多元空间。在第一个层次，整个空间被分为两半，在最高的方差参数的方向。在一个新的水平，从第一级的半是自己分成两半，再在参数的方差（之间父斌事件）是最高的方向。因此，每个级别导致垃圾箱的数量增加了一倍。因此，在模型箱的数量是2^nRecursions。
A model is of little interest by itself. Its only utility is to provide a sort of basis function for forming fingerprints. It may be interesting in some cases to use the entire set x to build the model, and then to generate fingerprints for each instance in x. For this reason, calling flowFP without a model causes flowFPModel to be invoked silently under the hood in order to create a model of the flowSet provided to flowFP.
模型是由本身的兴趣不大。其唯一的效用是提供一个排序的基础上形成指纹的功能。在某些情况下，它可能是有趣的使用整套x来建立模型，然后以x中的每个实例生成指纹。对于这个原因，要求无模型flowFP的原因flowFPModel引擎盖下默默地被调用以创建一个提供到flowFP flowSet的模型。
值----------Value----------
An object of type flowFPModel is returned.
返回对象的类型flowFPModel。
注意----------Note----------
If a flowSet is provided as input, it is internally collapsed to a flowFrame for binning.
，如果flowSet作为输入提供的，它内部崩溃一个分级flowFrame。
作者（S）----------Author(s)----------
Herb Holyst <<a href="mailto:holyst@mail.med.upenn.edu">holyst@mail.med.upenn.edu</a>>, Wade Rogers <<a href="mailto:rogersw@mail.med.upenn.edu">rogersw@mail.med.upenn.edu</a>>
参考文献----------References----------
Multivariate Distribution Differences, Cytometry 45, 47-55.
Multivariate Distributions, Cytometry Part A 73, 430-441.
参见----------See Also----------
flowFP, flowFPModel
flowFP，flowFPModel
举例----------Examples----------
# load a flowSet to use as an example.[加载flowSet使用作为一个例子。]
library(flowFP)
data(fs1)
fs1
model <- flowFPModel(fs1, parameters=c(4,5), nRecursions=6)
fp <- flowFP(fs1, model)
plot(fp)
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。
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发表于 2012-2-25 17:52:44
