R语言 EBarrays包 ebarraysFamily-class()函数中文帮助文档(中英文对照)

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ebarraysFamily-class(EBarrays)
ebarraysFamily-class()所属R语言包：EBarrays

                                        Class of Families to be used in the EBarrays package
                                         家庭类在EBarrays包

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Objects used as family in the emfit function.
对象为家庭使用emfit功能。

The package contains three functions that create such objects for the three most commonly used families, Gamma-Gamma, Lognormal-Normal and Lognormal-Normal with modified variances. Users may create their own  families as well.
该软件包包含三个功能，最常用的三个家庭，伽马 - 伽马，对数正态正常和修改方差的对数正态分布师范大学创建这样的对象。用户可以创建自己的家庭，以及。


用法----------Usage----------


eb.createFamilyGG()
eb.createFamilyLNN()
eb.createFamilyLNNMV()



Details

详情----------Details----------

The emfit function can potentially fit models corresponding to several different Bayesian conjugate families. This is specified as the family argument, which ultimately has to be an object of formal class “ebarraysFamily” with some specific slots that determine the behavior of the "family".
emfit函数可能适合机型对应几个不同的贝叶斯共轭家庭。这被指定为family的说法，这最终成为一个正式的课堂“ebarraysFamily”与一些特定的插槽，确定的“家庭”的行为对象。

For users who are content to use the predefined GG, LNN and LNNMV models, no further details than that given in the documentation for emfit are necessary. If you wish to create your own families, read on.
对于用户使用预定义的GG，LNN型和LNNMV模型，没有进一步的细节比emfit是必要的文件中的内容。如果你想创建自己的家庭，阅读。


值----------Value----------

Objects of class “ebarraysFamily” for the three predefined families Gamma-Gamma , Lognormal-Normal and Lognormal-Normal with modified  variances.
对象类的“ebarraysFamily”三个预定义的家庭伽马 - 伽马，对数正态正常和与修改方差的对数正态分布的正常的。


类的对象----------Objects from the Class----------

Objects of class “ebarraysFamily” can be created by calls of the form new("ebarraysFamily", ...). Predefined objects corresponding to the GG, LNN and LNNMV models can be created by eb.createFamilyGG() , eb.createFamilyLNN() and eb.createFamilyLNNMV(). The same effect is achieved by coercing from the strings "GG", "LNN" and "LNNMV" by as("GG", "ebarraysFamily"), as("LNN",   "ebarraysFamily") and as("LNNMV", "ebarraysFamily").
可以创建对象类“ebarraysFamily”检测形式new("ebarraysFamily", ...)。预定义的对象对应的GG，LNN型和LNNMV模型可以创建由eb.createFamilyGG()，eb.createFamilyLNN()和eb.createFamilyLNNMV()。从字符串胁迫取得同样的效果"GG"，"LNN"和"LNNMV"由as("GG", "ebarraysFamily")，as("LNN",   "ebarraysFamily")和as("LNNMV", "ebarraysFamily")。


插槽----------Slots----------

An object of class “ebarraysFamily” extends the class "character" (representing a short hand name for the class) and should have the following slots (for more details see the source code):
一个类“ebarraysFamily”的对象延伸类的"character"（代表一个类的短手的名字），并应具备以下插槽（详细内容见源代码）：

A not too long character string describing the family
一个不太长的字符串描述家庭

function that maps user-visible parameters to the parametrization that would be used in the optimization step (e.g. log(sigma^2) for LNN). This allows the user to think in terms of familiar parametrization that may not necessarily be the best when optimizing w.r.t. those parameters.
功能，图用户可见的参数的参数化，将在优化步骤（如log(sigma^2)LNN型）。这使用户能够在熟悉的参数化方面认为，不一定是最好的优化时WRT这些参数。

inverse of the link function
反链接功能

function of a single argument data (matrix containing raw expression values), that calculates and returns as a numeric vector initial estimates of the parameters (in the parametrization used for optimization)
功能单一的参数data（矩阵包含原始表达式的值），计算并返回一个数值向量的初始参数估计（用于优化参数化）

function taking arguments theta and a list called args. f0 calculates the negative log likelihood at the given parameter value theta (again, in the parametrization used for optimization). This is called from emfit. When called, only genes with positive intensities across all samples are used.
函数参数theta和一个列表叫做args。 f0计算在给定的参数值theta（同样，用于优化参数化）的负面记录的可能性。这就是所谓的从emfit。当被调用时，只有积极的强度在所有样品的基因。

f0.pp is essentially the same as f0 except the terms common to the numerator and denominator when calculating posterior odds may be removed. It is called from postprob.
f0.pp基本上f0常见的分子和分母计算后的赔率时可能会被删除的条款除外。它被称为postprob。

function that takes arguments data, patterns (of class “ebarraysPatterns”) and groupid (for LNNMV family only) and returns a list with two components, common.args and pattern.args. common.args is a list of arguments to f0 that don't change from one pattern to another, whereas pattern.args[[i]][[j]] is a similar list of arguments, but specific to the columns in pattern[[i]][[j]]. Eventually, the two components will be combined for each pattern and used as the args argument to f0.
带参数的函数data，patterns（类“ebarraysPatterns”）和groupid（只为LNNMV家庭）和返回两个组件列表中，common.args和 pattern.args。 common.args是f0不改变从一个图案到另一个，而pattern.args[[i]][[j]]是一个类似的参数列表，但具体到列pattern[[i]][[j]]参数列表。最终，这两个组件将被合并为每个模式作为args参数f0使用。

function of two arguments x (data vector, containing log expressions) and theta (parameters in user-visible parametrization). Returns log marginal density of the natural log of intensity for the corresponding theoretical model. Used in plotMarginal
两个参数的函数x（数据向量，包含log表达式）和theta（用户可见的参数化的参数）。返回登录相应的理论模型强度的自然对数的边际密度。使用plotMarginal

vector of lower bounds for the argument theta of f0. Used in optim
向量参数theta下限f0。使用optim

vector of upper bounds for the argument theta of f0.
向量参数theta上限f0。


作者（S）----------Author(s)----------


Ming Yuan, Ping Wang, Deepayan Sarkar, Michael Newton, and Christina Kendziorski



参考文献----------References----------

On differential variability of expression ratios: Improving statistical inference about gene expression changes from microarray data. Journal of Computational Biology 8:37-52.
On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles. Statistics in Medicine 22:3899-3914.
Parametric Empirical Bayes Methods for Microarrays in The analysis of gene expression data: methods and software. Eds. G. Parmigiani, E.S. Garrett, R. Irizarry and S.L. Zeger, New York: Springer Verlag, 2003.
Detecting differential gene expression with a semiparametric hierarchical mixture model. Biostatistics 5: 155-176.
gene clustering and differential expression identification. Biometrics 62(4): 1089-1098.

参见----------See Also----------

emfit, optim, plotMarginal
emfit，optim，plotMarginal


举例----------Examples----------


show(eb.createFamilyGG())
show(eb.createFamilyLNN())
show(eb.createFamilyLNNMV())

转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


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