simone(simone)
simone()所属R语言包：simone
SIMoNe algorithm for network inference
西蒙娜网络推理算法
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The simone function offers an interface to infer networks based on partial correlation coefficients in various contexts and methods (steady-state data, time-course data, multiple sample setup, clustering prior)
simone功能提供了一个接口来推断网络的基础上偏相关系数在不同的场合和方法（稳态数据，时间过程数据，设置多个样品，聚类前）
用法----------Usage----------
simone(X,
type = "steady-state",
clustering = FALSE,
tasks = factor(rep(1, nrow(X))),
control = setOptions())
参数----------Arguments----------
参数：X
a n x p matrix of data, typically n expression levels associated to the same p genes. Can also be a data.frame with n entries, each column corresponding to a variable (a gene). Specifying colnames to X may be convenient in view of results analysis, since it will be used to annotate the plots. Note that this is the only required argument.
一个n x p数据矩阵，通常n相同p基因的表达水平。也可以是data.frame与n项，每一列对应一个变量（基因）。指定colnames到X可能是方便，结果分析，因为它会被用来注释的图。请注意，这是唯一需要的参数。
参数：type
a character string indicating the data specification (either "steady-state" or "time-course" data). Default is "steady-state".
一个字符串，表示数据规范（无论是"steady-state"或"time-course"数据）。默认是"steady-state"。
参数：clustering
a logical indicating if the network inference should be perfomed by penalizing the edges according to a latent clustering discovered during the network structure recovery. Default is FALSE.
一个逻辑，表示如果网络推理，应perfomed受到惩罚的边缘，根据聚类的网络结构恢复过程中发现的一个潜在的。默认是FALSE。
参数：tasks
A factor with n entries indicating the task belonging for each observation in the multiple sample framework. Default is factor(rep(1, nrow(X))), that is, all observations come from a unique homogeneous sample.
n条目表示任务属于每个观测多个样品框架的一个因素。默认值是的factor(rep(1, nrow(X)))，那就是，所有观测都从一个独特的均匀的样品。
参数：control
A list that is used to specify low-level options for the algorithm, defined through the setOptions function.
的列表是用于以指定的算法，通过setOptions函数定义的低级别的选项。
Details
详细信息----------Details----------
Any inference method available ("neighborhood selection", "graphical-Lasso", "VAR(1) inference" and "multitask learning" - see simone-package) relies on an optimization problem under the general form <center> Θ<sub>hat</sub> (λ) = argmax<sub>Θ</sub> L(Θ; data) - λ * pen<sub>l1</sub>(Θ, Z), </center> where L is the log-likelihood of the model (pseudo log-likelihood for "neighborhood selection") and λ is a penalty parameter which controls the sparsity level of the network. The p x p matrix Θ describes the parameters (basically, the edges) of the model, while Z represents a latent clustering which is also estimated when the argument clustering is set to TRUE.
任何推理方法（“邻里选择”，“图形”套索“，的”VAR（1）推断“和”多任务学习“ - simone-package）依赖于一个优化问题的一般形式<CENTER>下Θ <SUB>帽子</ sub>的（λ）= argmax <SUB>Θ</子> L（Θ;数据） - λ*笔<SUB> L1 </子>（Θ，Z）， </ CENTER>L是该模型的对数似然（伪对数似然为“邻里选择”），λ是惩罚参数控制的稀疏的网络。 p x p矩阵Θ的参数（基本上是边）的模式，而Z估计也是一个潜在的聚类参数clustering时，设置为<X >。
The model and the penalty function pen<sub>l1</sub> differ according to the context (steady-state/time-course data, multitask learning and its associated coupling effect). For further details on the models, please check the papers listed in the reference section of simone-package.
模型和罚函数笔<SUB> L1 </ sub>的根据不同的上下文（steady-state/time-course数据，多任务学习及其相关的耦合效应）。模型上的进一步详情，请检查文件中列出的参考部分simone-package。
The criterion displayed during a SIMoNe run is the value of the penalized likelihood for the current values of the estimor Θ<sub>hat</sub>(λ) corresponding to a given value of the overall penalty level λ.
SIMONE运行过程中显示的标准是惩罚项的似然值的电流值的estimorΘ<SUB>帽子</ sub>的（λ）对应的整体的罚款水平λ的给定值。
The following information criteria are also computed for any value of λ and part of the output of simone. The BIC (Bayesian Information Criterion)
下面的信息的准则也被算出的任何值的λ和的simone的输出的一部分。 BIC（贝叶斯信息准则）
<center> BIC(λ) = L(Θ<sub>hat</sub>(λ); data) - df(Θ<sub>hat</sub>(λ)) log(n)/2, </center> and the AIC (Akaike Information Criterion) <center> AIC(λ) = L(Θ<sub>hat</sub>(λ); data) - df(Θ<sub>hat</sub>(λ)) . </center>
<CENTER>的BIC（λ）= L（Θ<SUB>帽子</ SUB>（λ）;数据） - DF（Θ<SUB>帽子</ SUB>（λ））的log（n） / 2，</ CENTER> AIC（赤池信息准则）<CENTER> AIC（λ）= L（Θ<SUB>帽子</ SUB>（λ）;数据） - DF（Θ<子>帽子</ sub>的（λ））。 </ CENTER>
值----------Value----------
Returns an object of class simone, which is list-like and contains the following:
返回对象类simone，这是列表，包含以下内容：
<table summary="R valueblock"> <tr valign="top"><td>networks</td> <td> a list with all the inferred networks stocked as adjacency matrices (the successive values of Θ controled by the penalty level λ). In the multiple sample setup, each element of the list is a list with as many entries as samples or levels in tasks. </td></tr>
<table summary="R valueblock"> <tr valign="top"> <TD> networks</ TD> <td>一个列表中的所有推断网络的邻接矩阵（连续值Θ库存基于PLC控制的罚款λ）。在多个样品的设置，列表中的每个元素是与尽可能多的项目作为样本或水平tasks的列表。 </ TD> </ TR>
<tr valign="top"><td>penalties</td> <td> a vector of the same length as networks, containing the successive values of the penalty level. </td></tr>
<tr valign="top"> <TD> penalties </ TD> <TD>的向量相同长度的networks，包含的连续值的罚款。 </ TD> </ TR>
<tr valign="top"><td>n.edges</td> <td> a vector of the same length as networks, containing the successive numbers of edges in the inferred networks. In the multiple sample setup, n.edges is a matrix with as many columns as levels in tasks. </td></tr>
<tr valign="top"> <TD>n.edges</ TD> <td>一个相同的长度的矢量networks，在推断出的网络中包含的边缘的连续号码的。在多个样品设置，n.edges多列水平tasks是一个矩阵。 </ TD> </ TR>
<tr valign="top"><td>BIC</td> <td> a vector of the same length as networks, containing the value of the BIC for the successively estimated networks. </td></tr>
<tr valign="top"> <TD>BIC</ TD> <td>一个相同的长度的矢量networks，包含的值的连续估计网络的BIC。 </ TD> </ TR>
<tr valign="top"><td>AIC</td> <td> a vector of the same length as networks, containing the value of the AIC for the successively estimated networks. </td></tr>
<tr valign="top"> <TD>AIC</ TD> <td>一个相同的长度的矢量networks，含有依次估计网络的AIC的值。 </ TD> </ TR>
<tr valign="top"><td>clusters</td> <td> a size-p factor indicating the class of each variable. </td></tr>
<tr valign="top"> <TD> clusters </ TD> <TD>的大小p表示每个变量之类的因素。 </ TD> </ TR>
<tr valign="top"><td>weights</td> <td> a pxp matrix of weigths used to adapt the penalty to each entry of the Theta matrix. It is inferred through the algorithm according to the latent clustering of the network. When clustering is set to FALSE, all the weights are equal to "1", which mean no adaptive penalization. </td></tr>
<tr valign="top"> <TD> weights </ TD> <td>一个pxp矩阵的weigths适应的刑罚Theta矩阵的每个条目。据推断通过算法根据潜聚类网络。当clustering设置为FALSE，所有的权重都等于“1”，这意味着没有自适应惩罚。 </ TD> </ TR>
<tr valign="top"><td>control</td> <td> a list describing all the posterior values of the parameters used by the algorithm, to compare with the one set by the setOptions function. As a matter of fact, many of the options are defined depending on the nature of the data and can be automatically corrected during internal checks of the coherence of desired options to the characteristics of the data. </td></tr>
<tr valign="top"> <TD> control </ TD> <td>一个列表，描述后所使用的算法的参数值，进行比较的一组setOptions 函数。事实上，许多选项被定义的数据的性质取决于在内部检查所需的选项的特性的数据的连贯性，可以自动校正。 </ TD> </ TR>
</table>
</ TABLE>
注意----------Note----------
If nothing particular is specified about the penalty through the control list (see setOptions), the default is to start from a value of λ that ensures an empty network. Then λ is progressively shrinked, as close to zero as possible. Along the shrinkage of λ, only networks with different numbers of edges are kept in the final output.
如果没有什么特别的处罚规定通过control名单（见setOptions），默认的是从λ值，以确保空的网络。然后，λ是逐渐收缩的，尽可能接近零。沿收缩率λ，只有网络与不同数量的边缘保持在最终输出中。
（作者）----------Author(s)----------
J. Chiquet
参见----------See Also----------
setOptions, plot.simone, cancer and demo(package="simone").
setOptions，plot.simone，cancer和demo(package="simone")。
实例----------Examples----------
## load the breast cancer data set[＃加载乳腺癌数据集]
data(cancer)
attach(cancer)
## launch simone with the default parameters and plot results[推出西蒙娜的默认参数和图的结果]
plot(simone(expr))
## try with clustering now (clustering is achieved on a 30-edges network)[＃尝试与聚类（聚类上实现30-边缘网络）]
plot(simone(expr, clustering=TRUE, control=setOptions(clusters.crit=30)))
## try the multiple sample[＃尝试多个样品]
plot(simone(expr, tasks=status))
detach(cancer)
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 2012-9-30 02:52:16
