setOptions(simone)
setOptions()所属R语言包：simone
Low-level options of a SIMoNe run
低级别选项的西蒙娜运行
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function is intended to design low-level uses of SIMoNe by specifying various parameters of the underlying algorithms.
此功能的目的是通过指定的各种参数的底层算法设计低电平用途SIMONE。
用法----------Usage----------
setOptions(normalize = TRUE,
verbose = TRUE,
penalties = NULL,
penalty.min = NULL,
penalty.max = NULL,
n.penalties = 100,
edges.max = Inf,
edges.sym.rule = NULL,
edges.steady = "neighborhood.selection",
edges.coupling = "coopLasso",
clusters.crit = "BIC",
clusters.meth = "bayesian",
clusters.qmin = 2,
clusters.qmax = 4)
参数----------Arguments----------
参数：normalize
logical specifying wether the data should be normalized to unit variance. The normalization is made task-wisely in the multiple sample setting. Default is TRUE.
逻辑指定天气的数据进行归到单位方差。中马关系标准化作出明智的多个样品设置任务。默认是TRUE。
参数：verbose
a logical that indicates verbose mode to display progression. Default is TRUE.
一个逻辑表示详细模式显示进展。默认是TRUE。
参数：penalties
vector of decreasing penalty levels for the network estimation. If NULL (the default), an appropriate vector will be generated in simone with n.penalties entries, starting from penalty.max and shrinked to penalty.min.
矢量的罚则，减少网络估计。如果NULL（默认值），在适当的向量将产生在simonen.penalties项，从penalty.max，收缩到penalty.min。
参数：penalty.min
The minimal value of the penalty that will be tried for network inference. If NULL (the default), it will be set in simone to 1e-5 for the monotask framework and to 1e-2 for the multitask framework.
最小的值将尝试对网络推理的罚款。如果NULL（默认值），它将被设置在simone到1e-5，为monotask框架和1e-2多任务框架。
参数：penalty.max
The maximal value of the penalty that will be tried for network inference. If NULL (the default), it will be set to a value that provoques an empty granph. Default is NULL.
的极大值将尝试对网络推理的刑罚。如果NULL（默认值），它会被设置的值，provoques一个空的granph。默认是NULL。
参数：n.penalties
integer that indicates the number of penalties to put in the penalties vector. Default is 100.
整数，表示数的罚则，把penalties矢量。默认是100。
参数：edges.max
integer giving an upper bound for the number of edges to select: if a network is inferred along the algorithm with a number of edges overstepping edges.max, it will stop there. Default is Inf.
整数，给出一个上限的边数选择：如果一个网络可以推断沿算法与一些边缘超越edges.max，它会停在那里。默认是Inf。
参数：edges.steady
a character string indicating the method to use for the network inference associated to steady-state data, one task framework. Either "graphical.lasso" or "neighborhood.selection". Default is the later.
一个字符串，表示使用到稳定状态的数据，一个任务框架相关联的网络推理的方法。无论是"graphical.lasso"或"neighborhood.selection"。默认是后来的。
参数：edges.coupling
character string (either "coopLasso", "groupLasso" or "intertwined") that indicates the coupling method across task in the multiple sample setup. Defautl is "coopLasso".
字符串（是"coopLasso"，"groupLasso"或"intertwined"），表明整个任务在多个样品设置耦合的方法。 Defautl是"coopLasso"。
参数：edges.sym.rule
character string ("AND", "OR", "NO") for post-symmetrization of the infered networks. Enforced to "NO" for time-course data (directed network) and set to "AND" as default for steady-state data (undirected network).
字符串（"AND"，"OR"，"NO"）后的对称的焦秋生的网络。强迫的时间过程数据（向网络）和"NO"设置为"AND"默认情况下，在稳定的状态数据（无向网络）。
参数：clusters.crit
criterion to select the network that is used to find an underlying clustering. Either "BIC", "AIC" or an integer for the number of edges. Default is "BIC".
选择标准的网络，用于找到一个潜在的聚类。无论哪种"BIC"，"AIC"或的边的数目的整数。默认是"BIC"。
参数：clusters.qmin
minimum number of classes for clustering. Default is 2.
类聚类的最低数量。默认值是2。
参数：clusters.qmax
maximum number of classes for clustering. Default is 4.
类聚类的最大数量。默认值是4。
参数：clusters.meth
character string indicating the strategy used for the estimation: "variational", "classification", or "bayesian". See the mixer package for further details. Default is "bayesian".
字符串表示的策略用于估计："variational"，"classification"或"bayesian"。 mixer包进一步的细节。默认是"bayesian"。
值----------Value----------
A list that contains all the specified parameters.
一个列表，包含所有指定的参数。
注意----------Note----------
If the user specifies its own penalties vector, all the networks inferred during the algorithm will be kept, even if they share the very same number of edges.
如果用户指定自己的penalties向量，在算法中推断出的所有的网络将被保留，即使它们共享相同数量的边缘。
On the other hand, if you only specify penalty.max and/or penalty.min and/or n.penalties, the algorithm will only kept the networks who show different numbers of edges. That is to say, the number of networks stocked in the output of simone generally does not have a length equal to n.penalties.
另一方面，如果你只指定了penalty.max和/或penalty.min和/或n.penalties，该算法将只保留了网络的边缘显示不同的数字。也就是说，放养的输出中的网络的数量simone通常不具有的长度等于n.penalties。
（作者）----------Author(s)----------
J. Chiquet
参见----------See Also----------
simone.
simone。
实例----------Examples----------
## generate an object (list) with the default parameters[＃生成一个对象（list）的默认参数]
setOptions()
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 02:52:02
