stability.sim(Rtreemix)
stability.sim()所属R语言包：Rtreemix
Stability analysis of the mutagenetic trees mixture model
致突变的树木混合模型的稳定性分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function includes stability analysis on different levels of the mutagenetic trees mixture model: GPS values, encoded probability distribution, tree topologies. Each analysis contains the values of different similarity measures with their corresponding p-values.
功能包括在不同层面上，致突变的树木混合模型的稳定性分析：GPS值，编码的概率分布，树的拓扑结构。每个分析都包含不同的相似性措施，其相应的p值的值。
用法----------Usage----------
stability.sim(no.trees = 3, no.events = 9, prob = c(0.2, 0.8),
no.draws = 300, no.rands = 100, no.sim = 1)
参数----------Arguments----------
参数：no.trees
An integer larger than 2 giving the number of tree components of the mixture models considered in the stability analysis. The default value is 3.
一个integer大于树组件中的稳定性分析认为混合模型2。默认值是3。
参数：no.events
An integer larger than 0 giving the number of genetic events of the mixture models considered in the stability analysis.
一个integer大于0稳定性分析中考虑了混合模型的遗传事件的数量。
参数：prob
A numeric vector of length 2 specifying the boundaries for the edge weights of the randomly generated trees. The first component of the vector (the lower boundary) must be smaller than the second component (the upper boundary). The default value is (0.2, 0.8).
一个numeric向量长度为2指定为随机生成的树的边权的界限。向量的第一个组件（下边界）必须小于第二部分（上边界）。默认值为（0.2，0.8）。
参数：no.draws
An integer larger than 0 giving the size of the data sample drawn from the random models used for learning the mixture models. The default value is 300.
integer大于0给予用于学习混合模型的随机模型得出的数据样本的大小。默认值是300。
参数：no.rands
An integer larger than 0 specifying the number of random models used for calculating the p-values. The default value is 100.
integer大于0指定用来计算p值的随机模型。默认值是100。
参数：no.sim
An integer larger than 0 specifying the number of iterations used for the waiting time simulations (a part of the GPS calculation). The default value is 1.
integer大于0指定用于等候时间模拟（的GPS计算的一部分）的迭代数量。默认值是1。
Details
详情----------Details----------
The stability analysis is performed by first drawing a true mixture model uniformly at random from the model space, and drawing a data sample from it. Afterwards, a mutagenetic trees model is fitted to the drawn sample. The quality of the features derived from the model is then assessed by comparing its quality with the quality of the corresponding features of a sufficient number of random mixture models sampled uniformly from the model space. A p-value is obtained as a percentage of cases in which the true model is closer to a random model tnah to the fitted model.
先画一个真正的混合模型，从模型空间中随机均匀，从一个数据样本进行了稳定性分析。事后，致突变模型树装有绘制的样本。从模型中派生的功能质量，然后比较其质量与相应的功能足够数量的随机混合模型，从模型空间均匀采样的质量评估。 p值，得到一个百分比例中的真实模型更接近随机模型tnah的拟合模型。
值----------Value----------
参数：comp1
Results from the stability analysis of the GPS values derived from a fitted mixture model. A matrix with 4 columns and no.sim rows. The first two columns give the similarity values and their corresponding p-values when the Euclidian distance is used as a similarity measure for comparing the respective GPS vectors. The last two columns depict the same results, but with the rank correlation distance used as a similarity measure.
从一个装有混合模型得出的GPS值稳定性分析的结果。一个matrix4列和no.sim行。前两列给出相似的价值观和其相应的P-值时，欧氏距离作为相似性度量用于比较各自的GPS向量。最后两列描绘出相同的结果，但与排名相关距离作为相似性度量。
参数：comp2
Results from the stability analysis of the probability distributions induced by a fitted mixture model. A matrix with 6 columns and no.sim rows. Each two columns give the values of the comparissons between the true and the fitted probability distributions and their corresponding p-values, when using the cosine distance, the L1 distance, and the Kullback-Leibler divergence as similarity measures.
从一个装有混合模型产生的概率分布的稳定分析的结果。一个matrix6列和no.sim行。每两列给comparissons真实和拟合概率分布和其相应的P-值之间的值，当使用余弦距离，L1的距离，并作为相似措施的Kullback-Leibler距离。
参数：comp3
Results from the stability analysis of the topologies of the tree components of a fitted mixture model. A matrix with 2 columns and no.sim rows that give the value of the comparisson of the topologies between the true and the corresponding fitted model and their p-values. The similarity measure underlying the number of different edges was used.
拓扑结构的一个装有混合模型的树组件的稳定性分析结果。一个matrix2列no.sim行提供的价值之间的真正的和相应的拟合模型和p-值拓扑comparisson的与。相似性度量的基本不同的边缘。
参数：comp4
Similar to comp3. However, the similarity measure for comparing the tree topologies besides the number of distinct edges includes the L1 distances of the level vectors of events. See get.tree.levels.
类似comp3。然而，比较树的相似性测度的拓扑结构，除了一些明显的边缘，包括事件的水平向量的L1距离。看到get.tree.levels。
参数：comp5
A matrix where the columns correspond to the true GPS vector from each simulation iteration. The matrix has no.sim columns and no.draws rows.
一个matrix列对应的真正的GPS每次模拟迭代向量。矩阵no.sim列no.draws行。
参数：comp6
Same as comp5, but the matrix contains the fitted GPS values from each simulation iteration.
comp5，但矩阵同样包含从每个模拟迭代的GPS拟合值。
参数：comp7
A list where each component corresponds to the true models generated in each simulation iteration. the length of the list is no.sim.
一个list每个组件对应在每个模拟迭代产生的真正模型。列表的长度是no.sim。
参数：comp8
Same as comp7, but the list contains the fitted models.
comp7，但该名单同样包含拟合模型。
注意----------Note----------
The stability simulation examples are time consuming. They are commented out because of the time restrictions of the check of the package. For trying out the code please copy it and uncomment it.
稳定仿真的例子是费时。他们注释掉，因为包检查的时间限制。试图代码请复制并取消它。
作者（S）----------Author(s)----------
Jasmina Bogojeska
参考文献----------References----------
data, N. Beerenwinkel et al.; Estimating cancer survival and clinical outcome based on
参见----------See Also----------
RtreemixData-class, RtreemixModel-class, RtreemixGPS-class, RtreemixStats-class, fit-methods, gps-methods, distribution-methods, generate-methods, sim-methods, L1.dist, Pval.dist, comp.models, comp.trees, get.tree.levels, kullback.leibler
RtreemixData-class，RtreemixModel-class，RtreemixGPS-class，RtreemixStats-class，fit-methods，gps-methods，distribution-methods，generate-methods，< x>，sim-methods，L1.dist，Pval.dist，comp.models，comp.trees，get.tree.levels
举例----------Examples----------
## Stability analysis - a toy example[＃稳定性分析 - 一个玩具的例子]
#stability.sim(no.trees = 3, no.rands = 5, no.sim = 4, no.draws = 300)[stability.sim（no.trees = 3，no.rands = 5，no.sim = 4，no.draws = 300）]
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。
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发表于 2012-2-26 13:44:44
