fit-methods(Rtreemix)
fit-methods()所属R语言包：Rtreemix
Method for fitting mutagenetic trees mixture model to a given dataset
装修致突变树木混合模型的一个给定的数据集的方法
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Function for fitting a mutagenetic trees mixture model to a given dataset data. The dataset and the number of trees K have to be specified. The function estimates K-oncogenetic trees mixture model from the specified data by using an EM-like learning algorithm. The first tree component of the model has a star topology and is referred to as the noise component.
函数拟合致突变树木混合模型到一个给定的数据集data。 DataSet和树木的数量K必须指定。该函数从指定的数据使用EM - 样的学习算法的K-oncogenetic树木混合模型估计。第一棵树的模型组件有一个星型拓扑结构，被称为噪声成分。
用法----------Usage----------
fit(data, K, ...)
参数----------Arguments----------
参数：data
An RtreemixData object giving the dataset used for learning the trees mixture model.
RtreemixData对象提供学习的树木混合模型使用的数据集。
参数：K
An integer larger than 0 specifying the number of branchings in the mixture model.
integer大于0指定的分枝的混合模型。
参数：...
no.start.sol is an integer larger than 0 specifying the number of starting solutions for the k-means algorithm. The default value is 100. eps is a numeric giving the minimum conditional probability to include edge. The default value is 0.01. weighing is a logical specifying whether to use special weights log(Pr(v)) for the edges (root, v). The default value is FALSE. equal.edgeweights is a logical specifying whether to use equal edge weights in the noise component. The default value is TRUE. When you have few data samples always use its default value (TRUE) to ensure nonzero probabilities for all possible patterns (sets of events). seed is a positive integer specifying the random generator seed. The default value is (-1) and then the time is used as a random generator. noise is a logical indicating the presence of a noise (star) component in the fitted mixture model. It is mostly relevant for models with a single tree component, since it is assumed that mixture models with at least two components always have the noise as a first component.
no.start.sol是integer大于0指定的k-means算法的初始解决方案的数量。默认值是100。 eps是numeric给予最低的条件概率，包括边缘。默认值是0.01。 weighing是logical指定是否使用边特别重的log（PR（V））（根，V）。默认值是FALSE。 equal.edgeweights是logical指定是否使用噪声分量相等的边权重。默认值是TRUE。当你有几个数据样本始终使用其默认值（TRUE），以确保所有可能的模式（事件集）非零概率。 seed是一个积极的integer指定的随机生成器的种子。默认值是（-1），然后使用时间作为随机数发生器。 noise是logical表明存在噪音（星）在拟合混合模型组件。它主要是用一个单一的树组件模型有关，因为它被认为至少有两个组件的混合模型总是有作为第一个组件的噪声。
Details
详情----------Details----------
When K = 1 and noise = FALSE a single mutagenetic tree is fit to the data. When K = 1 and noise = TRUE a star mutagenetic tree is fit to the data. If K > 1 the first mutagenetic tree is always the star, i.e. the case K > 1 and noise = FALSE is not possible.
当K = 1时和噪声= false一个单一的致突变树是适合的数据。当K = 1时和噪声= TRUE，明星致突变树是适合的数据。如果钾> 1首先致突变树始终是明星，即情况的K> 1和噪声= FALSE是不可能的。
值----------Value----------
The method returns an RtreemixModel object that represents the K-trees mixture model learned from the given dataset.
该方法返回一个RtreemixModel对象，它代表的K-树的混合模型，从给定的数据集了解到。
注意----------Note----------
When you have too few data samples always use the default value TRUE for the equal.edgeweights. Like this you make sure that all possible patterns (sets of events) have non-zero probabilities. If they don't the fitting procedure will not be completed and you will get an error!
当你的数据样本太少，总是使用默认值TRUEequal.edgeweights。你这样确保所有可能的模式（事件集）有非零的概率。如果他们不装修过程将不能完成，你会得到一个错误！
作者（S）----------Author(s)----------
Jasmina Bogojeska
参考文献----------References----------
参见----------See Also----------
RtreemixData-class, RtreemixModel-class, generate-methods, bootstrap-methods, confIntGPS-methods
RtreemixData-class，RtreemixModel-class，generate-methods，bootstrap-methods，confIntGPS-methods
举例----------Examples----------
## Create an RtreemixData object from a randomly generated RtreemixModel object.[＃创建一个RtreemixData对象从随机产生RtreemixModel对象。]
rand.mod <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8))
data <- sim(model = rand.mod, no.draws = 300)
show(data)
## Create an RtreemixModel object by fitting model to the given data.[＃创建一个给定的数据拟合模型RtreemixModel对象。]
mod <- fit(data = data, K = 3, equal.edgeweights = TRUE, noise = TRUE)
show(mod)
## See the number of tree components in the mixture model.[＃见树组件中的混合模型。]
numTrees(mod)
## See the weights of the branchings from the fitted mixture model.[＃从拟合的混合模型的分枝的重量。]
Weights(mod)
## See a specific tree component k.[＃见一个特定的树组件ķ。]
getTree(object = mod, k = 2)
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 2012-2-26 13:42:32
