gps-methods(Rtreemix)
gps-methods()所属R语言包：Rtreemix
Methods for predicting the GPS of given dataset by
预测给定数据集的GPS方法
译者：生物统计家园网 机器人LoveR
描述----------Description----------
These functions compute the genetic progression score (GPS) of each sample in the given data by performing a waiting time simulation along the branchings of the mixture model model. The model has to be specified. If a dataset is missing a GPS for all possible patterns is calculated. The number of events of the samples in data equals the number of genetic events in the model.
这些函数计算每个样本的遗传进展评分（GPS）在给定的data执行沿分枝混合模型model等待时间模拟。该模型具有指定。如果数据集的所有可能的模式是缺少了GPS计算。样品的事件data等于在model遗传事件的数量。
用法----------Usage----------
gps(model, data, ...)
参数----------Arguments----------
参数：model
An object of the class RtreemixModel specifying the mutagenetic trees mixture model used for deriving the GPS values. The model should NOT have more than 20 genetic events.
用于GPS的值派生类RtreemixModel指定的致突变树木混合模型的对象。该模型不应该有超过20个遗传事件。
参数：data
An RtreemixData object or a 0-1 matrix containing the samples (patterns of genetic events) for which the GPS values are to be calculated. The length of each of them has to be equal to the number of genetic events in the model.
RtreemixData对象或0-1 matrix包含的GPS值来计算的样本（基因活动模式）。他们每个人的长度是相等的遗传事件model。
参数：...
sampling.mode is a character that specifies the sampling mode ("constant" or "exponential") used in the waiting time simulations. Its default value is "exponential". sampling.param is a numeric that specifies the sampling parameter corresponding to the sampling mode given by sampling.mode. Its default value is 1. no.sim is an integer larger than 0 giving the number of iterations for the waiting time simulations. Its default value is 10. seed is a positive integer specifying the random generator seed. Its default value is (-1) and then the time is used as a random generator.
sampling.mode是character指定的等待时间模拟中使用的采样模式（“不变”或“指数”）。其默认值是“指数”。 sampling.param是numeric指定sampling.mode采样模式给予相应的采样参数。其默认值是1。 no.sim是integer大于0等待时间模拟的迭代次数。它的默认值是10。 seed是一个积极的integer指定的随机生成器的种子。它的默认值是（-1），然后用时间作为随机数发生器。
值----------Value----------
The function returns an object from the RtreemixGPS class that containes the calculated GPS values, the model used for the computation, the data, and so on (see RtreemixGPS-class). The GPS values are represented as a numeric vector with length equal to the number of samples in data.
RtreemixGPS类中包含与GPS的计算值，计算，数据等，所使用的模型函数返回一个对象（看到RtreemixGPS-class）。 GPS值代表一个numeric向量长度等于样品data的数量。
方法----------Methods----------
注意----------Note----------
The mixture model used for deriving the GPS values should not have more than 20 genetic events. The reason for this is that the number of all possible patterns for which the GPS values are calculated during a computationally intensive simulations is in this case $2^20$. This demands too much memory. The GPS 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.
混合模型用于推导GPS值不应该超过20遗传事件。这样做的原因是，所有可能的模式为GPS值计算期间计算密集型模拟的数量是在这种情况下$ 2 ^ 20元。这需要太多的记忆。 GPS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----------
参见----------See Also----------
RtreemixGPS-class, RtreemixData-class, RtreemixModel-class, fit-methods, confIntGPS-methods
RtreemixGPS-class，RtreemixData-class，RtreemixModel-class，fit-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 = 2, no.events = 7, noise.tree = TRUE, prob = c(0.2, 0.8))[rand.mod < - 生成（k = 2，no.events = 7，noise.tree = TRUE，概率= C（0.2，0.8））]
#data <- sim(model = rand.mod, no.draws = 400)[数据< - SIM卡（型号= rand.mod，no.draws = 400）]
## 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 = 2, equal.edgeweights = TRUE, noise = TRUE)[MOD < - 适合（数据=数据，钾= 2，equal.edgeweights = TRUE，噪音= TRUE时）]
#show(mod)[节目（MOD）]
## Create an RtreemixGPS object by calculating the GPS for all possible patterns.[＃创建一个计算所有可能的模式的GPS RtreemixGPS对象。]
#modGPS.all <- gps(model = mod, no.sim = 1000) ## time consuming copmutations[modGPS.all < - GPS（型号为= MOD，no.sim = 1000）＃时间耗费copmutations]
#show(modGPS.all)[展（modGPS.all）]
## See the GPS values for all possible data.[＃GPS的所有可能的数据值。]
#GPS(modGPS.all) ## time consuming copmutations[GPS（modGPS.all）＃耗时copmutations]
## Create an RtreemixGPS object by calculating the GPS for the data based on the model mod.[＃创建一个基于模型MOD数据计算的GPS RtreemixGPS对象。]
#modGPS <- gps(model = mod, data = data, no.sim = 1000)[modGPS < - GPS（型号= MOD，数据=数据，no.sim = 1000）]
#show(modGPS) ## time consuming copmutations[展（modGPS）＃耗时copmutations]
## See the GPS values for data.[＃见的GPS数据值。]
#GPS(modGPS) ## time consuming copmutations[GPS（modGPS）＃耗时copmutations]
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 13:42:57
