powclasspred(gaga)
powclasspred()所属R语言包：gaga
Expected probability that a future sample is correctly classified.
预计未来的样本正确分类的概率。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimates posterior expected probability that a future sample is correctly classified when performing class prediction. The estimate is obtained via Monte Carlo simulation from the posterior predictive.
未来的样本正确分类，表演类的预测时，估计后的期望概率。估计获得通过蒙特卡罗模拟从后路预测。
用法----------Usage----------
powclasspred(gg.fit, x, groups, prgroups, v0thre=1, ngene=100, B=100)
参数----------Arguments----------
参数：gg.fit
GaGa or MiGaGa fit (object of type gagafit, as returned by fitGG).
GAGA或MiGaGa合适的类型gagafit，fitGG返回的对象。
参数：x
ExpressionSet, exprSet, data frame or matrix containing the gene expression measurements used to fit the model.
ExpressionSet，exprSet，数据框或矩阵包含用于拟合模型的基因表达测量。
参数：groups
If x is of type ExpressionSet or exprSet, groups should be the name of the column in pData(x) with the groups that one wishes to compare. If x is a matrix or a data frame, groups should be a vector indicating to which group each column in x corresponds to.
x如果类型ExpressionSet或exprSet，groups应该是列名pData(x)一个愿望比较组。 x如果是一个矩阵或一个数据框，groups应该是哪一组x中的每一列对应的向量。
参数：prgroups
Vector specifying prior probabilities for each group. Defaults to equally probable groups.
向量为每个组指定先验概率。同样可能群体的默认。
参数：v0thre
Only genes with posterior probability of being equally expressed below v0thre are used.
同样被下面的v0thre表示后验概率只有基因使用。
参数：ngene
Number of genes to use to build the classifier. Genes with smaller probability of being equally expressed are selected first.
用来建立分类的基因数目。首先选择与被同样表示的概率较小的基因。
参数：B
Number of Monte Carlo samples to be used.
要使用蒙特卡罗样品的数量。
Details
详情----------Details----------
The routine simulates future samples (microarrays) from the posterior predictive distribution of a given group (e.g. control/cancer). Then it computes the posterior probability that the new sample belongs to each of the groups and classifies the sample into the group with highest probability. This process is repeated B times, and the proportion of correctly classified samples is reported for each group. The standard error is obtained via the usual normal approximation (i.e. SD/B). The overall probability of correct classification is also provided (i.e. for all groups together), but using a more efficient variant of the algorithm. Instead of reporting the observed proportion of correctly classified samples, it reports the expected proportion of correctly classified samples (i.e. the average posterior probability of the class that the sample is assigned to).
常规模拟未来的样品（芯片），从一个给定的组后的预测分布（如控制/癌症）。然后它计算后验概率，新的样本属于每个组和同组的概率最高分为样品。重复此过程B倍，正确分类的样本比例为每个组的报告。获得通过平时的正常逼近（即SD / B），标准错误。还提供整体正确分类的概率（即各组），但使用更有效的算法变种。报告正确分类的样本观察到的比例，而不是报告正确分类的样本（即该样品被分配到的平均类的后验概率）的预期比例。
值----------Value----------
List with components:
与组件列表：
参数：ccall
Estimated expected probability of correctly classifying a future sample.
估计预期未来的样本正确分类的概率。
参数：seccall
Estimated standard error of ccall.
ccall估计标准误差。
参数：ccgroup
Vector with the estimated probability of correctly classifying a sample from each group.
从各组样品正确分类的概率的估计向量。
参数：segroup
Estimated standard error of ccgroup.
ccgroup估计标准误差。
作者（S）----------Author(s)----------
David Rossell
参考文献----------References----------
flexible hierarchical model for microarray
参见----------See Also----------
classpred, fitGG, parest. See powfindgenes for differential
classpred，fitGG，parest。看到powfindgenes差
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 2012-2-25 18:21:40
