R语言 iterativeBMAsurv包 iterativeBMAsurv-package()函数中文帮助文档(中英文对照)

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iterativeBMAsurv-package(iterativeBMAsurv)
iterativeBMAsurv-package()所属R语言包：iterativeBMAsurv

                                         The Iterative Bayesian Model Averaging (BMA) algorithm for survival analysis
                                         迭代贝叶斯模型平均算法（BMA）的生存分析

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

The iterative Bayesian Model Averaging (BMA) algorithm for survival analysis is a variable selection method for applying survival analysis to microarray data..
迭代贝叶斯模型平均（BMA）的生存分析算法是应用生存分析微阵列数据的变量选择方法......


Details

详情----------Details----------

The function iterateBMAsurv.train selects relevant variables by iteratively applying the bic.surv function from the BMA package until all variables in the training data are exhausted. The variables are  assumed to be pre-sorted by rank when this function is called. The function  iterateBMAsurv.train.wrapper acts as a wrapper for iterateBMAsurv.train,  returning the names of the selected variables and an object of class bic.surv  if the iterations exhaust all variables in the training set (-1 otherwise). Again,  the variables are assumed to be pre-sorted by rank, so calling this function  allows users to experiment with different univariate ranking measures. The function  iterateBMAsurv.train.predict.assess combines the training, prediction, and  assessment phases. It returns a list consisting of the numbers of selected genes  and models from the training phase, the predicted risk scores of the test samples,  and the overall survival analysis statistics indicating the difference between risk  groups (p-value, chi-square statistic, and variance matrix). It also writes a  Kaplan-Meier survival analysis curve to file, which serves as a pictorial nonparametric estimator of the difference between risk groups. The variables are not assumed to be  pre-sorted by rank when this function is called. iterateBMAsurv.train.predict.assess  calls singleGeneCoxph, which ranks the genes based on their log likelihood scores  using Cox Proportional Hazards Regression. iterateBMAsurv.train.predict.assess  calls iterateBMAsurv.train.wrapper in its training phase, so if Cox Proportional  Hazards Regression is the desired univariate ranking algorithm, then calling this  function with the training and testing sets is all that is necessary for a complete  survival analysis run. The function crossVal performs k runs of n-fold cross validation on a training data set, where k and n are specified by the user.  crossVal calls iterateBMAsurv.train.predict.assess during each fold, so Cox Proportional Hazards Regression is the univariate ranking measure for this function.
直到用尽所有的变量都在训练数据反复申请iterateBMAsurv.train包bic.surv函数的功能BMA选择相关的变量。假设变量被排名时，此功能被称为预先排序。 iterateBMAsurv.train.wrapper作为的包装函数iterateBMAsurv.train，返回所选变量的名称和类对象bic.surv如果迭代用尽在训练集的所有变量（否则为-1） 。再次，变量会被排名预先排序，所以调用此功能，使用户能够试验不同的单因素的排名措施。功能iterateBMAsurv.train.predict.assess相结合的培训，预测和评估阶段。它返回一个列表中选定的基因和训练阶段，测试样本的预测风险分数，整体生存分析统计表明高危人群之间的差异（p值，卡方统计，从模型的数字组成方差矩阵）。它也写的Kaplan-Meier法生存分析文件的曲线，作为图案的高危人群之间的差异的非参数估计。变量不承担被排名时，此功能被称为预先排序。 iterateBMAsurv.train.predict.assess称singleGeneCoxph，位居log的可能性使用Cox比例风险回归的分数为基础的基因。 iterateBMAsurv.train.predict.assess要求iterateBMAsurv.train.wrapper在训练阶段，因此，如果Cox比例风险回归所需的单变量排序算法，然后调用这个训练集和测试集的功能是，有必要为一个完整的生存分析运行。功能crossVal执行k的n倍交叉验证的训练数据集，其中k和n由用户指定的运行。 crossVal要求iterateBMAsurv.train.predict.assess在每个倍，因此Cox比例风险回归是此功能的排名单因素的措施。


作者（S）----------Author(s)----------



Ka Yee Yeung, University of Washington, Seattle, WA,
Amalia Annest, University of Washington, Tacoma, WA

Maintainer: Ka Yee Yeung <kayee@u.washington.edu>
Amalia Annest <amanu@u.washington.edu>




参考文献----------References----------

Iterative Bayesian Model Averaging for Survival Analysis. Manuscript in Progress.
Bayesian model selection in social research (with Discussion). Sociological Methodology 1995 (Peter V. Marsden, ed.), pp. 111-196, Cambridge, Mass.: Blackwells.
Bayesian Model Averaging in Proprtional Hazard Models: Assessing the Risk of a Stroke.  Applied Statistics 46: 433-448.
Bayesian Model Averaging: Development of an improved multi-class, gene selection and classification tool for microarray data.  Bioinformatics 21: 2394-2402.

参见----------See Also----------

iterateBMAsurv.train,   iterateBMAsurv.train.wrapper, iterateBMAsurv.train.predict.assess, singleGeneCoxph, predictiveAssessCategory, crossVal, trainData, trainSurv,  trainCens, testData, testSurv, testCens
iterateBMAsurv.train，iterateBMAsurv.train.wrapper，iterateBMAsurv.train.predict.assess，singleGeneCoxph，predictiveAssessCategory，crossVal，trainData，trainSurv，trainCens，testData，testSurv，testCens


举例----------Examples----------


library (BMA)
library (iterativeBMAsurv)
data(trainData)
data(trainSurv)
data(trainCens)
data(testData)
data(testSurv)
data(testCens)

## Use p=10 genes and nbest=5 for fast computation[＃使用快速计算P = 10基因和nbest的= 5]
ret.bma <- iterateBMAsurv.train.predict.assess (train.dat=trainData, test.dat=testData, surv.time.train=trainSurv, surv.time.test=testSurv, cens.vec.train=trainCens, cens.vec.test=testCens, p=10, nbest=5)

## Extract the statistics from this survival analysis run[＃从这个生存分析运行中提取的统计信息]
number.genes <- ret.bma$nvar
number.models <- ret.bma$nmodel
evaluate.success <- ret.bma$statistics

## Perform 1 run of 2-fold cross validation on the training set, using p=10 genes and nbest=5 for fast computation[＃1 2倍交叉验证的训练集上运行，使用快速计算P = 10基因和nbest的= 5]
cv <- crossVal(exset=trainData, survTime=trainSurv, censor=trainCens, diseaseType="DLBCL", noFolds=2, noRuns=1, p=10, nbest=5)


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