predictBicSurv(iterativeBMAsurv)
predictBicSurv()所属R语言包：iterativeBMAsurv
Predicted patient risk scores from iterative Bayesian Model Averaging
预测病人的风险得分迭代贝叶斯模型平均
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function predicts the risk scores for patient
此功能预测病人的风险分数
用法----------Usage----------
predictBicSurv(newdata.vec, postprob.vec, mle.mat)
参数----------Arguments----------
参数：newdata.vec
A vector consisting of the data from a test sample.
从测试样品中的数据组成一个向量。
参数：postprob.vec
A vector consisting of the posterior probability of each BMA selected model.
一个向量组成的每个劳社所选模型的后验概率。
参数：mle.mat
A matrix with one row per model and one column per variable giving the maximum likelihood estimate of each coefficient for each bic.surv selected model.
与一列每个模型和每一个给每个系数的最大似然估计，每个bic.surv选择模型的变量列的矩阵。
Details
详情----------Details----------
The function begins by computing the risk score of each model k in the selected set of models M. The risk score for a model k = sum (coefficient in model k * corresponding expression level in newdata.vec). The function then predicts a patient risk score by summing the product of the posterior probability of model k and the risk score of model k over all models in M. In other words, predicted patient risk score = sum (postprob model k * risk score model k). This function is called
通过计算风险评分模型研究选定的一组中的每一个K型风险评分模型K = SUM（模型系数K *在newdata.vec相应的表达水平）的功能开始。病人风险评分= SUM（K型postprob *风险评分模型预测的功能，然后预测病人的风险评分，总结产品的后验概率的K型和M中的所有型号，K型风险，换句话说得分K）。此功能称为
值----------Value----------
A real number representing the predicted risk score of a given
一个真正的数字代表预测一个给定的风险得分
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.predict.assess, iterateBMAsurv.train.wrapper, predictiveAssessCategory, trainData, trainSurv, trainCens, testData
iterateBMAsurv.train.predict.assess，iterateBMAsurv.train.wrapper，predictiveAssessCategory，trainData，trainSurv，trainCens，testData
举例----------Examples----------
library (BMA)
library (iterativeBMAsurv)
data(trainData)
data(trainSurv)
data(trainCens)
data(testData)
## Training phase: select relevant genes. Assume the training data is sorted [训练阶段：选择相关的基因。假定训练数据进行排序]
## and includes the desired number of top-ranked genes.[＃，其中包括世界排名第一的基因所需的数量。]
ret.list <- iterateBMAsurv.train.wrapper (x=trainData, surv.time=trainSurv, cens.vec=trainCens, nbest=5)
ret.bma <- ret.list$obj
## Get the selected genes with probne0 > 0[＃获取与probne0> 0选定的基因]
selected.genes <- ret.list$curr.names[ret.bma$probne0 > 0]
## Get the subset of test data with the genes from the last iteration of bic.surv[＃获取从对bic.surv的最后一次迭代的基因测试数据的子集]
curr.test.dat <- testData [, selected.genes]
## Compute the predicted risk scores for the test samples[＃计算测试样品的预测风险评分]
y.pred.test <- apply (curr.test.dat, 1, predictBicSurv, postprob.vec=ret.bma$postprob, mle.mat=ret.bma$mle)
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 22:44:19
