predictiveAssessCategory(iterativeBMAsurv)
predictiveAssessCategory()所属R语言包：iterativeBMAsurv
Risk Groups: assignment of patient test samples
高危人群：病人的试验样品的分配
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function assigns a risk group (high-risk or low-risk) to each patient sample in the test set based on the value of the patient's predicted risk score. The cutPoint between high-risk and
此功能分配到每个病人的样本危险群病人的预测风险评分值的基础上设置测试（高风险或低风险）。 cutPoint之间的高风险和
用法----------Usage----------
predictiveAssessCategory (y.pred.test, y.pred.train, cens.vec.test, cutPoint=50)
参数----------Arguments----------
参数：y.pred.test
A vector containing the predicted risk scores of the test samples.
一个测试样本的预测风险分数的向量。
参数：y.pred.train
A vector containing the computed risk scores of the training samples.
一个向量，训练样本计算风险分数。
参数：cens.vec.test
A vector of censor data for the patient samples in the test set. In general, 0 = censored and 1 = uncensored.
一个向量，患者样本测试集的数据进行审查。在一般情况下，0 =审查，1 =未经审查的。
参数：cutPoint
Threshold percent for separating high- from low-risk groups. The default is 50.
分离高从低风险群体的阈值％。默认值是50。
Details
详情----------Details----------
This function begins by using the computed risk scores of the training set (y.pred.train) to define a real-number empirical cutoff point between high- and low-risk groups. The cutoff point is determined by the percentile cutPoint as designated by the user. The predicted risk scores from the test samples are then matched against this cutoff point to determine whether they belong in the high-risk or the low-risk
此功能开始使用训练集计算风险分数（y.pred.train）来定义高和低风险的群体之间的实数的经验分界点。截止点是确定由百分cutPoint由用户指定。从样本预测的风险评分，然后再配对这个分界点，以确定它们是否属于高风险或低风险
值----------Value----------
A list consisting of 2 components:
列表2部分组成：
参数：assign.risk
A 2 x 2 table indicating the number of test samples in each category (high-risk/censored, high-risk/uncensored, low-risk/censored, low-risk/uncensored).
一个2×2表指示在每个类别中的试验样品的数量（high-risk/censored，high-risk/uncensored的，low-risk/censored low-risk/uncensored）。
参数：groups
A list of all patient samples in the test set with their corresponding 'High-risk' or 'Low-risk' designations.
所有患者样本测试其相应的“高风险”或“低风险”称号的名单。
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, predictBicSurv, singleGeneCoxph, printTopGenes, trainData, trainSurv, trainCens, testData, testSurv, testCens,
iterateBMAsurv.train.predict.assess，predictBicSurv，singleGeneCoxph，printTopGenes，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)
## Training should be pre-sorted before beginning[＃培训应预先排序，然后再开始]
## Initialize the matrix for the active bic.surv window with variables 1 through maxNvar[＃初始化为活动bic.surv窗口的1通过maxNvar变量矩阵]
maxNvar <- 25
curr.mat <- trainData[, 1:maxNvar]
nextVar <- maxNvar + 1
## Training phase: select relevant genes, using nbest=5 for fast computation[训练阶段：选择相关的基因，使用快速计算nbest = 5]
ret.bic.surv <- iterateBMAsurv.train (x=trainData, surv.time=trainSurv, cens.vec=trainCens, curr.mat, stopVar=0, nextVar, maxNvar=25, nbest=5)
# Apply bic.surv again using selected genes[再次申请bic.surv使用选定的基因]
ret.bma <- bic.surv (x=ret.bic.surv$curr.mat, surv.t=trainSurv, cens=trainCens, nbest=5, maxCol=(maxNvar+1))
## Get the matrix for genes with probne0 > 0[＃获取基因与probne0> 0的矩阵]
ret.gene.mat <- ret.bic.surv$curr.mat[ret.bma$probne0 > 0]
## Get the gene names from ret.gene.mat[＃从ret.gene.mat获取基因名称]
selected.genes <- dimnames(ret.gene.mat)[[2]]
## Show the posterior probabilities of selected models[＃显示所选模型的后验概率]
ret.bma$postprob
## Get the subset of test data with the genes from the last iteration of 'bic.surv'[＃获取基因测试数据的子集，从最后一次迭代“bic.surv”]
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)
## Compute the risk scores in the training set[＃在训练集计算风险分数]
y.pred.train <- apply (trainData[, selected.genes], 1, predictBicSurv, postprob.vec=ret.bma$postprob, mle.mat=ret.bma$mle)
## Assign risk categories for test samples[＃指定测试样本的风险类别]
ret.table <- predictiveAssessCategory (y.pred.test, y.pred.train, testCens, cutPoint=50)
## Extract risk group vector and risk group table[＃提取风险组的向量和风险组表]
risk.list <- ret.table$groups
risk.table <- ret.table$assign.risk
## Create a survival object from the test set[＃创建一个测试集的生存对象]
mySurv.obj <- Surv(testSurv, testCens)
## Extract statistics including p-value and chi-square[＃提取统计数据，包括P-值和卡方]
stats <- survdiff(mySurv.obj ~ unlist(risk.list))
## The entire block of code above can be executed simply by calling[＃整个上面的代码块可以通过简单地调用执行]
## 'iterateBMAsurv.train.predict.assess' [＃“iterateBMAsurv.train.predict.assess”]
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 2012-2-25 22:44:27
