iterateBMAsurv.train(iterativeBMAsurv)
iterateBMAsurv.train()所属R语言包：iterativeBMAsurv
Iterative Bayesian Model Averaging: training
迭代贝叶斯模型平均：培训
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Survival analysis and variable selection on microarray data. This is a multivariate technique to select a small number of relevant variables (typically genes) to perform survival analysis on microarray data. This function performs the training phase. It repeatedly calls bic.surv from the BMA package until all variables are exhausted. The
生存分析和微阵列数据的变量选择。这是一个多元的技术，选择一个小数目的相关变量（通常是基因）芯片数据进行生存分析。执行此功能的训练阶段。它一再要求bic.survBMA包，直到耗尽所有的变量。 “
用法----------Usage----------
iterateBMAsurv.train (x, surv.time, cens.vec, curr.mat, stopVar=0, nextVar, nbest=10, maxNvar=25, maxIter=200000, thresProbne0=1, verbose = FALSE, suff.string="")
参数----------Arguments----------
参数：x
Data matrix where columns are variables and rows are observations. The variables (columns) are assumed to be sorted using a univariate measure. In the case of gene expression data, the columns (variables) represent genes, while the rows (observations) represent samples.
数据矩阵列变量和行观察。假设变量（列）进行排序，使用一元的措施。在基因表达数据的情况下，列（变量）代表的基因，而行（意见）代表样品。
参数：surv.time
Vector of survival times for the patient samples. Survival times are assumed to be presented in uniform format (e.g., months or days), and the length of this vector should be equal to the number of rows in x.
向量的患者样本的存活时间。假设存活时间要统一格式（例如，数月或数天），这个向量的长度应该等于在x的行数。
参数：cens.vec
Vector of censor data for the patient samples. In general, 0 = censored and 1 = uncensored. The length of this vector should equal the number of rows in x and the number of elements in surv.time.
向量的患者样本的数据进行审查。在一般情况下，0 =审查，1 =未经审查的。这个向量的长度应该等于在x和元素surv.time的数量的行的数目。
参数：curr.mat
Matrix of independent variables in the active bic.surv window. There can be at most maxNvar variables in the window at any given time.
矩阵活动bic.surv窗口中的自变量。可以有最maxNvar窗口中的变量，在任何特定时间。
参数：stopVar
0 to continue iterations, 1 to stop iterations (default 0)
0继续迭代，1停止迭代（默认为0）
参数：nextVar
Integer placeholder indicating the next variable to be brought into the active bic.surv window.
整数的占位符表示一个变量被带入活动bic.surv窗口。
参数：nbest
A number specifying the number of models of each size returned to bic.surv in the BMA package. The default is 10.
返回一个数字，指定每个大小的模型bic.survBMA包。默认为10。
参数：maxNvar
A number indicating the maximum number of variables used in each iteration of bic.surv from the BMA package. The default is 25.
一个数字，指示中用于bic.survBMA包的每个迭代变量的最大数目。默认值为25。
参数：maxIter
A number indicating the maximum iterations of bic.surv. The default is 200000.
一个数字，指示bic.surv最大迭代。默认是200000。
参数：thresProbne0
A number specifying the threshold for the posterior probability that each variable (gene) is non-zero (in percent). Variables (genes) with such posterior probability less than this threshold are dropped in the iterative application of bic.surv. The default is 1 percent.
一个数字，指定阈值，每个变量（基因）是非零（％）为后验概率。在bic.surv的迭代应用后验概率小于这个阈值的变量（基因）被丢弃。默认是1％。
参数：verbose
A boolean variable indicating whether or not to print interim information to the console. The default is FALSE.
一个布尔变量，表示是否打印到控制台的临时信息。默认值为FALSE。
参数：suff.string
A string for writing to file.
写入文件的字符串。
Details
详情----------Details----------
The training phase consists of first ordering all the variables (genes) by a univariate measure such as Cox Proportional Hazards Regression, and then iteratively applying the bic.surv algorithm from the BMA package. In the first application of the bic.surv algorithm, the top maxNvar univariate ranked genes are used. After each application of the bic.surv algorithm, the genes with probne0 < thresProbne0 are dropped, and the next univariate ordered genes are added
训练阶段，由第一顺序由单因素Cox比例风险回归的措施，如所有的变量（基因），然后反复申请bic.surv包BMA算法。 bic.surv算法的首次应用，在顶端maxNvar单因素排名基因。经过每个bic.surv算法，probne0<thresProbne0下降，下单因素下令基因添加的基因中的应用
值----------Value----------
On the last iteration of bic.surv, four items are returned:
上bic.surv最后一次迭代，返回四个项目：
参数：curr.mat
A vector containing the names of the variables (genes) from the final iteration of bic.surv </table>
一个bic.surv</ TABLE>最后一次迭代变量的名称（基因的向量）
.
。
参数：stopVar
The ending value of stopVar after all iterations.
结束后，所有迭代stopVar价值。
参数：nextVar
The ending value of nextVar after all iterations.
结束后，所有迭代nextVar价值。
参数：
An object of class bic.surv resulting from the last iteration of bic.surv. The object is a list consisting of the following components:
bic.surv从bic.surv的最后一次迭代造成一个类的对象。对象是一个列表，包含以下组件：
namesxthe names of the variables in the last iteration of bic.surv.
namesxthe在bic.surv的最后一次迭代的变量名。
postprobthe posterior probabilities of the models selected.
postprobthe选定模型后验概率。
labellabels identifying the models selected.
选择模型labellabels确定。
bicvalues of BIC for the models.
bicvalues的BIC模型。
sizethe number of independent variables in each of the models.
sizethe每个模型的独立变量的数目。
whicha logical matrix with one row per model and one column per variable indicating whether that variable is in the model.
whicha逻辑矩阵与一列每个模型和列表示该变量是否是模型中的每一个变量。
probne0the posterior probability that each variable is non-zero (in percent).
probne0the每个变量是非零（％）的后验概率。
postmeanthe posterior mean of each coefficient (from model averaging).
postmeanthe后，平均每个系数（平均模型）。
postsdthe posterior standard deviation of each coefficient (from model averaging).
postsdthe后每个系数模型平均标准偏差。
condpostmeanthe posterior mean of each coefficient conditional on the variable being included in the model.
condpostmeanthe后，平均每个条件被包括在模型中的变量的系数。
condpostsdthe posterior standard deviation of each coefficient conditional on the variable being included in the model.
condpostsdthe后每个条件被包括在模型中的变量的系数的标准偏差。
mlematrix with one row per model and one column per variable giving the maximum likelihood estimate of each coefficient for each model.
mlematrix与一列每个模型和每一个变量列，使每个模型各系数的最大似然估计。
sematrix with one row per model and one column per variable giving the standard error of each coefficient for each model.
sematrix与一列每个模型和每一个变量列给每个模型的每个系数的标准误差。
reduceda logical indicating whether any variables were dropped before model averaging.
reduceda逻辑模型平均下降之前，是否有任何变数。
droppeda vector containing the names of those variables dropped before model averaging.
droppeda向量含有这些变量的名称前模型平均下降。
callthe matched call that created the bma.lm object.
callthe匹配调用，创建的bma.lm的对象。
注意----------Note----------
The BMA package is required.
BMA包是必需的。
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.wrapper, iterateBMAsurv.train.predict.assess, singleGeneCoxph, predictBicSurv, trainData, trainSurv, trainCens, testData
iterateBMAsurv.train.wrapper，iterateBMAsurv.train.predict.assess，singleGeneCoxph，predictBicSurv，trainData，trainSurv，trainCens，testData
举例----------Examples----------
library(BMA)
library(iterativeBMAsurv)
data(trainData)
data(trainSurv)
data(trainCens)
data(testData)
## Training data should be pre-sorted before beginning[＃训练数据应预先排序，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：选择使用快速计算nbest = 5的相关基因]
ret.bic.surv <- iterateBMAsurv.train (x=trainData, surv.time=trainSurv, cens.vec=trainCens, curr.mat, stopVar=0, nextVar, nbest=5, maxNvar=25)
# 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[＃获取基因测试数据的子集，从最后一次迭代]
## 'bic.surv'[＃“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)
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 22:44:00
