iterateBMAglm.train.predict(iterativeBMA)
iterateBMAglm.train.predict()所属R语言包：iterativeBMA
Iterative Bayesian Model Averaging: training and prediction
迭代贝叶斯模型平均：训练和预测
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Classification and variable selection on microarray data. This is a multivariate technique to select a small number of relevant variables (typically genes) to classify microarray samples. This function performs the training, and prediction steps. The data is assumed to consist of
微阵列数据分类和变量选择。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）芯片的样品进行分类。执行此功能的训练，和预测的步骤。假设数据包括
用法----------Usage----------
iterateBMAglm.train.predict (train.expr.set, test.expr.set, train.class, p=100, nbest=10, maxNvar=30, maxIter=20000, thresProbne0=1)
参数----------Arguments----------
参数：train.expr.set
an ExpressionSet object. We assume the rows in the expression data represent variables (genes), while the columns represent samples or experiments. This training data is used to select relevant genes (variables) for classification.
ExpressionSet对象。我们假设在表达数据行代表变量（基因），而列代表样本或实验。这个训练数据用来选择分类相关的基因（变量）。
参数：test.expr.set
an ExpressionSet object. We assume the rows in the expression data represent variables (genes), while the columns represent samples or experiments. The variables selected using the training data is used to classify samples on this test data.
ExpressionSet对象。我们假设在表达数据行代表变量（基因），而列代表样本或实验。选择使用的训练数据的变量，用于在本次测试数据分类的样本。
参数：train.class
class vector for the observations (samples or experiments) in the training data. Class numbers are assumed to start from 0, and the length of this class vector should be equal to the number of rows in train.dat. Since we assume 2-class data, we expect the class vector consists of zero's and one's.
在训练数据的意见（样品或实验）类向量。假设类数从0开始的，这一类向量的长度应该是平等的行在train.dat数。由于我们假设2级的数据，我们期待零和一个人的类向量组成。
参数：p
a number indicating the maximum number of top univariate genes used in the iterative BMA algorithm. This number is assumed to be less than the total number of genes in the training data. A larger p usually requires longer computational time as more iterations of the BMA algorithm are potentially applied. The default is 100.
数字说明中所使用的顶级单因素基因的最大数量的迭代BMA的算法。这个数字被认为是比在训练数据的基因总数少。一个更大的P级通常需要较长的计算时间的BMA算法迭代潜在应用。默认是100。
参数：nbest
a number specifying the number of models of each size returned to bic.glm in the BMA package. The default is 10.
返回一个数字，指定每个大小的模型bic.glmBMA包。默认为10。
参数：maxNvar
a number indicating the maximum number of variables used in each iteration of bic.glm from the BMA package. The default is 30.
数字显示中用于bic.glmBMA包的每个迭代变量的最大数目。默认值为30。
参数：maxIter
a number indicating the maximum of iterations of bic.glm. The default is 20000.
bic.glm迭代的最大的一个数字，指示。默认是20000。
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.glm. The default is 1 percent.
一个数字，指定每个变量（基因）是非零（％）为后验概率的阈值。在bic.glm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％。
Details
详情----------Details----------
This function consists of the training phase and the prediction phase. The training phase consists of first ordering all the variables (genes) by a univariate measure called between-groups to within-groups sums-of-squares (BSS/WSS) ratio, and then iteratively applying the bic.glm algorithm from the BMA package. The prediction phase uses the variables (genes) selected in the training phase to classify the samples
此功能由训练阶段和预测阶段。训练阶段，由第一顺序组间组内款项的平方（BSS / WSS）的比例称为一元的措施的所有变量（基因），然后从反复申请bic.glm算法BMA包。预测阶段使用的变量（基因）在训练阶段中选择的样本分类
值----------Value----------
A vector consisting of the predicted probability that each test
预测概率组成的一个向量，每个测试
注意----------Note----------
The BMA and Biobase packages are required.
BMA和Biobase包需要。
参考文献----------References----------
Bayesian model selection in social research (with Discussion). Sociological Methodology 1995 (Peter V. Marsden, ed.), pp. 111-196, Cambridge, Mass.: Blackwells.
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----------
iterateBMAglm.train, iterateBMAglm.train.predict.test, brier.score
iterateBMAglm.train，iterateBMAglm.train.predict.test，brier.score
举例----------Examples----------
library (Biobase)
library (BMA)
library (iterativeBMA)
data(trainData)
data(trainClass)
data (testData)
ret.vec <- iterateBMAglm.train.predict (train.expr.set=trainData, test.expr.set=testData, trainClass, p=100)
## compute the Brier Score[＃计算马库斯 - 布莱尔分数]
data (testClass)
brier.score (ret.vec, testClass)
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 22:46:30
