iterateBMAglm.wrapper(iterativeBMA)
iterateBMAglm.wrapper()所属R语言包：iterativeBMA
Iterative Bayesian Model Averaging
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 repeatedly calls bic.glm from the BMA package until all variables are exhausted. The data is assumed to consist of
此功能重复调用bic.glmBMA包，直到所有的变量都用尽。假设数据包括
用法----------Usage----------
iterateBMAglm.wrapper (sortedA, y, nbest=10, maxNvar=30, maxIter=20000, thresProbne0=1)
参数----------Arguments----------
参数：sortedA
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 or experiments.
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）代表样品或实验。
参数：y
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 sortedA. 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sortedA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：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----------
In this function, the variables are assumed to be sorted, and bic.glm is called repeatedly. In the first application of the bic.glm algorithm, the top maxNvar univariate ranked genes are used. After each application of the bic.glm algorithm, the genes with probne0 < thresProbne0 are dropped, and the next univariate ordered genes are added to the BMA window. The function iterateBMAglm.train calls BssWssFast before calling this function. Using this function, users can experiment with alternative
在这个函数中，假定的变量进行排序，bic.glm反复调用。 bic.glm算法的首次应用，在顶端maxNvar单因素排名基因。后每个bic.glm算法的应用，基因probne0<thresProbne0下降，下单因素下令基因添加到BMA的窗口。功能iterateBMAglm.train调用BssWssFast前调用这个函数。使用此功能，用户可以尝试替代
值----------Value----------
If all variables are exhausted, an object of class bic.glm returned by the last iteration of bic.glm. Otherwise, -1 is returned. The object of class bic.glm is a list consisting of the following components:
如果用尽了所有的变量，一个类的对象bic.glm回由bic.glm的最后一次迭代。否则，则返回-1。 bic.glm类的对象是一个列表，包括以下几部分组成：
参数：namesx
the names of the variables in the last iteration of bic.glm.
在bic.glm最后一次迭代变量的名称。
参数：postprob
the posterior probabilities of the models selected.
选择模型的后验概率。
参数：deviance
the estimated model deviances.
估计模型deviances。
参数：label
labels identifying the models selected.
标签标识选定的模型。
参数：bic
values of BIC for the models.
模型的BIC值。
参数：size
the number of independent variables in each of the models.
每个模型的独立变量的数目。
参数：which
a logical matrix with one row per model and one column per variable indicating whether that variable is in the model.
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的逻辑矩阵。
参数：probne0
the posterior probability that each variable is non-zero (in percent).
每个变量是非零的后验概率（％）。
参数：postmean
the posterior mean of each coefficient (from model averaging).
每个系数后平均（平均模型）。
参数：postsd
the posterior standard deviation of each coefficient (from model averaging).
后每个系数的标准偏差（平均模型）。
参数：condpostmean
the posterior mean of each coefficient conditional on the variable being included in the model.
后，平均每个条件被包括在模型中的变量的系数。
参数：condpostsd
the posterior standard deviation of each coefficient conditional on the variable being included in the model.
每个条件被包括在模型中的变量的系数后的标准偏差。
参数：mle
matrix with one row per model and one column per variable giving the maximum likelihood estimate of each coefficient for each model.
与一列每个模型，并给每个模型各系数的最大似然估计每一个变量列的矩阵。
参数：se
matrix with one row per model and one column per variable giving the standard error of each coefficient for each model.
矩阵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。
参数：reduced
a logical indicating whether any variables were dropped before model averaging.
逻辑模型平均下降之前，是否有任何变数。
参数：dropped
a vector containing the names of those variables dropped before model averaging.
前模型平均下降矢量包含这些变量的名称。
参数：call
the matched call that created the bma.lm object.
匹配的的呼叫创建的bma.lm对象。
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, iterateBMAglm.train.predict.test, BssWssFast
iterateBMAglm.train，iterateBMAglm.train.predict，iterateBMAglm.train.predict.test，BssWssFast
举例----------Examples----------
library (Biobase)
library (BMA)
library (iterativeBMA)
data(trainData)
data(trainClass)
## Use the BSS/WSS ratio to rank all genes in the training data[＃使用的BSS / WSS比排名在所有的训练数据的基因]
sorted.vec <- BssWssFast (t(exprs(trainData)), trainClass, numClass = 2)
## get the top ranked 50 genes[＃取得了名列前茅的50个基因]
sorted.train.dat <- t(exprs(trainData[sorted.vec$ix[1:50], ]))
## run iterative bic.glm[＃运行迭代bic.glm]
ret.bic.glm <- iterateBMAglm.wrapper (sorted.train.dat, y=trainClass)
## The above commands are equivalent to the following [＃上面的命令是等价于下面]
ret.bic.glm <- iterateBMAglm.train (train.expr.set=trainData, trainClass, p=50)
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 22:46:47
