tr.rocc(rocc)
tr.rocc()所属R语言包：rocc
Training of a ROC based classifier
培训的ROC基础的分类
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function establishes the ROC based classifier, returning the classifier specifications.
该函数建立中华民国的分类，返回分类规范。
用法----------Usage----------
tr.rocc(g, out, xgenes = 200)
参数----------Arguments----------
参数：g
the input data in form of a matrix with genes as rows and samples as columns. rownames(g) and colnames (g) must be specified.
与作为列的行和样品的基因作为一个矩阵的形式的输入数据。 rownames（g）及colnames（G），必须指定。
参数：out
describes the phenotype of the samples. a factor vector with levels 0 and 1 (in this order) with as many values as there are samples.
描述的表型的样本。向量与0和1的水平（按照这个顺序）与尽可能多的价值有样品的一个因素。
参数：xgenes
numeric (vector of length 1), determines the number of features to be selected in feature selection.
数字（矢量长度为1），确定了一些功能，选择功能选择。
Details
详细信息----------Details----------
For feature selection the function picks the given number of xgenes with highest AUC (AUC below 0.5 are mirrored). Features negatively associated (AUC below 0.5) are multiplied by -1. The selected features are merged by the mean values to form a metagene. Samples are ranked according to the metagene expression. The optimal split of positive (i.e., 1) and negative (i.e., 0) samples is determined as the split yielding the highest accuracy, i.e. correct class assignments in respect to the real class. The split yielding optimal accuracy in the ROC curve is determined using the package ROCR. The metagene threshold is computed as the mean metagene expression value of the two samples that build the boarder of the split. The final classifier specifications consist of a) the selected genes b) positive (AUC above 0.5) or negative (AUC below 0.5) association of these genes to the true class, and c) the metagene threshold. A new sample can be classified using the o.rocc() function.
特征选择的功能挑选一定数目的xgenes最高AUC（AUC低于0.5的镜像）。特点负相关，（AUC 0.5以下）乘以-1。所选择的功能被合并由平均值形成metagene。样品排名根据的metagene表达。最优分割的正（即，1）和阴性（即0）来确定样品为分割得到最高的精度，即正确的类别分配到真正的类。分割得到最佳精度的ROC曲线确定使用包ROCR。该metagene阈值计算的是两个样本建立边界的分裂的的平均metagene的表达式的值。最终的分类器的规格包括）所选择的基因二）正（0.5以上AUC）或负（AUC为0.5以下），这些基因的真类的关联，和c）metagene阈值。一个新的样品可分为使用o.rocc（）函数。
值----------Value----------
a list as a trocc object with components <table summary="R valueblock"> <tr valign="top"><td>AUCs</td> <td> a matrix containing the selected features with corresponding AUC (aucv), positiv or negativ association (posneg), and mirrored AUC (allpos). </td></tr> <tr valign="top"><td>genes</td> <td> character vector containing the genes selected in the feature selection. </td></tr> <tr valign="top"><td>positiv</td> <td> character vector containing all positively associated genes (AUC above 0.5) selected in the feature selection. </td></tr> <tr valign="top"><td>negativ</td> <td> character vector containing all negatively associated genes (AUC below 0.5) selected in the feature selection. </td></tr> <tr valign="top"><td>metagene.expression</td> <td> numeric vector containing the metagene values of the training samples. </td></tr> <tr valign="top"><td>metagene.expression.ranked</td> <td> numeric vector containing the samples ranked by metagene expression values. </td></tr> <tr valign="top"><td>cutoffvalue</td> <td> the metagene threshold obtained from the best split of training samples. </td></tr> <tr valign="top"><td>method</td> <td> the classification method used: ROC.based.predictor. </td></tr>
作为组件一个trocc对象的列表<table summary="R valueblock"> <tr valign="top"> <TD> AUCs</ TD> <td>一个矩阵包含所选功能，与相应的AUC（ aucv），阳性或负面的协会（posneg）的，和镜像AUC（allpos）的。 </ TD> </ TR> <tr valign="top"> <TD>genes</ TD> <TD>字符向量的基因特征选择中选择。 </ TD> </ TR> <tr valign="top"> <TD>positiv </ TD> <TD>字符向量正相关的基因（AUC 0.5以上）中选择的功能选择。 </ TD> </ TR> <tr valign="top"> <TD>negativ </ TD> <TD>字符向量的负相关基因（AUC小于0.5）中选择的功能选择。 </ TD> </ TR> <tr valign="top"> <TD>metagene.expression</ TD> <TD>数字矢量包含的metagene的训练样本值。 </ TD> </ TR> <tr valign="top"> <TD>metagene.expression.ranked</ TD> <TD>数字向量的样品排名metagene表达式的值。 </ TD> </ TR> <tr valign="top"> <TD>cutoffvalue</ TD> <TD> metagene获得最佳分割的训练样本的阈值。 </ TD> </ TR> <tr valign="top"> <TD>method</ TD> <TD>的分类方法：ROC.based.predictor。 </ TD> </ TR>
</table>
</ TABLE>
注意----------Note----------
depends on the package ROCR
取决于包ROCR中
（作者）----------Author(s)----------
Martin Lauss
参考文献----------References----------
Lauss M, Frigyesi A, Ryden T, Hoglund M. Robust assignment of cancer subtypes from expression data using a uni-variate gene expression average as classifier. BMC Cancer 2010 (in print)
参见----------See Also----------
p.rocc,o.rocc
p.rocc，o.rocc
实例----------Examples----------
### Random Dataset and phenotype[＃＃随机数据集和表型]
set.seed(100)
## Dataset should be a matrix[＃数据集应是一个矩阵]
g <- matrix(rnorm(1000*25),ncol=25)
rownames(g) <- paste("Gene",1:1000,sep="_")
colnames(g) <- paste("Sample",1:25,sep="_")
## Phenotype should be a factor with levels 0 and 1: [＃表型与0级和1应该是一个因素：]
out <- as.factor(sample(c(0:1),size=25,replace=TRUE))
predictor <- tr.rocc (g,out,xgenes=50)
## find classifier specification:[＃分类规格：]
predictor$positiv
predictor$negativ
predictor$cutoffvalue
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 2012-9-27 22:40:41
