prediction(CMA)
prediction()所属R语言包：CMA
General method for predicting classes of new observations
预测新观察类的一般方法
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This method constructs the given classifier using the specified training data, gene selection and tuning results.. Subsequently, class labels are predicted for new observations.<br> For S4 method information, s. classification-methods.
这种方法构造分类，使用指定的训练数据，基因选择和调整结果......随后，类标签为新的观测结果进行了预测。对于S4方法信息的参考。 classification-methods。
用法----------Usage----------
prediction(X.tr,y.tr,X.new,f,classifier,genesel,models=F,nbgene,tuneres,...)
参数----------Arguments----------
参数：X.tr
Training gene expression data. Can be one of the following:
培训的基因表达数据。可以是下列之一：
A matrix. Rows correspond to observations, columns to variables.
Amatrix。行对应的观察，列变量。
A data.frame, when f is not missing (s. below).
一个data.frame时f不缺少（S.下面）。
An object of class ExpressionSet.
对象类ExpressionSet。
参数：X.new
gene expression data. Can be one of the following:
基因表达数据。可以是下列之一：
A matrix. Rows correspond to observations, columns to variables.
Amatrix。行对应的观察，列变量。
A data.frame, when f is not missing (s. below).
一个data.frame时f不缺少（S.下面）。
An object of class ExpressionSet. </ul>
对象类ExpressionSet。 </ UL>
参数：y.tr
Class labels of training observation. Can be one of the following:
训练观察类的标签。可以是下列之一：
A numeric vector.
一个numeric向量。
A factor.
Afactor。
A character if X is an ExpressionSet that specifies the phenotype variable.
一个如果character X是一个ExpressionSet指定的表型变量。
missing, if X is a data.frame and a proper formula f is provided.
missing，X是data.frame和适当的公式f提供。
WARNING: The class labels will be re-coded for classifier construction to range from 0 to K-1, where K is the total number of different classes in the learning set.
警告：分类建筑类标签将被重新编码范围从0K-1，K是在学习集不同类别的总数。
参数：f
A two-sided formula, if X is a data.frame. The left part correspond to class labels, the right to variables.
一个双面的公式，如果X是data.frame。左边部分对应类的标签，对变量的权利。
参数：genesel
Optional (but usually recommended) object of class genesel containing variable importance information for the argument learningsets. In this case the object contains a single variable selection. Appropriate genesel-objects can be obtained using the function genesel without learningset and setting X=X.tr and y=y.tr (i.e. corresponding to the training data of this function).
可选（但通常推荐）对象类genesel含参数learningsets变量重要性的信息。在这种情况下，该对象包含一个单一的变量选择。适当genesel对象可以得到没有learningset使用的功能genesel设置X=X.tr和y=y.tr（即对应此功能的训练数据）。
参数：nbgene
Number of best genes to be kept for classification, based on either genesel or the call to GeneSelection using genesellist. In the case that both are missing, this argument is not necessary. note:
最好的基因数目要保持分类的基础上，要么genesel或GeneSelection用genesellist。都缺少的情况下，这种说法是没有必要的。注意：
If the gene selection method has been one of "lasso", "elasticnet", "boosting", nbgene will be reset to min(s, nbgene) where s is the number of nonzero coefficients.
如果基因选择方法之一"lasso", "elasticnet", "boosting"，nbgene将重置min(s, nbgene)其中s是非零系数的数目。
if the gene selection scheme has been "one-vs-all", "pairwise" for the multiclass case, there exist several rankings. The top nbgene will be kept of each of them, so the number of effective used genes will sometimes be much larger.
如果基因选择方案一直"one-vs-all", "pairwise"为多的情况下，存在着几个排名。顶部nbgene将保持他们每个人的，所以有效使用基因的数量有时会大得多。
参数：classifier
Name of function ending with CMA indicating the classifier to be used.
CMA指示要使用的分类函数名结束。
参数：tuneres
Analogous to the argument genesel - object of class tuningresult containing information about the best hyperparameter choice for the argument learningsets. Appropriate tuning-objects can be obtained using the function tune without learningsets and setting parameters X=X.tr, y=y.tr and genesel=genesel (i.e. using the same training data and gene selection as in this function)
类似的说法genesel - 类tuningresult包含有关为参数learningsets最好hyperparameter的选择的信息的对象。没有learningsets使用的功能tune和设置参数，可以得到适当的调整对象X=X.tr，y=y.tr和genesel=genesel（即使用相同的训练数据和基因选择在此功能）
参数：models
a logical value indicating whether the model object shall be returned
一个逻辑值，该值指示是否应归还模型对象
参数：...
Further arguments passed to the function classifier.
进一步的参数传递给函数classifier。
Details
详情----------Details----------
This function builds the specified classifier and predicts the class labels of new observations. Hence,
此功能建立指定的分类和预测新的观测类的标签。因此，
值----------Value----------
A object of class predoutput-class; Predicted classes can be seen by show(predoutput)
predoutput-class;预测类的类对象可以看出show(predoutput)
作者（S）----------Author(s)----------
Christoph Bernau <a href="mailto:bernau@ibe.med.uni-muenchen.de">bernau@ibe.med.uni-muenchen.de</a>
Anne-Laure Boulesteix <a href="mailto:boulesteix@ibe.med.uni-muenchen.de">boulesteix@ibe.med.uni-muenchen.de</a>
参考文献----------References----------
CMA - A comprehensive Bioconductor package for supervised classification with high dimensional data.
参见----------See Also----------
GeneSelection, tune, evaluation, compBoostCMA, dldaCMA, ElasticNetCMA, fdaCMA, flexdaCMA, gbmCMA, knnCMA, ldaCMA, LassoCMA, nnetCMA, pknnCMA, plrCMA, pls_ldaCMA, pls_lrCMA, pls_rfCMA, pnnCMA, qdaCMA, rfCMA,
GeneSelection，tune，evaluation，compBoostCMA，dldaCMA，ElasticNetCMA，fdaCMA，flexdaCMA，gbmCMA，knnCMA，ldaCMA，LassoCMA，nnetCMA，pknnCMA，plrCMA，pls_ldaCMA，pls_lrCMA ，pls_rfCMA，pnnCMA，qdaCMA，rfCMA
举例----------Examples----------
### a simple k-nearest neighbour example[＃一个简单的k-近邻例如]
### datasets[＃＃集]
## Not run: plot(x)[＃不运行：图（X）]
data(golub)
golubY <- golub[,1]
golubX <- as.matrix(golub[,-1])
###Splitting data into training and test set[＃＃分割成培训集和测试集数据]
X.tr<-golubX[1:30]
X.new<-golubX[31:39]
y.tr<-golubY[1:30]
### 1. GeneSelection[＃＃1。 GeneSelection]
selttest <- GeneSelection(X=X.tr, y=y.tr, method = "t.test")
### 2. tuning[＃＃2。调音]
tunek <- tune(X.tr, y.tr, genesel = selttest, nbgene = 20, classifier = knnCMA)
### 3. classification[＃＃3。分类]
pred <- prediction(X.tr=X.tr,y.tr=y.tr,X.new=X.new, genesel = selttest,
tuneres = tunek, nbgene = 20, classifier = knnCMA)
### show and analyze results:[＃显示和分析结果：]
show(pred)
## End(Not run)[＃结束（不运行）]
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 15:25:41
