selectFFS(BioSeqClass)
selectFFS()所属R语言包：BioSeqClass
feature forward selection
功能正向选择
译者：生物统计家园网 机器人LoveR
描述----------Description----------
feature forward selection.
配备着选择。
用法----------Usage----------
selectFFS(data, accCutoff, stop.n,
classifyMethod="knn",cv=10)
参数----------Arguments----------
参数：data
a data frame including the feature matrix and class label. The last column is a vector of class label comprising of "-1" or "+1"; Other columns are features.
一个数据框，包括特征矩阵和类标号。最后一列是一个阶级标签的向量，“-1”或“+1”的组成;其他列的功能。
参数：accCutoff
a numeric indicating the minimum difference of accuracy between two models in selectFFS. Feature subsets will stop increasing when the difference of accuracy is samll than accCutoff.
数字表明两个模型之间的差异最小的准确性，在selectFFS。特征子集，将停止增加时精度的差异比accCutoff球¢。
参数：stop.n
number of selected features by selectFFS.
数selectFFS选择的功能。
参数：classifyMethod
a string for the classification method. This must be one of the strings "libsvm", "svmlight", "NaiveBayes", "randomForest", "knn", "tree", "nnet", "rpart", "ctree", "ctreelibsvm", "bagging".
分类方法的字符串。这必须是一个字符串“libsvm的”，“svmlight”，“NaiveBayes”，“randomForest”，“KNN”，“树”，“nnet”，“软件rpart”，“ctree” “ctreelibsvm”，“套袋”。
参数：cv
an integer for the time of cross validation, or a string "leave\_one\_out" for the jacknife test.
交叉验证的时间，或一个字符串“假\ _one \ _OUT”Rsync常常用于测试的整数。
Details
详情----------Details----------
selectFFS uses FFS (Feature Forword Selection) method to increase feature, and use NNA (Neareast Neighbor Analysis) to evaluate the performance of feature subset. Two conditions are used to stop feature increasing: control the difference of accuracy between two models; control the number of selected features by Parameter "stop.n".
selectFFS使用的FFS（特征FORWORD选择）方法来增加功能，并使用NNA（Neareast邻居分析）评估性能特征子集。停止功能，增加两个条件控制精度两款车型之间的差异;控制选择功能参数“stop.n”的数量。
作者（S）----------Author(s)----------
Hong Li
举例----------Examples----------
## read positive/negative sequence from files.[＃从文件中读取正/负序列。]
tmpfile1 = file.path(.path.package("BioSeqClass"), "example", "acetylation_K.pos40.pep")
tmpfile2 = file.path(.path.package("BioSeqClass"), "example", "acetylation_K.neg40.pep")
posSeq = as.matrix(read.csv(tmpfile1,header=FALSE,sep="\t",row.names=1))[,1]
negSeq = as.matrix(read.csv(tmpfile2,header=FALSE,sep="\t",row.names=1))[,1]
seq=c(posSeq,negSeq)
classLable=c(rep("+1",length(posSeq)),rep("-1",length(negSeq)) )
data = data.frame(featureBinary(seq),classLable)
if(interactive()){
## Use KNN to evaluate the performance of feature subset, [＃使用KNN算法的特征子集的性能评估，]
## and use Feature Forword Selection method to increase feature.[＃和使用功能FORWORD选择方法，以增加功能。]
# If the difference of accuracy between two models is less than 0.01, feature [如果精度两款车型之间的差异是小于0.01，功能]
# selection will stop.[选择将停止。]
FFS_NNA_CV5 = selectFFS(data,accCutoff=0.01,classifyMethod="knn",cv=5)
# If 20 features have been selected, feature selection will stop.[如果已选定20功能，选择功能将停止。]
FFS_NNA_CV5 = selectFFS(data,stop.n=3,classifyMethod="knn",cv=5)
# If any one condiction is satisfied, feature selection will stop.[如果任何一个condiction满意，功能选择将停止。]
FFS_NNA_CV5 = selectFFS(data,accCutoff=0.001,stop.n=100,classifyMethod="knn",cv=5)
}
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 13:43:03
