GenerateLearningsets(CMA)
GenerateLearningsets()所属R语言包：CMA
Repeated Divisions into learn- and tets sets
重复分部学习和四面体套
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Due to very small sample sizes, the classical division learnset/testset does not give accurate information about the classification performance. Therefore, several different divisions should be used and aggregated. The implemented methods are discussed in Braga-Neto and Dougherty (2003) and Molinaro et al. (2005) whose terminology is adopted.
由于样本量非常小，古典的分工learnset，/ testset不准确的信息有关的分类性能。因此，应使用几个不同的部门，汇总。布拉加，内托和多尔蒂（2003）和莫利纳罗等人所实施的方法进行了讨论。 （2005）采用的术语。
This function is usually the basis for all deeper analyses.
此功能通常是所有更深入的分析的基础上。
用法----------Usage----------
GenerateLearningsets(n, y, method = c("LOOCV", "CV", "MCCV", "bootstrap"),
fold = NULL, niter = NULL, ntrain = NULL, strat = FALSE)
参数----------Arguments----------
参数：n
The total number of observations in the available data set. May be missing if y is provided instead.
在现有数据集的观测总数。可能是missing如果y。
参数：y
A vector of class labels, either numeric or a factor. Must be given if strat=TRUE or n is not specified.
一类标签的向量，要么numeric或factor。如果strat=TRUE或n没有指定必须给予。
参数：method
Which kind of scheme should be used to generate divisions into learning sets and test sets ? Can be one of the following:
应采用哪一种方案生成师学习集和测试集？可以是下列之一：
"LOOCV"Leaving-One-Out Cross Validation.
LOOCV“留一交叉验证。
"CV"(Ordinary) Cross-Validation. Note that fold must as well be specified.
“简历”（普通）的交叉验证。注意fold以及被指定。
"MCCV"Monte-Carlo Cross Validation, i.e. random divisions into learning sets with ntrain(s.below) observations and tests sets with ntrain observations.
的“MCCV”蒙特卡洛交叉验证，即深入学习ntrain（s.below）观察和测试集的随机部门设置ntrain意见。
"bootstrap"Learning sets are generated by drawing n times with replacement from all observations. Those not drawn not all form the test set.
“引导”学习集生成绘制更换所有观测n倍。那些不画不是所有形式的测试集。
参数：fold
Gives the number of CV-groups. Used only when method="CV"
给人简历群体。只有当使用method="CV"
参数：niter
Number of iterations (s.details).
迭代次数（S.details).
参数：ntrain
Number of observations in the learning sets. Used only when method="MCCV".
在学习套的意见。只有当method="MCCV"使用。
参数：strat
Logical. Should stratified sampling be performed, i.e. the proportion of observations from each class in the learning sets be the same as in the whole data set ? Does not apply for method = "LOOCV".
逻辑。应该进行分层抽样，即从每类在学习观察的比例设置在整个数据集相同吗？不适用于method = "LOOCV"。
Details
详情----------Details----------
When method="CV", niter gives the number of times the whole CV-procedure is repeated. The output matrix has then foldxniter rows. When method="MCCV" or method="bootstrap", niter is simply the number of considered learning sets.
当method="CV"，niter给整个简历的过程是重复的次数。输出矩阵foldXniter行。当method="MCCV"或method="bootstrap"，niter简直是考虑学习组的数量。
Note that method="CV",fold=n is equivalent to method="LOOCV".
请注意，method="CV",fold=n相当于method="LOOCV"的。
值----------Value----------
An object of class learningsets
一个对象的类learningsets
作者（S）----------Author(s)----------
Martin Slawski <a href="mailto:ms@cs.uni-sb.de">ms@cs.uni-sb.de</a>
Anne-Laure Boulesteix <a href="mailto:boulesteix@ibe.med.uni-muenchen.de">boulesteix@ibe.med.uni-muenchen.de</a>
Christoph Bernau <a href="mailto:bernau@ibe.med.uni-muenchen.de">bernau@ibe.med.uni-muenchen.de</a>
参考文献----------References----------
methods.
CMA - A comprehensive Bioconductor package for supervised classification with high dimensional data. BMC Bioinformatics 9: 439
参见----------See Also----------
learningsets, GeneSelection, tune,
learningsets，GeneSelection，tune
举例----------Examples----------
# LOOCV[LOOCV]
loo <- GenerateLearningsets(n=40, method="LOOCV")
show(loo)
# five-fold-CV[五倍的简历]
CV5 <- GenerateLearningsets(n=40, method="CV", fold=5)
show(loo)
# MCCV[MCCV]
mccv <- GenerateLearningsets(n=40, method = "MCCV", niter=3, ntrain=30)
show(mccv)
# Bootstrap[引导]
boot <- GenerateLearningsets(n=40, method="bootstrap", niter=3)
# stratified five-fold-CV[分层五倍简历]
set.seed(113)
classlabels <- sample(1:3, size = 50, replace = TRUE, prob = c(0.3, 0.5, 0.2))
CV5strat <- GenerateLearningsets(y = classlabels, method="CV", fold=5, strat = TRUE)
show(CV5strat)
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 15:20:54
