edd(edd)
edd()所属R语言包：edd
new expression density diagnostics interface
新的表达密度诊断接口
译者：生物统计家园网 机器人LoveR
描述----------Description----------
this will replace edd.unsupervised; has more sensible parameters
这将取代edd.unsupervised有更明智的参数
用法----------Usage----------
edd(eset, distList=eddDistList, tx=c(sort,flatQQNormY)[[1]],
refDist=c("multiSim", "theoretical")[1],
method=c("knn", "nnet", "test")[1], nRowPerCand=100, ...)
参数----------Arguments----------
参数：eset
eset – instance of Biobase ExpressionSet class
ESET - BIOBASEExpressionSet类的实例
参数：distList
distList – list comprised of eddDist objects
组成eddDist对象distList - 名单
参数：tx
tx – transformation of data and reference prior to classification
TX - 数据和参考之前分类的转换
参数：refDist
refDist – type of reference distribution system to use
refDist - 参考配电系统的类型使用
参数：method
method – type of classifier to use. knn is k-nearest neighbors, nnet is neural net, test is max p-value from ks.test
方法 - 类型分类使用。 KNN是K-近邻，nnet是神经网络，测试是从ks.test最大p值
参数：nRowPerCand
nRowPerCand – number of realizations for a multiSim reference system
nRowPerCand - 一个Multisim参考系统实现数
参数：...
... – parameters to classifiers
... - 分类参数
Details
详情----------Details----------
Classifies genes according to distributional shape, by comparing observed expression distributions to a collection of references, which may be simulated or evaluated theoretically.
根据分布形状的基因进行分类，通过比较观测到的表达分布的参考文献的收集，这可能是模拟或评估理论上。
The distList argument is important. It enumerates the catalog of distributions for classification of gene expression vectors by distributional shape. See the HOWTO-edd vignette for information on how this list is constructed and how it can be extended.
distList参数是重要的。它列举了基因表达向量的分类，分布形状分布的目录。看到这个名单是如何构建的信息，以及如何可以延长的HOWTO EDD暗角。
The tx argument specifies how the data are processed for comparison to the reference catalog. This is a function on a vector returning a vector, but the input and the output need not have the same length. The default value of tx is sort, which entails that the order statistics are treated as multivariate data for classification.
TX参数指定如何处理的数据作比较的参考目录。这是一个向量函数返回一个向量，输入和输出，但不必具有相同的长度。 TX默认值是排序，这就需要作为多元数据进行分类处理，以便统计。
The refDist argument selects the type of reference catalog. Options are 'multiSim', for which the reference consists of nRowPerCand realizations of each catalog entry, and 'theoretical', for which the reference consists of one vector of quantiles for each catalog entry.
refDist参数选择的参考目录。选项是“Multisim的参考nRowPerCand实现每个目录条目组成，与”理论“，其中的参考之一，每个目录项的位数向量组成。
The method argument selects the type of classifier. It would be desirable to allow this to be a function, but there is insufficient structure on classifier argument and return value structure to permit this at present; see the e1071 package for some work on handling various classifiers programmatically (e.g., tune).
方法的参数选择的类型分类。这是一个功能，这将是可取的，但有结构上的分类参数和返回值的结构，允许目前的不足;看到一些编程处理各种分类工作e1071包（例如，tune ）。
值----------Value----------
a character vector or factor depending on the classifier
一个字符向量或根据分级的因素
作者（S）----------Author(s)----------
Vince Carey <stvjc@channing.harvard.edu>
参见----------See Also----------
ExpressionSet
ExpressionSet
举例----------Examples----------
require(Biobase)
data(sample.ExpressionSet)
# should filter to genes with reasonable variation[应过滤到合理的变异基因]
table( edd(sample.ExpressionSet, meth="nnet", size=10, decay=.2) )
library(golubEsets)
data(Golub_Merge)
madvec <- apply(exprs(Golub_Merge),1,mad)
minvec <- apply(exprs(Golub_Merge),1,min)
keep <- (madvec > median(madvec)) & (minvec > 300)
gmfilt <- Golub_Merge[keep==TRUE,]
ALL <- gmfilt$ALL.AML=="ALL"
gall <- gmfilt[,ALL==TRUE]
gaml <- gmfilt[,ALL==FALSE]
alldists <- edd(gall, meth="nnet", size=10, decay=.2)
amldists <- edd(gaml, meth="nnet", size=10, decay=.2)
table(alldists,amldists)
amldists2 <- edd(gaml, meth="nnet", refDist="theoretical", size=10, decay=.2)
table(amldists,amldists2)
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 17:00:52
