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R语言 qpgraph包 qpGraph()函数中文帮助文档(中英文对照)

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发表于 2012-2-26 11:39:55 | 显示全部楼层 |阅读模式
qpGraph(qpgraph)
qpGraph()所属R语言包:qpgraph

                                         The qp-graph
                                         QP-图

                                         译者:生物统计家园网 机器人LoveR

描述----------Description----------

Obtains a qp-graph from a matrix of non-rejection rates
从非排斥反应发生率的矩阵得到的QP图


用法----------Usage----------


qpGraph(nrrMatrix, threshold=NULL, topPairs=NULL, pairup.i=NULL, pairup.j=NULL,
        return.type=c("adjacency.matrix", "edge.list", "graphNEL", "graphAM"))



参数----------Arguments----------

参数:nrrMatrix
matrix of non-rejection rates.
矩阵非排斥反应发生率。


参数:threshold
threshold on the non-rejection rate above which pairs of variables are assumed to be disconnected in the resulting qp-graph.
对上述这对变量被假设为在QP图断开的非废品率的阈值。


参数:topPairs
number of edges from the top of the ranking, defined by the non-rejection rates in nrrMatrix, to use to form the resulting qp-graph. This parameter is incompatible with a value different from NULL in threshold.
nrrMatrix非排斥反应发生率的定义,从排名的顶部边缘,数量,使用形成产生QP图。此参数是不同的值从NULLthreshold不相容。


参数:pairup.i
subset of vertices to pair up with subset pairup.j
顶点子集,子集pairup.j配对


参数:pairup.j
subset of vertices to pair up with subset pairup.i
顶点子集,子集pairup.i配对


参数:return.type
type of data structure on which the resulting undirected graph should be returned. Either a logical adjacency matrix with cells set to TRUE when the two indexing variables are connected in the qp-graph (default), or a list of edges in a matrix where each row corresponds to one edge and the two columns contain the two vertices defining each edge, or a graphNEL-class object, or a graphAM-class object.
类型的数据结构造成的无向图上应退还。无论是设置为TRUE时QP-图形(默认),或边矩阵中的每一行对应一个边缘和两列包含两个顶点定义的列表中连接两个索引变量的逻辑单元的邻接矩阵每个边缘,或graphNEL-class对象,或graphAM-class对象。


Details

详情----------Details----------

This function requires the graph package when return.type=graphNEL or return.type=graphAM.
此功能需要包graph时return.type=graphNEL或return.type=graphAM。


值----------Value----------

The resulting qp-graph as either an adjacency matrix, a graphNEL object or a graphAM object, depending on the value of the return.type parameter. Note that when some gold-standard graph is available for comparison, a value for the parameter threshold can be found by calculating a precision-recall curve with qpPrecisionRecall with respect to this gold-standard, and then using qpPRscoreThreshold. Parameters threshold and topPairs are mutually exclusive, that is, when we specify with topPairs=n that we want a qp-graph with n edges then threshold cannot be used.
造成QP-图的邻接矩阵,graphNEL对象或graphAM对象,取决于return.type参数值。请注意,一些黄金标准图进行比较时,参数threshold值可以计算qpPrecisionRecall就这个黄金标准,然后使用精密召回曲线qpPRscoreThreshold。参数threshold和topPairs是相互排斥的,也就是说,当我们用topPairs=n指定我们希望与QPn边图,然后threshold可以不使用。


作者(S)----------Author(s)----------


R. Castelo and A. Roverato



参考文献----------References----------

Gaussian graphical model search from microarray data with p larger than n, J. Mach. Learn. Res., 7:2621-2650, 2006.

参见----------See Also----------

qpNrr qpAvgNrr qpEdgeNrr qpAnyGraph qpGraphDensity qpClique qpPrecisionRecall qpPRscoreThreshold
qpNrrqpAvgNrrqpEdgeNrrqpAnyGraphqpGraphDensityqpCliqueqpPrecisionRecallqpPRscoreThreshold


举例----------Examples----------


require(mvtnorm)

nVar <- 50  ## number of variables[#变量]
maxCon <- 5 ## maximum connectivity per variable[#最大连接每个变量]
nObs <- 30  ## number of observations to simulate[#号观测到模拟]

set.seed(123)

A <- qpRndGraph(p=nVar, d=maxCon)
Sigma <- qpG2Sigma(A, rho=0.5)
X <- rmvnorm(nObs, sigma=as.matrix(Sigma))

## estimate non-rejection rates[#估计非排斥反应发生率。]
nrr.estimates <- qpNrr(X, q=5, verbose=FALSE)

## the higher the threshold[#较高的阈值]
g <- qpGraph(nrr.estimates, threshold=0.9)

## the denser the qp-graph[#致密的QP图]
(sum(g)/2) / (nVar*(nVar-1)/2)

## the lower the threshold[#阈值较低]
g <- qpGraph(nrr.estimates, threshold=0.5)

## the sparser the qp-graph[#稀疏的QP图]
(sum(g)/2) / (nVar*(nVar-1)/2)


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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