qpNrr(qpgraph)
qpNrr()所属R语言包：qpgraph
Non-rejection rate estimation
非废品率估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimates non-rejection rates for every pair of variables.
估计为每对变量的非排斥反应发生率。
用法----------Usage----------
## S4 method for signature 'ExpressionSet'
qpNrr(X, q=1, I=NULL, restrict.Q=NULL, fix.Q=NULL, nTests=100,
alpha=0.05, pairup.i=NULL, pairup.j=NULL,
verbose=TRUE, identicalQs=TRUE, exact.test=TRUE,
R.code.only=FALSE, clusterSize=1, estimateTime=FALSE,
nAdj2estimateTime=10)
## S4 method for signature 'data.frame'
qpNrr(X, q=1, I=NULL, restrict.Q=NULL, fix.Q=NULL, nTests=100,
alpha=0.05, pairup.i=NULL, pairup.j=NULL,
long.dim.are.variables=TRUE, verbose=TRUE,
identicalQs=TRUE, exact.test=TRUE, R.code.only=FALSE,
clusterSize=1, estimateTime=FALSE, nAdj2estimateTime=10)
## S4 method for signature 'matrix'
qpNrr(X, q=1, I=NULL, restrict.Q=NULL, fix.Q=NULL, nTests=100,
alpha=0.05, pairup.i=NULL, pairup.j=NULL,
long.dim.are.variables=TRUE, verbose=TRUE, identicalQs=TRUE,
exact.test=TRUE, R.code.only=FALSE, clusterSize=1,
estimateTime=FALSE, nAdj2estimateTime=10)
参数----------Arguments----------
参数：X
data set from where to estimate the non-rejection rates. It can be an ExpressionSet object, a data frame or a matrix.
数据集从何处来估计非排斥反应发生率。它可以是一个ExpressionSet对象，一个数据框或一个矩阵。
参数：q
partial-correlation order to be employed.
被聘用部分相关秩序。
参数：I
indexes or names of the variables in X that are discrete. When X is an ExpressionSet then I may contain only names of the phenotypic variables in X. See details below regarding this argument.
索引或X，变量的名称，是离散的。当X是ExpressionSet然后I可能只包含在X的表型变量的名字。详情请参阅下面关于这个论点。
参数：restrict.Q
indexes or names of the variables in X that restrict the sample space of conditioning subsets Q.
X限制空调子集问：样本空间的变量的索引或名称
参数：fix.Q
indexes or names of the variables in X that should be fixed within every conditioning conditioning subsets Q.
索引或变量的名称X应固定在每一个空调空调子集问：
参数：nTests
number of tests to perform for each pair for variables.
测试，以执行对每个变量的数目。
参数：alpha
significance level of each test.
每个测试的显着水平。
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：long.dim.are.variables
logical; if TRUE it is assumed that when data are in a data frame or in a matrix, the longer dimension is the one defining the random variables (default); if FALSE, then random variables are assumed to be at the columns of the data frame or matrix.
逻辑;如果TRUE假设数据在一个数据框，或在一个矩阵时，较长的尺寸是一个随机变量的定义（默认）;如果FALSE，然后随机变量假定的数据框或矩阵列。
参数：verbose
show progress on the calculations.
计算表明进展。
参数：identicalQs
use identical conditioning subsets for every pair of vertices (default), otherwise sample a new collection of nTests subsets for each pair of vertices.
每对顶点（默认）使用相同的调节亚群，否则新的集合样本每对顶点的nTests亚群。
参数：exact.test
logical; if FALSE an asymptotic conditional independence test is employed with mixed (i.e., continuous and discrete) data; if TRUE (default) then an exact conditional independence test with mixed data is employed. See details below regarding this argument.
逻辑;如果FALSE一个渐进的条件独立性测试采用混合（即连续和离散）数据;如果TRUE（默认），然后一个确切的数据好坏参半，有条件的独立测试采用。详情请参阅下面关于这个论点。
参数：R.code.only
logical; if FALSE then the faster C implementation is used (default); if TRUE then only R code is executed.
逻辑;如果FALSE然后用C实现更快（默认）;如果TRUE然后只R代码被执行。
参数：clusterSize
size of the cluster of processors to employ if we wish to speed-up the calculations by performing them in parallel. A value of 1 (default) implies a single-processor execution. The use of a cluster of processors requires having previously loaded the packages snow and rlecuyer.
聘请如果我们希望加快执行并行计算处理器聚类的大小。 1（默认）值意味着一个单处理器的执行。要求曾装包snow和rlecuyer使用的处理器的聚类。
参数：estimateTime
logical; if TRUE then the time for carrying out the calculations with the given parameters is estimated by calculating for a limited number of adjacencies, specified by nAdj2estimateTime, and extrapolating the elapsed time; if FALSE (default) calculations are performed normally till they finish.
逻辑;如果TRUE然后给定的参数进行计算的时间估计是由nAdj2estimateTime指定的邻接数量有限，通过计算，推断所用的时间;如果FALSE （默认）计算正常执行，直到他们完成。
参数：nAdj2estimateTime
number of adjacencies to employ when estimating the time of calculations (estimateTime=TRUE). By default this has a default value of 10 adjacencies and larger values should provide more accurate estimates. This might be relevant when using a cluster facility.
邻接的数量估计计算时间（estimateTime=TRUE）时聘请。默认情况下，有一个10邻接和较大的值的默认值应该提供更准确的估计。这可能是相关使用聚类设施时。
Details
详情----------Details----------
Note that for pure continuous data the possible values of q should be in the range 1 to min(p, n-3), where p is the number of variables and n the number of observations. The computational cost increases linearly with q and quadratically in p. When setting identicalQs to FALSE the computational cost may increase between 2 times and one order of magnitude (depending on p and q) while asymptotically the estimation of the non-rejection rate converges to the same value. Full details on the calculation of the non-rejection rate can be found in Castelo and Roverato (2006).
请注意，q纯连续数据的可能值应该在范围从1到min(p, n-3)，其中p是变量的数目和n的若干意见。计算成本的增加而线性与q“二次p。当设置identicalQsFALSE计算成本可能会增加2倍之间和一个量级（取决于p和q）渐近估计非排斥率收敛到相同的值。非废品率计算的全部细节，可以发现在城堡和Roverato（2006）。
When I is set different to NULL then mixed graphical model theory is employed and, concretely, it is assumed that the data comes from an homogeneous conditional Gaussian distribution. In this setting further restrictions to the maximum value of q apply, concretely, it cannot be smaller than p plus the number of levels of the discrete variables involved in the marginal distributions employed by the algorithm. By default, with exact.test=TRUE, an exact test for conditional independence is employed, otherwise an asymptotic one will be used. Full details on these features can be found in Tur and Castelo (2011).
当INULL然后混合图形模型理论采用，具体设置不同，它是假设，从同质化的条件高斯分布的数据。在此设置的最大值进一步限制q申请，具体的，它不能比p小加参与该算法采用的边缘分布的离散变量的水平数。默认情况下，exact.test=TRUE用，有条件独立的确切测试就业，否则一个渐进的将使用。对这些功能的全部细节，可以发现在图尔堡（2011）。
值----------Value----------
A dspMatrix-class symmetric matrix of estimated non-rejection rates with the diagonal set to NA values. If arguments pairup.i and pairup.j are employed, those cells outside the constrained pairs will get also a NA value.
一个dspMatrix-class对称矩阵的估计非排斥反应发生率与对角线设置为NA值。如果参数pairup.i和pairup.j就业，外约束对这些单元会得到也NA值。
Note, however, that when estimateTime=TRUE, then instead of the matrix of estimated non-rejection rates, a vector specifying the estimated number of days, hours, minutes and seconds for completion of the calculations is returned.
但是请注意，当estimateTime=TRUE，然后，而不是估计的非排斥反应发生率的矩阵，向量指定天，小时，分钟和秒计算完成的估计数，则返回。
作者（S）----------Author(s)----------
R. Castelo, A. Roverato and I. Tur
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.
In Proc. 27th Conference on Uncertainty in Artificial Intelligence, F.G. Cozman and A. Pfeffer eds., pp. 689-697, AUAI Press, ISBN 978-0-9749039-7-2, Barcelona, 2011.
参见----------See Also----------
qpAvgNrr qpEdgeNrr qpHist qpGraphDensity qpClique
qpAvgNrrqpEdgeNrrqpHistqpGraphDensityqpClique
举例----------Examples----------
library(mvtnorm)
nVar <- 50 ## number of variables[＃变量]
maxCon <- 3 ## 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))
nrr.estimates <- qpNrr(X, q=3, verbose=FALSE)
## distribution of non-rejection rates for the present edges[＃非排斥反应发生率，目前的边缘分布]
summary(nrr.estimates[upper.tri(nrr.estimates) & A])
## distribution of non-rejection rates for the missing edges[＃非排斥反应发生率的分布失踪边缘]
summary(nrr.estimates[upper.tri(nrr.estimates) & !A])
## using R code only this would take much more time[＃R代码，这需要更多的时间]
qpNrr(X, q=3, R.code.only=TRUE, estimateTime=TRUE)
## Not run: [＃无法运行：]
library(snow)
library(rlecuyer)
## only for moderate and large numbers of variables the[＃仅适用于中度和大量的变量]
## use of a cluster of processors speeds up the calculations[＃使用了计算处理器速度的聚类]
nVar <- 500
maxCon <- 3
A <- qpRndGraph(p=nVar, d=maxCon)
Sigma <- qpG2Sigma(A, rho=0.5)
X <- rmvnorm(nObs, sigma=as.matrix(Sigma))
system.time(nrr.estimates <- qpNrr(X, q=10, verbose=TRUE))
system.time(nrr.estimates <- qpNrr(X, q=10, verbose=TRUE, clusterSize=4))
## 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。
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。
|
发表于 2012-2-26 11:40:39
