qpGenNrr(qpgraph)
qpGenNrr()所属R语言包：qpgraph
Generalized non-rejection rate estimation
广义的非排斥反应率估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimates generalized non-rejection rates for every pair of variables from two or more data sets.
估计每双从两个或多个数据集的变量的广义的非排斥反应发生率。
用法----------Usage----------
## S4 method for signature 'ExpressionSet'
qpGenNrr(X, datasetIdx=1, qOrders=NULL, I=NULL, restrict.Q=NULL,
fix.Q=NULL, return.all=FALSE, 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'
qpGenNrr(X, datasetIdx=1, qOrders=NULL, I=NULL, restrict.Q=NULL,
fix.Q=NULL, return.all=FALSE, 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'
qpGenNrr(X, datasetIdx=1, qOrders=NULL, I=NULL, restrict.Q=NULL,
fix.Q=NULL, return.all=FALSE, 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 average non-rejection rates. It can be an ExpressionSet object, a data frame or a matrix.
数据集从那里来估计平均无排斥反应发生率。它可以是一个ExpressionSet对象，一个数据框或一个矩阵。
参数：datasetIdx
either a single number, or a character string, indicating the column in the phenotypic data of the ExpressionSet object, or in the input matrix or data frame, containing the indexes to the data sets. Alternatively, it can be a vector of these indexes with as many positions as samples.
无论是单数，或一个字符串，表明该在ExpressionSet对象的表型数据的列，或在输入矩阵或数据框，包含索引的数据集。另外，它还可以作为样本的许多职位是这些指标的向量。
参数：qOrders
either a NULL value (default) indicating that a default guess on the q-order will be employed for each data set or a vector of particular orders with one for each data set. The default guess corresponds to the floor of the median value among the valid q orders of the data set.
任何一个NULL值（默认），表明一个默认的猜测Q-顺序将受聘为每个数据集或一个特定订单的向量为每个数据集之一。默认的猜测对应的中位数的Q数据集之间的有效订单价值的地板。
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：return.all
logical; if TRUE all intervining non-rejection rates will be return in a matrix per dataset within a list; FALSE (default) if only generalized non-rejection rates should be returned.
逻辑;如果为TRUE，将所有intervining非排斥反应发生率的回报，在每个数据集的矩阵列表内; FALSE（默认值），如果只广义的非排斥率应退还。
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 the data is a data frame or a matrix, the longer dimension is the one defining the random variables; if FALSE, then random variables are assumed to be at the columns of the data frame or matrix.
逻辑;如果为TRUE，则假定数据是一个数据框或一个矩阵时，较长的尺寸是一个定义的随机变量;如果为FALSE，则随机变量的假设是在数据框或矩阵列。
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.
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。
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;如果为FALSE则更快的C实现使用（默认）;如果TRUE，那么只有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 when specifying a vector of particular orders q, these values should be in the range 1 to min(p,n-3), where p is the number of variables and n the number of observations for the corresponding data set. The computational cost increases linearly within each q value 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.
请注意，当指定一个特定订单的向量q，这些值应该在范围从1到min(p,n-3)，p是变量n数，相应的数据集的观测。计算成本的增加在每个q值和二次线性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。
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 list containing the following two or more entries: a first one with name genNrr with a dspMatrix-class symmetric matrix of estimated generalized non-rejection rates with the diagonal set to NA values. When using the arguments pairup.i and pairup.j, those cells outside the constraint pairs will get also a NA value; a second one with name qOrders with the q-orders employed in the calculation for each data set; if return.all=TRUE then there will be one additional entry for each data set containing the matrix of the non-rejection rates estimated from that data set with the corresponding q-order, using the indexing value of the data set as entry name.
一个列表，其中包含以下两个或两个以上的项目：一个名字的第一个genNrrdspMatrix-class估计广义非排斥反应发生率对称矩阵的对角线集NA值。当使用参数pairup.i和pairup.j，外约束对这些单元会得到一个NA值，一个名字的第二个qOrders与Q-订单受雇于每个数据集的计算;如果return.all=TRUE然后将每个数据项集，其中包含矩阵的非排斥反应发生率与相应的Q-为了数据估计，使用索引值数据项的名称。
Note, however, that when estimateTime=TRUE, then instead of the list with matrices of estimated (generalized) 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，然后估计（广义）的非排斥反应发生率的矩阵列表，而不是一个向量计算完成指定天，小时，分钟和秒的估计数，则返回。
作者（S）----------Author(s)----------
R. Castelo and A. Roverato
参考文献----------References----------
networks from microarray data with qp-graphs. J. Comp. Biol., 16(2):213-227, 2009.
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----------
qpNrr qpAvgNrr qpEdgeNrr qpHist qpGraphDensity qpClique
qpNrrqpAvgNrrqpEdgeNrrqpHistqpGraphDensityqpClique
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)
A1 <- qpRndGraph(p=nVar, d=maxCon)
A2 <- qpRndGraph(p=nVar, d=maxCon)
Sigma1 <- qpG2Sigma(A1, rho=0.5)
Sigma2 <- qpG2Sigma(A2, rho=0.5)
X1 <- rmvnorm(nObs, sigma=as.matrix(Sigma1))
X2 <- rmvnorm(nObs, sigma=as.matrix(Sigma2))
nrr.estimates <- qpGenNrr(rbind(X1, X2), datasetIdx=rep(1:2, each=nObs),
long.dim.are.variables=FALSE, verbose=FALSE)
## distribution of generalized non-rejection rates for the common present edges[＃广义非排斥反应发生率，目前常见的边缘分布]
summary(nrr.estimates$genNrr[upper.tri(nrr.estimates$genNrr) & A1 & A2])
## distribution of generalized non-rejection rates for the present edges specific to A1[广义非排斥反应发生率目前为A1边缘分布]
summary(nrr.estimates$genNrr[upper.tri(nrr.estimates$genNrr) & A1 & !A2])
## distribution of generalized non-rejection rates for the present edges specific to A2[具体到A2目前边缘分布的广义非排斥反应发生率]
summary(nrr.estimates$genNrr[upper.tri(nrr.estimates$genNrr) & !A1 & A2])
## distribution of generalized non-rejection rates for the common missing edges[＃共同失踪的边缘分布的广义非排斥反应发生率]
summary(nrr.estimates$genNrr[upper.tri(nrr.estimates$genNrr) & !A1 & !A2])
## compare with the average non-rejection rate on the pooled data set[＃比较汇集的数据集上的平均非排斥率]
avgnrr.estimates <- qpAvgNrr(rbind(X1, X2), long.dim.are.variables=FALSE, verbose=FALSE)
## distribution of average non-rejection rates for the common present edges[＃平均无排斥反应发生率，目前常见的边缘分布]
summary(avgnrr.estimates[upper.tri(avgnrr.estimates) & A1 & A2])
## distribution of average non-rejection rates for the present edges specific to A1[＃平均无排斥反应发生率目前为A1边缘分布]
summary(avgnrr.estimates[upper.tri(avgnrr.estimates) & A1 & !A2])
## distribution of average non-rejection rates for the present edges specific to A2[具体到A2目前边缘分布平均无排斥反应发生率]
summary(avgnrr.estimates[upper.tri(avgnrr.estimates) & !A1 & A2])
## distribution of average non-rejection rates for the common missing edges[＃共同失踪的边缘分布平均无排斥反应发生率]
summary(avgnrr.estimates[upper.tri(avgnrr.estimates) & !A1 & !A2])
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 2012-2-26 11:39:35
