qpClique(qpgraph)
qpClique()所属R语言包：qpgraph
Complexity of the resulting qp-graphs
QP-图的复杂性
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Calculates and plots the size of the largest maximal clique (the so-called clique number or maximum clique size) as function of the non-rejection rate.
计算和图的规模最大的集团最大的非废品率的功能（所谓的团数或最大团的大小）。
用法----------Usage----------
qpClique(nrrMatrix, N=NA, threshold.lim=c(0,1), breaks=5, plot=TRUE,
exact.calculation=TRUE, approx.iter=100,
qpCliqueOutput=NULL, density.digits=0,
logscale.clqsize=FALSE,
titleclq="maximum clique size as function of threshold",
verbose=FALSE)
参数----------Arguments----------
参数：nrrMatrix
matrix of non-rejection rates.
矩阵非排斥反应发生率。
参数：N
number of observations from where the non-rejection rates were estimated.
非排斥反应发生率估计从那里观测的数量。
参数：threshold.lim
range of threshold values on the non-rejection rate.
对非废品率阈值的范围。
参数：breaks
either a number of threshold bins or a vector of threshold breakpoints.
无论是阈值箱数或矢量阈值的断点。
参数：plot
logical; if TRUE makes a plot of the result; if FALSE it does not.
逻辑;如果为TRUE，使结果的图;如果为FALSE，它没有。
参数：exact.calculation
logical; if TRUE then the exact clique number is calculated; if FALSE then a lower bound is given instead.
逻辑;如果为TRUE，那么确切的团数计算;如果false，那么下限代替。
参数：approx.iter
number of iterations to be employed in the calculation of the lower bound (i.e., only applies when exact.calculation=FALSE).
迭代次数，在计算下界（即只适用于当exact.calculation=FALSE）。
参数：qpCliqueOutput
output from a previous call to qpClique. This allows one to plot the result changing some of the plotting parameters without having to do the calculation again.
从以前的通话qpClique输出。这允许一个绘制一些绘图参数的改变，而无需再次做计算的结果。
参数：density.digits
number of digits in the reported graph densities.
在图密度位数。
参数：logscale.clqsize
logical; if TRUE then the scale for the maximum clique size is logarithmic which is useful when working with more than 1000 variables; FALSE otherwise (default).
逻辑，规模最大团的大小如果为TRUE则是对数超过1000个变量时这是有用的，否则返回FALSE（默认）。
参数：titleclq
main title to be shown in the plot.
要显示主标题中的图。
参数：verbose
show progress on calculations.
显示在计算方面取得的进展。
Details
详情----------Details----------
The estimate of the complexity of the resulting qp-graphs is calculated as the area enclosed under the curve of maximum clique sizes.
QP-图的复杂性估计下的最大团大小的曲线围成的面积计算。
The maximum clique size, or clique number, is obtained by calling the function qpCliqueNumber The calculation of the clique number of an undirected graph is an NP-complete problem which means that its computational cost is bounded by an exponential running time (Pardalos and Xue, 1994). Therefore, giving breakpoints between 0.95 and 1.0 may result into very dense graphs which can lead to extremely long execution times. If it is necessary to look at that range of breakpoints it is recommended either to use the lower bound on the clique number (exact.calculation=FALSE) or to look at qpGraphDensity.
集团规模最大，或集团的数量，通过调用函数qpCliqueNumber一个无向图的团数的计算是一个NP完全问题，这意味着指数的运行时间为界，其计算成本（ pardalos和薛，1994）。因此，给予0.95和1.0之间的断点可能导致非常密集的图形，从而导致极长的执行时间。如果有必要来看看，断点建议使用集团（exact.calculation=FALSE），或在qpGraphDensity看下界。
值----------Value----------
A list with the maximum clique size and graph density as function of threshold, an estimate of the complexity of the resulting qp-graphs across the thresholds, the threshold on the non-rejection rate that provides a maximum clique size strictly smaller than the sample size N and the resulting maximum clique size.
造成QP图跨越的阈值，非废品率的阈值，提供了最大的集团规模，严格比样本规模较小的最大团的大小和功能的阈值，估计复杂的图形密度名单N和所产生的最大团的大小。
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.
J. Global Optim., 4:301-328, 1994.
参见----------See Also----------
qpCliqueNumber qpGraphDensity
qpCliqueNumberqpGraphDensity
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))
## the higher the q the less complex the qp-graph[＃Q值越高，那么复杂的QP图]
nrr.estimates <- qpNrr(X, q=1, verbose=FALSE)
qpClique(nrr.estimates, plot=FALSE)$complexity
nrr.estimates <- qpNrr(X, q=5, verbose=FALSE)
qpClique(nrr.estimates, plot=FALSE)$complexity
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。
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发表于 2012-2-26 11:38:57
