qaptest(sna)
qaptest()所属R语言包：sna
Perform Quadratic Assignment Procedure (QAP) Hypothesis Tests for Graph-Level Statistics
进行二次分配程序（QAP）假设检验走势级统计信息
译者：生物统计家园网 机器人LoveR
描述----------Description----------
qaptest tests an arbitrary graph-level statistic (computed on dat by FUN) against a QAP null hypothesis, via Monte Carlo simulation of likelihood quantiles. Note that fair amount of flexibility is possible regarding QAP tests on functions of such statistics (see an equivalent discussion with respect to CUG null hypothesis tests in Anderson et al. (1999)). See below for more details.
qaptest测试一个任意图统计（计算datFUN）针对QAP零假设，通过蒙特卡罗模拟的可能性位数。需要注意的是相当数量的灵活性QAP测试这样的统计功能的（看到一个等价与CUG空Anderson等人（1999））的假设检验的讨论。请参阅下面的更多细节。
用法----------Usage----------
qaptest(dat, FUN, reps=1000, ...)
参数----------Arguments----------
参数：dat
graphs to be analyzed. Though one could in principle use a single graph, this is rarely if ever sensible in a QAP-test context.
图表进行分析。虽然原则上可以用一个单一的图形，这是明智的很少，如果在QAP测试中。
参数：FUN
function to generate the test statistic. FUN must accept dat and the specified g arguments, and should return a real number.
函数来生成的检验统计量。 FUN必须接受dat和指定的g参数，并应该返回一个实数。
参数：reps
integer indicating the number of draws to use for quantile estimation. Note that, as for all Monte Carlo procedures, convergence is slower for more extreme quantiles. By default, reps=1000.
整数表示即将使用的分位数估计。需要注意的是，所有蒙特卡罗程序，收敛速度较慢更多极端位数的。默认情况下，reps= 1000。
参数：...
additional arguments to FUN.
附加参数到FUN。
Details
详细信息----------Details----------
The null hypothesis of the QAP test is that the observed graph-level statistic on graphs G_1,G_2,… was drawn from the distribution of said statistic evaluated (uniformly) on the set of all relabelings of G_1,G_2,…. Pragmatically, this test is performed by repeatedly (randomly) relabeling the input graphs, recalculating the test statistic, and then evaluating the fraction of draws greater than or equal to (and less than or equal to) the observed value. This accumulated fraction approximates the integral of the distribution of the test statistic over the set of unlabeled input graphs.
零假设的QAP试验是图统计图上观察到的G_1,G_2,…从分布的统计评估（均匀）上设置G_1,G_2,…的所有relabelings的。务实，此试验是通过重复地（随机地）重新标记的输入的曲线图，重新计算检验统计量，然后评价分数绘制（小于或等于）所观察到的值大于或等于。该累积的分数超过设定的未标记的输入图的检验统计量的分布近似的积分。
The qaptest procedure returns a qaptest object containing the estimated likelihood (distribution of the test statistic under the null hypothesis), the observed value of the test statistic on the input data, and the one-tailed p-values (estimated quantiles) associated with said observation. As usual, the (upper tail) null hypothesis is rejected for significance level alpha if p>=observation is less than alpha (or p<=observation, for the lower tail); if the hypothesis is undirected, then one rejects if either p<=observation or p>=observation is less then alpha/2. Standard caveats regarding the use of null hypothesis testing procedures are relevant here: in particular, bear in mind that a significant result does not necessarily imply that the likelihood ratio of the null model and the alternative hypothesis favors the latter.
qaptest过程返回一个qaptest对象的可能性的估计（的零假设下检验统计量的分布），对输入数据的检验统计量的观测值，和单尾P-值（估计分位数）与所述观察。像往常一样，的（上限尾）空假设被拒绝的显着性水平α-如果p> =观察是小于阿尔法（或p <=观察，下尾），如果假设是无向图，然后1拒绝的话要么p <观察或P> =观察不到α/ 2。关于使用的空假设检验程序的相关标准注意事项：特别是，要记住，一个显着的结果并不一定意味着空模型的似然比和备择假设有利于后者。
In interpreting a QAP test, it is important to bear in mind the nature of the QAP null hypothesis. The QAP test should not be interpreted as evaluating underlying structural differences; indeed, QAP is more accurately understood as testing differences induced by a particular vertex labeling controlling for underlying structure. Where there is substantial automorphism in the underling structures, QAP will tend to given non-significant results. (In fact, it is impossible to obtain a one-tailed significance level in excess of max_[g in {G,H}] |Aut(g)|/|Perm(g)| when using a QAP test on a bivariate graph statistic f(G,H), where Aut(g) and Perm(g) are the automorphism and permutation groups on g, respectively. This follows from the fact that all members of Aut(g) will induce the same values of f().) By turns, significance under QAP does not necessarily imply that the observed structural relationship is unusual relative to what one would expect from typical structures with (for instance) the sizes and densities of the graphs in question. In contexts in which one's research question implies a particular labeling of vertices (e.g., "within this group of individuals, do friends also tend to give advice to one another"), QAP can be a very useful way of ruling out spurious structural influences (e.g., some respondents tend to indiscriminately nominate many people (without regard to whom), resulting in a structural similarity which has nothing to do with the identities of those involved). Where one's question does not imply a labeled relationship (e.g., is the shape of this group's friendship network similar to that of its advice network), the QAP null hypothesis is inappropriate.
在解释QAP测试，重要的是要牢记的的QAP空假设的性质。 QAP的测试不应该被解释为评估相关的结构上的差异，事实上，QAP是更准确GEO解为测试差异控制底层结构由一个特定的顶点标签诱导。下属结构中有很大的同构，QAP会倾向于给定的非显着的效果。 （事实上，这是不可能的，以获得单尾显着性水平超过max_[g in {G,H}] |Aut(g)|/|Perm(g)|QAP测试时使用统计f(G,H)，，AUT（g）和权限（G）是一个二元图克，分别同构和置换群。在此之前，从QAP的事实，AUT（G）的所有成员轮流，会引起相同的价值观f()。）意义下并不一定意味着所观察到的结构是不寻常的关系相对于什么人会期望从典型结构（例如）中的图形的大小和密度。在上下文中，一个人的研究问题意味着一个特定的标签的顶点（例如，“在这组个人的朋友也往往到给意见，一个又一个”），QAP可以是一个非常有用的方法，排除杂散结构的影响（例如，一些受访者往往不分青红皂白地提名人（不考虑谁），导致相似的结构，其中有无关的身份参与）。当一个人的问题，并不意味着标记的关系（例如，是这个群体的友谊网络的形状类似于其意见网络），QAP空的假设是不恰当的。
值----------Value----------
An object of class qaptest, containing
类qaptest，包含的对象
<table summary="R valueblock"> <tr valign="top"><td>testval</td> <td> The observed value of the test statistic. </td></tr> <tr valign="top"><td>dist</td> <td> A vector containing the Monte Carlo draws. </td></tr> <tr valign="top"><td>pgreq</td> <td> The proportion of draws which were greater than or equal to the observed value. </td></tr> <tr valign="top"><td>pleeq</td> <td> The proportion of draws which were less than or equal to the observed value. </td></tr> </table>
<table summary="R valueblock"> <tr valign="top"> <TD>testval</ TD> <TD>检验统计量的观测值。 </ TD> </ TR> <tr valign="top"> <TD> dist</ TD> <td>一个矢量蒙地卡罗平。 </ TD> </ TR> <tr valign="top"> <TD> pgreq</ TD> <TD>的比例绘制的观测值大于或等于。 </ TD> </ TR> <tr valign="top"> <TD> pleeq</ TD> <TD>的比例绘制，小于或等于观察到的值。 </ TD> </ TR> </ TABLE>
（作者）----------Author(s)----------
Carter T. Butts <a href="mailto:buttsc@uci.edu">buttsc@uci.edu</a>
参考文献----------References----------
Anderson, B.S.; Butts, C.T.; and Carley, K.M. (1999). “The Interaction of Size and Density with Graph-Level Indices.” Social Networks, 21(3), 239-267.
Hubert, L.J., and Arabie, P. (1989). “Combinatorial Data Analysis: Confirmatory Comparisons Between Sets of Matrices.” Applied Stochastic Models and Data Analysis, 5, 273-325.
Krackhardt, D. (1987). “QAP Partialling as a Test of Spuriousness.” Social Networks, 9 171-186.
Krackhardt, D. (1988). &ldquo redicting With Networks: Nonparametric Multiple Regression Analyses of Dyadic Data.” Social Networks, 10, 359-382.
参见----------See Also----------
cugtest
cugtest
实例----------Examples----------
#Generate three graphs[生成三幅图]
g<-array(dim=c(3,10,10))
g[1,,]<-rgraph(10)
g[2,,]<-rgraph(10,tprob=g[1,,]*0.8)
g[3,,]<-1; g[3,1,2]<-0 #This is nearly a clique[这是近一个集团]
#Perform qap tests of graph correlation[执行QAP的图形相关的测试]
q.12<-qaptest(g,gcor,g1=1,g2=2)
q.13<-qaptest(g,gcor,g1=1,g2=3)
#Examine the results[检查结果]
summary(q.12)
plot(q.12)
summary(q.13)
plot(q.13)
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注：
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发表于 2012-9-30 11:00:30
