nem.calcSignificance(nem)
nem.calcSignificance()所属R语言包：nem
Statistical significance of network hypotheses
网络假说的统计意义
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Assess statistical significance of a network hypothesis by comparing it to a null hypothesis.
比较空的假设，评估网络假说的统计意义。
用法----------Usage----------
nem.calcSignificance(D, x, N=1000, seed=1, mc.cores=8)
参数----------Arguments----------
参数：D
data matrix with experiments in the columns (binary or continious)
矩阵中列的实验数据（二进制或305.11）
参数：x
nem object
NEM对象
参数：N
number of random networks to sample
随机网络的采样数
参数：seed
random seed
随机种子
参数：mc.cores
number of cores to be used on a multicore processor
要使用多核处理器的核心数量
Details
详情----------Details----------
Given data, N random network hypotheses from a null distribution are drawn as follows: For each S-gene S_{k} we randomly choose a number o of outgoing edges between 0 and 3. We then select o S-genes having at most 1 ingoing edge, connected S_{k} to them and transitively closed the graph. For all random network hypotheses it is counted, how often their likelihood is bigger than that of the given network. This yields an exact p-value.
鉴于数据，氮空分布的随机网络的假设得出如下：对于每个S-基因S_{k}我们随机选择一个介于0和3出边数Ø。然后选择O S-基因具有最多1迁入边缘，连接S_{k}给他们，并传递封闭的图形。对于所有随机网络的假设计算，往往他们的可能性大于给定的网络。这将产生一个确切的p值。
Another way of assessing the statistical significance of the network hypothesis is to draw random permutations of the node labels. Note that in this case the node degree distribution is the same as in the given network. Again, we can obtain an exact p-value by counting, how often the likelihood of the permuted network is bigger than that of the given network.
评估网络假说的统计意义的另一种方法是绘制节点的标签随机排列。请注意，在这种情况下，节点的度分布是在给定的网络相同。再次，我们可以通过计算获得一个确切的p值，如何经常置换网络的可能性大于给定的网络。
Finally, comparison to randomly perturbed networks (insertion or deletion of 1 edge) yields an exact p-value describing the stability of the network.
最后，比较随机扰动网络（1边沿插入或删除），产生一个确切的p值，描述了网络的稳定性。
值----------Value----------
参数：p.value.rnd
p-value of the network according to the null hypothesis that it is random
根据网络的零假设，即它是随机P-值
参数：p.value.perm
p-value of the network according to the null hypothesis that a network with permuted node labels is at least as good
p值的网络，根据网络与置换节点标签的零假设是至少一样好
参数：p.value.mod
p-value of the network according to the null hypothesis a randomly peturbed network is at least as good
根据零假设p值的网络随机peturbed网络至少为好
作者（S）----------Author(s)----------
Holger Froehlich
参见----------See Also----------
nem.consensus, nem.jackknife, nem.bootstrap, nem
nem.consensus，nem.jackknife，nem.bootstrap，nem
举例----------Examples----------
## Not run: [＃无法运行：]
data("BoutrosRNAi2002")
D <- BoutrosRNAiDiscrete[,9:16]
res = nem(D, control=set.default.parameters(unique(colnames(D)), para=c(0.13,0.05))) # get best network[获得最佳的网络]
nem.calcSignificance(D,res) # assess its statistical significance[评估其统计意义]
## 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。
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发表于 2012-2-26 07:46:20
