space.neighbor(space)
space.neighbor()所属R语言包：space
A function to estimate partial correlations using the neighborhood selection approach
使用的邻域选择方法的偏相关函数估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
A function to estimate partial correlations using the neighborhood selection approach
使用的邻域选择方法的偏相关函数估计
用法----------Usage----------
space.neighbor(Y.m, lam1, lam2=0)
参数----------Arguments----------
参数：Y.m
numeric matrix. Each column is for one variable and each row is for one sample. Missing values are not allowed. It's recommended to first standardize each column to have mean 0 and norm 1.
数字矩阵。每一列是一个变量，每行是一个样本。遗漏值是不允许的。建议先规范每一列，均值为0，标准1。
参数：lam1
numeric value. This is the l_1 norm penalty parameeter. If the columns of Y.m have norm one, then the suggested range of lam1 is: O(n^{1/2}\Phi^{-1}(1-\alpha/(2p^2))) for small \alpha such as 0.1.
数值。这是l_1规范处罚parameeter。如果Ym的列范数1，然后是所建议的范围内lam1：O(n^{1/2}\Phi^{-1}(1-\alpha/(2p^2)))小\alpha（如0.1）。
参数：lam2
numeric value. If not specified, lasso regression is used in the neighborhood selection. Otherwise, elastic net regression is used and <VAR>lam2</VAR> serves as the l_2 norm penalty parameter.
数值。如果没有指定，套索回归是在周边的选择使用。否则，弹力网回归<VAR> lam2 </ VAR>作为l_2规范刑罚参数。
Details
详细信息----------Details----------
值----------Value----------
A list with two components <table summary="R valueblock"> <tr valign="top"><td>ParCor</td> <td> the estimated partial correlation matrix.</td></tr> <tr valign="top"><td>sig.fit</td> <td> numeric vector of the estimated \sigma^{ii}</td></tr> </table>
两部分组成的列表<table summary="R valueblock"> <tr valign="top"> <TD> ParCor</ TD> <TD>部分相关矩阵的估计。</ TD> </ TR > <tr valign="top"> <TD> sig.fit </ TD> <TD>数字向量估计\sigma^{ii} </ TD> </ TR> </表>
（作者）----------Author(s)----------
J. Peng, P. Wang, N. Zhou, J. Zhu
参考文献----------References----------
J. Peng, P. Wang, N. Zhou, J. Zhu (2007). Partial Correlation Estimation by Joint Sparse Regression Model.
Meinshausen, N., and Buhlmann, P. (2006), High Dimensional Graphs and Variable Selection with the Lasso, Annals of Statistics, 34, 1436-1462.
实例----------Examples----------
#############################################################################################[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃ ＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃]
############################ (A) The simulated Hub.net example in Peng et. al. (2007).[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃（A）的模拟Hub.net Peng等例如在。人。 （2007年）。]
#############################################################################################[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃ ＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃]
data(spaceSimu)
n=nrow(spaceSimu$Y.data)
p=ncol(spaceSimu$Y.data)
true.adj=abs(spaceSimu$ParCor.true)>1e-6
#################### view the network corresponding to the parcial correlation matrix in the simulation example[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃查看网络对应的parcial相关矩阵中的仿真例子]
#################### view the network corresponding to the parcial correlation matrix in the simulation example[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃查看网络对应的parcial相关矩阵中的仿真例子]
########### the following code can run only if the "igraph" is installed in the system.[＃＃＃＃＃＃＃＃＃＃下面的代码可以仅当“的igraph”被安装在系统中运行。]
#library(igraph)[库（IGRAPH）]
#plot.adj=true.adj[plot.adj = true.adj]
#diag(plot.adj)=0[诊断（plot.adj）= 0]
#temp=graph.adjacency(adjmatrix=plot.adj, mode="undirected")[温度= graph.adjacency（adjmatrix = plot.adj，模式=“无方向”）]
#temp.degree=apply(plot.adj, 2, sum)[temp.degree应用（plot.adj，2，总和）]
#V(temp)$color=(temp.degree>9)+3[V（临时）颜色=（temp.degree 9）+3]
#plot(temp, vertex.size=3, vertex.frame.color="white",layout=layout.fruchterman.reingold, vertex.label=NA, edge.color=grey(0.5))[图（温度，vertex.size 3，vertex.frame.color =“白”，布局= layout.fruchterman.reingold，vertex.label = NA，edge.color =灰色（0.5））]
#################### estimate the parcial correlation matrix with various methods[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃估计的parcial的相关矩阵的各种方法]
alpha=1
l1=1/sqrt(n)*qnorm(1-alpha/(2*p^2))
iter=3
########### the values of lam1 were selected to make the results of different methods comparable. [＃＃＃＃＃＃＃＃＃＃的值lam1被选择到的结果不同的方法可比。]
#### 1. MB method[＃＃＃1。 MB方法]
result1=space.neighbor(spaceSimu$Y.data, lam1=l1*0.7, lam2=0)
fit.adj=abs(result1$ParCor)>1e-6
sum(fit.adj==1)/2 ##total number of edges detected [＃总数检测到的边缘]
sum(fit.adj[true.adj==1]==1)/2 ##total number of true edges detected [＃总数真正的边缘检测]
#### 2. Joint method with no weight[＃＃＃2。没有重量的联合方法]
result2=space.joint(spaceSimu$Y.data, lam1=l1*n*1.56, lam2=0, iter=iter)
fit.adj=abs(result2$ParCor)>1e-6
sum(fit.adj==1)/2 ##total number of edges detected [＃总数检测到的边缘]
sum(fit.adj[true.adj==1]==1)/2 ##total number of true edges detected [＃总数真正的边缘检测]
#### 3. Joint method with residue variance based weights[＃＃＃3。联合残留方差方法与基于权重]
result3=space.joint(spaceSimu$Y.data, lam1=l1*n*1.86, lam2=0, weight=1, iter=iter)
fit.adj=abs(result3$ParCor)>1e-6
sum(fit.adj==1)/2 ##total number of edges detected [＃总数检测到的边缘]
sum(fit.adj[true.adj==1]==1)/2 ##total number of true edges detected [＃总数真正的边缘检测]
#### 4. Joint method with degree based weights[＃＃＃4。联合程度的权重的方法]
result4=space.joint(spaceSimu$Y.data, lam1=l1*n*1.61, lam2=0, weight=2, iter=iter)
fit.adj=abs(result4$ParCor)>1e-6
sum(fit.adj==1)/2 ##total number of edges detected [＃总数检测到的边缘]
sum(fit.adj[true.adj==1]==1)/2 ##total number of true edges detected [＃总数真正的边缘检测]
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。
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发表于 2012-9-30 12:14:20
