pstar(sna)
pstar()所属R语言包：sna
Fit a p*/ERG Model Using a Logistic Approximation
装一个P * / ERG模式的使用逻辑逼近
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Fits a p*/ERG model to the graph in dat containing the effects listed in effects. The result is returned as a glm object.
适合AP * / ERG模式的dat的影响中列出effects中的图形。返回的结果是作为一个glm对象。
用法----------Usage----------
pstar(dat, effects=c("choice", "mutuality", "density", "reciprocity",
"stransitivity", "wtransitivity", "stranstri", "wtranstri",
"outdegree", "indegree", "betweenness", "closeness",
"degcentralization", "betcentralization", "clocentralization",
"connectedness", "hierarchy", "lubness", "efficiency"),
attr=NULL, memb=NULL, diag=FALSE, mode="digraph")
参数----------Arguments----------
参数：dat
a single graph
一个单一的图形
参数：effects
a vector of strings indicating which effects should be fit.
一个向量的字符串，说明它的影响应该是合适的。
参数：attr
a matrix whose columns contain individual attributes (one row per vertex) whose differences should be used as supplemental predictors.
矩阵的列包含各个属性的每个顶点（一排），其差额应作为补充的预测。
参数：memb
a matrix whose columns contain group memberships whose categorical similarities (same group/not same group) should be used as supplemental predictors.
矩阵的列中包含的组成员身份明确的相似性（相同/不相同的组）应作为补充的预测。
参数：diag
a boolean indicating whether or not diagonal entries (loops) should be counted as meaningful data.
一个布尔值，指示是否对角元素（循环），应算作有意义的数据。
参数：mode
"digraph" if dat is directed, else "graph"
"digraph"如果dat的指示，否则"graph"
Details
详细信息----------Details----------
p* (also called the Exponential Random Graph (ERG) family) is an exponential family specification for network data. Under p*, it is assumed that
P *（也被称为指数型图（ERG）家庭）是一个指数系列规范网络数据。根据P *，则假定
p(G=g) propto exp(beta_0 gamma_0(g) + beta_1 gamma_1(g) + …)</i>
P（G = G）propto EXP（beta_0 gamma_0（G）+ beta_1 gamma_1（G）+ ...）</ P>
Using the effects argument, a range of different potential parameters can be estimated. The network measure associated with each is, in turn, the edge-perturbed difference in: <ol> choice: the number of edges in the graph (acts as a constant)
effects使用参数，不同的潜在参数可以估算的。网络与每个相关联的措施是，反过来，边缘扰动差异进行：<ol>choice：图中的边的数目（作为一个恒定的行为）
mutuality: the number of reciprocated dyads in the graph
mutuality：投桃报李二元关系图中的
density: the density of the graph
density：密度的曲线图
reciprocity: the edgewise reciprocity of the graph
reciprocity：沿边互惠的图形
stransitivity: the strong transitivity of the graph
stransitivity：强传递性的图形
wtransitivity: the weak transitivity of the graph
wtransitivity：弱传递性的图形
stranstri: the number of strongly transitive triads in the graph
stranstri：在图中的数字强传递的黑社会的
wtranstri: the number of weakly transitive triads in the graph
wtranstri：图中的弱传递的黑社会
outdegree: the outdegree of each actor (|V| parameters)
outdegree：每一个演员的出度（| V |参数）
indegree: the indegree of each actor (|V| parameters)
indegree：每个演员的入度（| V |参数）
betweenness: the betweenness of each actor (|V| parameters)
betweenness：“中间的每一个演员（| V |参数）
closeness: the closeness of each actor (|V| parameters)
closeness：每个演员的亲密程度（| V |参数）
degcentralization: the Freeman degree centralization of the graph
degcentralization：民丰度集中的图
betcentralization: the betweenness centralization of the graph
betcentralization：“中间集中的图形
clocentralization: the closeness centralization of the graph
clocentralization：亲密集中的图形
connectedness: the Krackhardt connectedness of the graph
connectedness：Krackhardt连通的图
hierarchy: the Krackhardt hierarchy of the graph
hierarchy：Krackhardt的曲线图的层次结构
efficiency: the Krackhardt efficiency of the graph
efficiency：Krackhardt效率的曲线图
lubness: the Krackhardt LUBness of the graph </ol> (Note that some of these do differ somewhat from the common p* parameter formulation, e.g. quantities such as density and reciprocity are computed as per the gden and grecip functions rather than via the unnormalized "choice" and "mutual" quantities one often finds in the p* literature.) Please do not attempt to use all effects simultaneously!!! In addition to the above, the user may specify a matrix of individual attributes whose absolute dyadic differences are to be used as predictors, as well as a matrix of individual memberships whose dyadic categorical similarities (same/different) are used in the same manner.
lubness：Krackhardt LUBness图表</ OL>（请注意，其中一些也有所不同，从常见的p参数制定，例如数量，如密度和互惠计算每gden grecip功能，而不是通过非标准化的“选择”和“互惠”的数量经常发现在p *文献。）请不要尝试同时使用所有的效果！！在除了上述情况中，用户可以指定的矩阵的单个属性的绝对值是被用作预测的二进差异，以及个人会员的矩阵的二进分类相似性（相同/不同的）中相同的方式使用。
Although the p* framework is quite versatile in its ability to accommodate a range of structural predictors, it should be noted that the substantial collinearity of many of the standard p* predictors can lead to very unstable model fits. Measurement and specification errors compound this problem; thus, it is somewhat risky to use p* in an exploratory capacity (i.e., when there is little prior knowledge to constrain choice of parameters). While raw instability due to multicollinearity should decline with graph size, improper specification will still result in biased coefficient estimates so long as an omitted predictor correlates with an included predictor. Caution is advised.
虽然在p *框架是相当具有通用性，在其能力，以适应的范围内的结构的预测因子，应当指出，许多标准的P *预测因子的主要共线性可以导致非常不稳定模型配合。测量和规范错误复合这个问题，因此，它是有点冒险使用p *的探索能力（即，当有一点先验知识来约束的参数的选择）。由于多重共线性，而原材料不稳定应拒绝与图形大小，不当的规范仍然会导致偏差系数估计这样的，只要省略预测与包括预测。注意建议。
值----------Value----------
A glm object
Aglm对象
警告----------WARNING ----------
Estimation of p* models by maximum pseudo-likelihood is now known to be a dangerous practice. Use at your own risk.
最大的伪似然估计的p *模型是目前已知的是一种危险的做法。使用您自己的风险。
注意----------Note----------
In the long run, support will be included for p* models involving arbitrary functions (much like the system used with cugtest and qaptest).
从长远来看，将支持包括为p *模型的任意函数（很像系统使用cugtest和qaptest）。
（作者）----------Author(s)----------
Carter T. Butts <a href="mailto:buttsc@uci.edu">buttsc@uci.edu</a>
参考文献----------References----------
Holland, P.W., and Leinhardt, S. (1981). “An Exponential Family of Probability Distributions for Directed Graphs.” Journal of the American statistical Association, 81, 51-67.
Wasserman, S., and Pattison, P. (1996). “Logit Models and Logistic Regressions for Social Networks: I. An introduction to Markov Graphs and p*.” Psychometrika, 60, 401-426.
参见----------See Also----------
eval.edgeperturbation
eval.edgeperturbation
实例----------Examples----------
#Create a graph with expansiveness and popularity effects[创建膨胀性和普及效果图]
in.str<-rnorm(20,0,3)
out.str<-rnorm(20,0,3)
tie.str<-outer(out.str,in.str,"+")
tie.p<-apply(tie.str,c(1,2),function(a){1/(1+exp(-a))})
g<-rgraph(20,tprob=tie.p)
#Fit a model with expansiveness only[拟合模型与膨胀性]
p1<-pstar(g,effects="outdegree")
#Fit a model with expansiveness and popularity[拟合模型的膨胀性和普及]
p2<-pstar(g,effects=c("outdegree","indegree"))
#Fit a model with expansiveness, popularity, and mutuality[拟合模型与拓展性，普及和相互关系，]
p3<-pstar(g,effects=c("outdegree","indegree","mutuality"))
#Compare the model AICs -- use ONLY as heuristics!!![比较模型的工商行政管理部门 - 只能使用启发式算法！]
extractAIC(p1)
extractAIC(p2)
extractAIC(p3)
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 2012-9-30 11:00:21
