sedist {blockmodeling} | R Documentation |
The functions computed the distances in terms of Structural equivalence (Lorrain and White, 1971) between the units of a one-mode network. Several options for treating the diagonal values are supported.
sedist(M, method = "default", fun = "default", fun.on.rows = "default", handle.interaction = "switch", use = "pairwise.complete.obs", ...)
M |
A matrix representing the (usually valued) network. For now, only one-relational networks are supported. The network must be one-mode. |
method |
The method used to compute distances - any of the methods alloed by functions dist, cor or cov {all package::stats} or just "cor" or "cov" (given as character). |
fun |
Which function should be used to comput distacnes (given as character), . |
fun.on.rows |
For non-standard function - does the function compute measure on rows (such as cor , cov ,...) of the data matrix (as opposed to computing measure on columns (such as dist ). |
handle.interaction |
How should the interaction between the vertices analysed be handled: "switch" (the default) - assumes that when comparing units i and j, M[i,i] should be compared with M[j,j] and M[i,j] with M[j,i] "ignore" (diagonal) - Diagonal is ignored "none" - the matrix is used "as is" |
use |
For use with methods "cor" and "cov", for other methods (the default option should be used if handle.interaction=="ignore"), "pairwise.complete.obs" are always used, if stats.dist.cor.cov=TRUE |
... |
Additional arguments to fun |
If both method
and fun
are "default", the euclidian distances are computed. the "default" method for fun="dist"
is "eucludian" and for fun="cor"
"pearson".
A matrix (usually of class dist) is returned.
Aleš Žiberna
Batagelj, V., Ferligoj, A., Doreian, P. (1992): Direct and indirect methods for structural equivalence. Social Networks 14, 63-90.
Lorrain, F., White, H.C., 1971. Structural equivalence of individuals in social networks. Journal of Mathematical Sociology 1, 49-80.
dist
, hclust
, REGE
, crit.fun
, opt.par
, opt.random.par
#generating a simple network corresponding to the simple Sum of squares #structural equivalence with blockmodel: # null com # null null n<-20 net<-matrix(NA,ncol=n,nrow=n) clu<-rep(1:2,times=c(5,15)) tclu<-table(clu) net[clu==1,clu==1]<-rnorm(n=tclu[1]*tclu[1],mean=0,sd=1) net[clu==1,clu==2]<-rnorm(n=tclu[1]*tclu[2],mean=4,sd=1) net[clu==2,clu==1]<-rnorm(n=tclu[2]*tclu[1],mean=0,sd=1) net[clu==2,clu==2]<-rnorm(n=tclu[2]*tclu[2],mean=0,sd=1) D<-sedist(M=net) plot.mat(net, clu=cutree(hclust(d=D,method="ward"),k=2))